Top 10 real-world data science case studies.
Data science has become integral to modern businesses and organizations, driving decision-making, optimizing operations, and improving customer experiences. From predicting machine failures in manufacturing to personalizing healthcare treatments, data science is profoundly transforming industries.
Data science, often called the "most desirable job of the 21st century," is a multidisciplinary field that combines data analysis, machine learning, and domain knowledge to extract meaningful insights from data. It has far-reaching applications in diverse industries, revolutionizing how we solve problems and make decisions.
In this blog, we will delve into the top 10 real-world data science case studies that showcase the power and versatility of data-driven insights across various sectors.
Let’s dig in!
Table of Contents
- 1. Case study 1: Predictive maintenance in manufacturing
- 1.2. 2. Siemens
- 2. Case study 2: Healthcare diagnostics and treatment personalization
- 2.1. 1. IBM Watson Health
- 2.2. 2. PathAI
- 3. Case study 3: Fraud detection and prevention in finance
- 3.1. 1. PayPal
- 3.2. 2. Capital One
- 4. Case study 4: Urban planning and smart cities
- 4.1. 1. Singapore
- 4.2. 2. Barcelona
- 5. Case study 5: E-commerce personalization and recommendation systems
- 5.1. 1. Amazon
- 5.2. 2. eBay
- 6. Case study 6: Agricultural yield prediction
- 6.1. 1. John Deere
- 6.2. 2. Caterpillar Inc.
- 7. Case study 7: Energy consumption optimization
- 7.1. 1. EnergyOptiUS
- 7.2. 2. CarbonSmart USA
- 8. Case study 8: Transportation and route optimization
- 8.1. 1. Uber
- 8.2. 2. Lyft
- 9. Case study 9: Natural language processing in customer service
- 9.1. 1. Zendesk
- 10. Case study 10: Environmental conservation and data analysis
- 10.1. 1. NASA
- 10.2. 2. WWF
- 11. Conclusion
Case study 1: Predictive maintenance in manufacturing
General Electric (GE), a global industrial conglomerate, leverages data science to implement predictive maintenance solutions. By analyzing sensor data from their industrial equipment, such as jet engines and wind turbines, GE can predict the need for maintenance before a breakdown occurs. This proactive approach minimized downtime and reduced maintenance costs.
Here’s how data science played a pivotal role in enhancing GE's manufacturing operations through predictive maintenance:
- In their aviation division, GE has reported up to a 30% reduction in unscheduled maintenance by utilizing predictive analytics on sensor data from jet engines.
- In the renewable energy sector, GE's wind turbines have seen a 15% increase in operational efficiency due to data-driven maintenance practices.
- Over the past year, GE saved $50 million in maintenance costs across various divisions thanks to predictive maintenance models.
Siemens, another industrial giant, embraces predictive maintenance through data science. They use machine learning algorithms to monitor and analyze data from their manufacturing machines. This approach allows Siemens to identify wear and tear patterns and schedule maintenance precisely when required.
As a result, Siemens achieved substantial cost savings and increased operational efficiency through:
- Siemens has reported a remarkable 20% reduction in unplanned downtime across its manufacturing facilities globally since implementing predictive maintenance solutions powered by data science.
- Through data-driven maintenance, Siemens has achieved a 15% increase in overall equipment effectiveness (OEE), resulting in improved production efficiency and reduced production costs.
- In a recent case study, Siemens documented a $25 million annual cost savings in maintenance expenditures, directly attributed to their data science-based predictive maintenance approach.
Case study 2: Healthcare diagnostics and treatment personalization
1. ibm watson health.
IBM Watson Health employs data science to enhance healthcare by providing personalized diagnostic and treatment recommendations. Watson's natural language processing capabilities enable it to sift through vast medical literature and patient records to assist doctors in making more informed decisions.
Data science has significantly aided IBM Watson Health in healthcare diagnostics and personalized treatment in:
- IBM Watson Health has demonstrated a 15% increase in the accuracy of cancer diagnoses when assisting oncologists in analyzing complex medical data, including genomic information and medical journals.
- In a recent clinical trial, IBM Watson Health's AI-powered recommendations helped reduce the average time it takes to develop a personalized cancer treatment plan from weeks to just a few days, potentially improving patient outcomes and survival rates.
- Watson's data-driven insights have contributed to a 30% reduction in medication errors in some healthcare facilities by flagging potential drug interactions and allergies in patient records.
- IBM Watson Health has processed over 200 million pages of medical literature to date, providing doctors with access to a vast knowledge base that can inform their diagnostic and treatment decisions.
PathAI utilizes machine learning algorithms to assist pathologists in diagnosing diseases more accurately. By analyzing digitized pathology images, PathAI's system can identify patterns and anomalies that the human eye might miss. This analysis speeds up the diagnostic process and enhances the precision of pathology reports by 6-9%, leading to better patient care.
Data science has been instrumental in PathAI's advancements in:
- PathAI's AI-driven pathology platform has shown a 25% improvement in diagnostic accuracy compared to traditional manual evaluations when identifying challenging cases like cancer subtypes or rare diseases.
- In a recent study involving over 10,000 pathology reports, PathAI's system helped pathologists reduce the time it takes to analyze and report findings by 50%, enabling quicker treatment decisions for patients.
- By leveraging machine learning, PathAI has been able to significantly decrease the rate of false negatives and false positives in pathology reports, resulting in a 20% reduction in misdiagnoses.
- PathAI's platform has processed millions of pathology images, making it a valuable resource for pathologists to access a vast repository of data to aid in their diagnostic decisions.
Case study 3: Fraud detection and prevention in finance
PayPal, a leader in online payments, employs advanced data science techniques to detect and prevent fraudulent transactions in real-time. They analyze transaction data, user behavior, and other relevant factors to identify suspicious activity.
Here's how data science has helped PayPal in this regard:
- PayPal's real-time fraud detection system reported an impressive 99.9% accuracy rate in identifying and blocking fraudulent transactions, minimizing financial losses for both the company and its users.
- In a recent report, PayPal reported that their proactive fraud prevention measures saved users an estimated $2 billion in potential losses due to unauthorized transactions in a single year.
- The average time it takes for PayPal's data science algorithms to detect and respond to a fraudulent transaction is just milliseconds, ensuring that fraudulent activities are halted before they can cause harm.
- PayPal's continuous monitoring and data-driven approach to fraud prevention have resulted in a 40% reduction in the overall fraud rate across their platform over the past three years.
2. Capital One
Capital One, a major player in the banking industry, relies on data science to combat credit card fraud. Their machine-learning models assess transaction patterns and historical data to flag potentially fraudulent activities. This assessment safeguards their customers and enhances their trust in the bank's services.
Here's how data science has helped Capital One in this regard:
- Capital One's data-driven fraud detection system has achieved an industry-leading fraud detection rate of 97%, meaning that it successfully identifies and prevents fraudulent transactions with a high level of accuracy.
- In the past year, Capital One has reported a $50 million reduction in fraud-related losses, thanks to their machine-learning models, which continuously evolve to adapt to new fraud tactics.
- The bank's real-time fraud detection capabilities allow them to stop fraudulent transactions in progress, with an average response time of less than 1 second, minimizing potential financial losses for both the bank and its customers.
- Customer surveys have shown that 94% of Capital One customers feel more secure about their financial transactions due to the bank's proactive fraud prevention measures, thereby enhancing customer trust and satisfaction.
Case study 4: Urban planning and smart cities
Singapore is pioneering the smart city concept, using data science to optimize urban planning and public services. They gather data from various sources, including sensors and citizen feedback, to manage traffic flow, reduce energy consumption, and improve the overall quality of life in the city-state.
Here’s how data science helped Singapore in efficient urban planning:
- Singapore's real-time traffic management system, powered by data analytics, has led to a 25% reduction in peak-hour traffic congestion, resulting in shorter commute times and lower fuel consumption.
- Through its data-driven initiatives, Singapore has achieved a 15% reduction in energy consumption across public buildings and street lighting, contributing to significant environmental sustainability gains.
- Citizen feedback platforms have seen 90% of reported issues resolved within 48 hours, reflecting the city's responsiveness in addressing urban challenges through data-driven decision-making.
- The implementation of predictive maintenance using data science has resulted in a 30% decrease in the downtime of critical public infrastructure, ensuring smoother operations and minimizing disruptions for residents.
Barcelona has embraced data science to transform into a smart city as well. They use data analytics to monitor and control waste management, parking, and public transportation services. By doing so, Barcelona improves the daily lives of its citizens and makes the city more attractive for tourists and businesses.
Data science has significantly influenced Barcelona's urban planning and the development of smart cities, reshaping the urban landscape of this vibrant Spanish metropolis by:
- Barcelona's data-driven waste management system has led to a 20% reduction in the frequency of waste collection in certain areas, resulting in cost savings and reduced environmental impact.
- The implementation of smart parking solutions using data science has reduced the average time it takes to find a parking spot by 30%, easing congestion and frustration for both residents and visitors.
- Public transportation optimization through data analytics has improved service reliability, resulting in a 10% increase in daily ridership and reduced waiting times for commuters.
- Barcelona's efforts to become a smart city have attracted 30% more tech startups and foreign investments over the past five years, stimulating economic growth and job creation in the region.
Case study 5: E-commerce personalization and recommendation systems
Amazon, the e-commerce giant, heavily relies on data science to personalize the shopping experience for its customers. They use algorithms to analyze customers' browsing and purchasing history, making product recommendations tailored to individual preferences. This approach has contributed significantly to Amazon's success and customer satisfaction by reducing customer service response times by 40%.
Additionally, Amazon leverages data science for:
- Amazon's data-driven product recommendations have led to a 29% increase in average order value as customers are more likely to add recommended items to their carts.
- A study found that Amazon's personalized shopping experience has resulted in a 68% improvement in click-through rates on recommended products compared to non-personalized suggestions.
- Customer service response times have been reduced by 40% due to fewer inquiries related to product recommendations, as customers find what they need more easily.
- Amazon's personalized email campaigns, driven by data science, have shown an 18% higher open rate and a 22% higher conversion rate compared to generic email promotions.
eBay also harnesses the power of data science to enhance user experiences. Their recommendation systems suggest relevant products and optimize search results, increasing user engagement and sales. This data-driven approach has helped eBay remain competitive in the ever-evolving e-commerce landscape.
Data science also helped eBay in:
- eBay's recommendation algorithms have contributed to a 12% increase in average order value as customers are more likely to discover and purchase complementary products.
- The optimization of search results using data science has led to a 20% reduction in bounce rates on the platform, indicating that users are finding what they're looking for more effectively.
- eBay's personalized marketing campaigns, driven by data analysis, have achieved an 18% higher conversion rate compared to generic promotions, leading to increased sales and revenue.
- Over the past year, eBay's revenue has grown by 10%, outperforming many competitors, thanks in part to their data-driven enhancements to the user experience.
Case study 6: Agricultural yield prediction
1. john deere.
John Deere, a leader in agricultural machinery, implements data science to predict crop yields. By analyzing data from sensors on their farming equipment, weather data, and soil conditions, they provide farmers with valuable insights for optimizing planting and harvesting schedules. These insights enable farmers to increase crop yields while conserving resources.
Here’s how John Deere leverages data science:
- Farmers using John Deere's data science-based crop prediction system have reported an average 15% increase in crop yields compared to traditional farming methods.
- By optimizing planting and harvesting schedules based on data insights, farmers have achieved a 20% reduction in water usage, contributing to sustainable agriculture and resource conservation.
- John Deere's predictive analytics have reduced the need for chemical fertilizers and pesticides by 25%, resulting in cost savings for farmers and reduced environmental impact.
- Over the past five years, John Deere's data-driven solutions have helped farmers increase their overall profitability by $1.5 billion through improved crop yields and resource management.
2. Caterpillar Inc.
Caterpillar Inc., a construction and mining equipment manufacturer, applies data science to support the agriculture industry. They use machine learning algorithms to analyze data from heavy machinery in the field, helping farmers identify maintenance needs and prevent costly breakdowns during critical seasons.
Here’s how Caterpillar leverages data science:
- Farmers who utilize Caterpillar's data science-based maintenance system have experienced a 30% reduction in unexpected equipment downtime, ensuring that critical operations can proceed smoothly during peak farming seasons.
- Caterpillar's predictive maintenance solutions have resulted in a 15% decrease in overall maintenance costs, as equipment issues are addressed proactively, reducing the need for emergency repairs.
- By optimizing machinery maintenance schedules, farmers have achieved a 10% increase in operational efficiency, enabling them to complete tasks more quickly and effectively.
- Caterpillar's data-driven approach has contributed to a 20% improvement in the resale value of heavy machinery, as well-maintained equipment retains its value over time.
Case study 7: Energy consumption optimization
EnergyOptiUS specializes in optimizing energy consumption in commercial buildings. They leverage data science to monitor and control heating, cooling, and lighting systems in real-time. Analyzing historical data and weather forecasts ensures energy efficiency while maintaining occupant comfort. Additionally, they leverage data science for:
- Buildings equipped with EnergyOptiUS's energy optimization solutions have achieved an average 20% reduction in energy consumption, leading to substantial cost savings for businesses and a reduced carbon footprint.
- Real-time monitoring and control of energy systems have resulted in a 15% decrease in maintenance costs, as equipment operates more efficiently and experiences less wear and tear.
- EnergyOptiUS's data-driven approach has led to a 25% improvement in occupant comfort, as temperature and lighting conditions are continuously adjusted to meet individual preferences.
- Over the past year, businesses using EnergyOptiUS's solutions have collectively saved $50 million in energy expenses, enhancing their overall financial performance and sustainability efforts.
2. CarbonSmart USA
CarbonSmart USA uses data science to assist businesses in reducing their carbon footprint. They provide actionable insights and recommendations based on data analysis, enabling companies to adopt more sustainable practices and meet their environmental goals. Additionally, CarbonSmart USA leverages data science to:
- Businesses that have partnered with CarbonSmart USA have, on average, reduced their carbon emissions by 15% within the first year of implementing recommended sustainability measures.
- Data-driven sustainability initiatives have led to $5 million in annual cost savings for companies through reduced energy consumption and waste reduction.
- CarbonSmart USA's recommendations have helped businesses collectively achieve a 30% increase in their sustainability ratings, enhancing their reputation and appeal to environmentally conscious consumers.
- Over the past five years, CarbonSmart USA's services have contributed to the reduction of 1 million metric tons of CO2 emissions, playing a significant role in mitigating climate change.
Case study 8: Transportation and route optimization
Uber revolutionized the transportation industry by using data science to optimize ride-sharing and delivery routes. Their algorithms consider real-time traffic conditions, driver availability, and passenger demand to provide efficient, cost-effective transportation services. Other use cases include:
- Uber's data-driven routing and matching algorithms have led to an average 20% reduction in travel time for passengers, ensuring quicker and more efficient transportation.
- By optimizing driver routes and minimizing detours, Uber has contributed to a 30% decrease in fuel consumption for drivers, resulting in cost savings and reduced environmental impact.
- Uber's real-time demand prediction models have helped reduce passenger wait times by 25%, enhancing customer satisfaction and increasing the number of rides booked.
- Over the past decade, Uber's data-driven approach has enabled 100 million active users to complete over 15 billion trips, demonstrating the scale and impact of their transportation services.
Lyft, a competitor to Uber, also relies on data science to enhance ride-sharing experiences. They use predictive analytics to match drivers with passengers efficiently and reduce wait times. This data-driven approach contributes to higher customer satisfaction and driver engagement. Additionally,
- Lyft's data-driven matching algorithms have resulted in an average wait time reduction of 20% for passengers, ensuring faster and more convenient rides.
- By optimizing driver-passenger pairings, Lyft has seen a 15% increase in driver earnings, making their platform more attractive to drivers and reducing turnover.
- Lyft's predictive analytics for demand forecasting have led to 98% accuracy in predicting peak hours, allowing for proactive driver allocation and improved service quality during high-demand periods.
- Customer surveys have shown a 25% increase in overall satisfaction among Lyft users who have experienced shorter wait times and smoother ride-sharing experiences.
Case study 9: Natural language processing in customer service
Zendesk, a customer service software company, utilizes natural language processing (NLP) to enhance customer support. Their NLP algorithms can analyze and categorize customer inquiries, automatically routing them to the most suitable support agent. This results in faster response times and improved customer experiences. Furthermore,
- Zendesk's NLP-driven inquiry routing has led to a 40% reduction in average response times for customer inquiries, ensuring quicker issue resolution and higher customer satisfaction.
- Customer support agents using Zendesk's NLP tools have reported a 25% increase in productivity, as the technology assists in categorizing and prioritizing inquiries, allowing agents to focus on more complex issues.
- Zendesk's automated categorization of customer inquiries has resulted in a 30% decrease in support ticket misrouting, reducing the chances of issues falling through the cracks and ensuring that customers' needs are addressed promptly.
- Customer feedback surveys indicate a 15% improvement in overall satisfaction since the implementation of Zendesk's NLP-enhanced customer support, highlighting the positive impact on the customer experience.
Case study 10: Environmental conservation and data analysis
NASA collects and analyzes vast amounts of data to better understand Earth's environment and climate. Their satellite observations, climate models, and data science tools contribute to crucial insights about climate change, weather forecasting, and natural disaster monitoring.
Here’s how NASA leverages data science:
- NASA's satellite observations have provided essential data for climate research, contributing to a 0.15°C reduction in the uncertainty of global temperature measurements, and enhancing our understanding of climate change.
- Their climate models have helped predict the sea level rise with 95% accuracy, which is vital for coastal planning and adaptation strategies in the face of rising sea levels.
- NASA's data-driven natural disaster monitoring has enabled a 35% increase in the accuracy of hurricane track predictions, allowing for better preparedness and evacuation planning.
- Over the past decade, NASA's climate data and research have led to a 20% reduction in the margin of error in long-term climate projections, improving our ability to plan for and mitigate the impacts of climate change.
The World Wildlife Fund (WWF) employs data science to support conservation efforts. They use data to track endangered species, monitor deforestation, and combat illegal wildlife trade. By leveraging data, WWF can make informed decisions and drive initiatives to protect the planet's biodiversity. Additionally,
- WWF's data-driven approach has led to a 25% increase in the accuracy of endangered species tracking, enabling more effective protection measures for vulnerable wildlife populations.
- Their deforestation monitoring efforts have contributed to a 20% reduction in illegal logging rates in critical rainforest regions, helping to combat deforestation and its associated environmental impacts.
- WWF's data-driven campaigns and initiatives have generated $100 million in donations and grants over the past five years, providing crucial funding for conservation projects worldwide.
- By leveraging data science, WWF has successfully influenced policy changes in 15 countries, leading to stronger regulations against illegal wildlife trade and habitat destruction.
Data science is not just a buzzword; it's a transformative force that reshapes industries and improves our daily lives. The real-world case studies mentioned above illustrate the incredible potential of data science in diverse domains, from healthcare to agriculture and beyond.
As technology advances, we can expect even more innovative applications of data science that will continue to drive progress and innovation across various sectors.
Whether predicting machine failures, personalizing healthcare treatments, or optimizing energy consumption, data science is at the forefront of solving some of the world's most pressing challenges.
Turing's expert data scientists offer tailored, cutting-edge, data-driven data science solutions across industries. With ethical data practices, scalable approaches, and a commitment to continuous improvement, Turing empowers organizations to harness the full potential of data science, driving innovation and progress in an ever-evolving technological landscape.
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Aditya is a content writer with 5+ years of experience writing for various industries including Marketing, SaaS, B2B, IT, and Edtech among others. You can find him watching anime or playing games when he’s not writing.
Frequently Asked Questions
Real-world data science case studies differ significantly from academic examples. While academic exercises often feature clean, well-structured data and simplified scenarios, real-world projects tackle messy, diverse data sources with practical constraints and genuine business objectives. These case studies reflect the complexities data scientists face when translating data into actionable insights in the corporate world.
Real-world data science projects come with common challenges. Data quality issues, including missing or inaccurate data, can hinder analysis. Domain expertise gaps may result in misinterpretation of results. Resource constraints might limit project scope or access to necessary tools and talent. Ethical considerations, like privacy and bias, demand careful handling.
Lastly, as data and business needs evolve, data science projects must adapt and stay relevant, posing an ongoing challenge.
Real-world data science case studies play a crucial role in helping companies make informed decisions. By analyzing their own data, businesses gain valuable insights into customer behavior, market trends, and operational efficiencies.
These insights empower data-driven strategies, aiding in more effective resource allocation, product development, and marketing efforts. Ultimately, case studies bridge the gap between data science and business decision-making, enhancing a company's ability to thrive in a competitive landscape.
Key takeaways from these case studies for organizations include the importance of cultivating a data-driven culture that values evidence-based decision-making. Investing in robust data infrastructure is essential to support data initiatives. Collaborating closely between data scientists and domain experts ensures that insights align with business goals.
Finally, continuous monitoring and refinement of data solutions are critical for maintaining relevance and effectiveness in a dynamic business environment. Embracing these principles can lead to tangible benefits and sustainable success in real-world data science endeavors.
Data science is a powerful driver of innovation and problem-solving across diverse industries. By harnessing data, organizations can uncover hidden patterns, automate repetitive tasks, optimize operations, and make informed decisions.
In healthcare, for example, data-driven diagnostics and treatment plans improve patient outcomes. In finance, predictive analytics enhances risk management. In transportation, route optimization reduces costs and emissions. Data science empowers industries to innovate and solve complex challenges in ways that were previously unimaginable.
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Federal Trade Commission
data cleaning , Federal Data Strategy , open data , data sharing
Profile in Data Sharing - National Electronic Interstate Compact Enterprise
The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the federal government and states support children who are being placed for adoption or foster care across state lines. It greatly reduces the work and time required for states to exchange paperwork and information needed to process the placements. Additionally, NEICE allows child welfare workers to communicate and provide timely updates to courts, relevant private service providers, and families.
Profile in Data Sharing - National Health Service Corps Loan Repayment Programs
The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Health Resources and Services Administration collaborates with the Department of Education to make it easier to apply to serve medically underserved communities - reducing applicant burden and improving processing efficiency.
Profile in Data Sharing - Roadside Inspection Data
The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the Department of Transportation collaborates with the Customs and Border Patrol and state partners to prescreen commercial motor vehicles entering the US and to focus inspections on unsafe carriers and drivers.
Profiles in Data Sharing - U.S. Citizenship and Immigration Service
The Federal CDO Council’s Data Sharing Working Group highlights successful data sharing activities to recognize mature data sharing practices as well as to incentivize and inspire others to take part in similar collaborations. This Profile in Data Sharing focuses on how the U.S. Citizenship and Immigration Service (USCIS) collaborated with the Centers for Disease Control to notify state, local, tribal, and territorial public health authorities so they can connect with individuals in their communities about their potential exposure.
SBA’s Approach to Identifying Data, Using a Learning Agenda, and Leveraging Partnerships to Build its Evidence Base
Through its Enterprise Learning Agenda, Small Business Administration’s (SBA) staff identify essential research questions, a plan to answer them, and how data held outside the agency can help provide further insights. Other agencies can learn from the innovative ways SBA identifies data to answer agency strategic questions and adopt those aspects that work for their own needs.
Small Business Administration
process redesign , Federal Data Strategy
Supercharging Data through Validation as a Service
USDA's Food and Nutrition Service restructured its approach to data validation at the state level using an open-source, API-based validation service managed at the federal level.
data cleaning , data validation , API , data sharing , process redesign , Federal Data Strategy
The Census Bureau Uses Its Own Data to Increase Response Rates, Helps Communities and Other Stakeholders Do the Same
The Census Bureau team produced a new interactive mapping tool in early 2018 called the Response Outreach Area Mapper (ROAM), an application that resulted in wider use of authoritative Census Bureau data, not only to improve the Census Bureau’s own operational efficiency, but also for use by tribal, state, and local governments, national and local partners, and other community groups. Other agency data practitioners can learn from the Census Bureau team’s experience communicating technical needs to non-technical executives, building analysis tools with widely-used software, and integrating efforts with stakeholders and users.
open data , data sharing , data management , data analysis , Federal Data Strategy
The Mapping Medicare Disparities Tool
The Centers for Medicare & Medicaid Services’ Office of Minority Health (CMS OMH) Mapping Medicare Disparities Tool harnessed the power of millions of data records while protecting the privacy of individuals, creating an easy-to-use tool to better understand health disparities.
Centers for Medicare & Medicaid Services
geospatial , Federal Data Strategy , open data
The Veterans Legacy Memorial
The Veterans Legacy Memorial (VLM) is a digital platform to help families, survivors, and fellow veterans to take a leading role in honoring their beloved veteran. Built on millions of existing National Cemetery Administration (NCA) records in a 25-year-old database, VLM is a powerful example of an agency harnessing the potential of a legacy system to provide a modernized service that better serves the public.
data sharing , data visualization , Federal Data Strategy
Transitioning to a Data Driven Culture at CMS
This case study describes how CMS announced the creation of the Office of Information Products and Data Analytics (OIPDA) to take the lead in making data use and dissemination a core function of the agency.
data management , data sharing , data analysis , data analytics
PDF (10 pages)
U.S. Department of Labor Case Study: Software Development Kits
The U.S. Department of Labor sought to go beyond merely making data available to developers and take ease of use of the data to the next level by giving developers tools that would make using DOL’s data easier. DOL created software development kits (SDKs), which are downloadable code packages that developers can drop into their apps, making access to DOL’s data easy for even the most novice developer. These SDKs have even been published as open source projects with the aim of speeding up their conversion to SDKs that will eventually support all federal APIs.
Department of Labor
open data , API
U.S. Geological Survey and U.S. Census Bureau collaborate on national roads and boundaries data
It is a well-kept secret that the U.S. Geological Survey and the U.S. Census Bureau were the original two federal agencies to build the first national digital database of roads and boundaries in the United States. The agencies joined forces to develop homegrown computer software and state of the art technologies to convert existing USGS topographic maps of the nation to the points, lines, and polygons that fueled early GIS. Today, the USGS and Census Bureau have a longstanding goal to leverage and use roads and authoritative boundary datasets.
U.S. Geological Survey and U.S. Census Bureau
data management , data sharing , data standards , data validation , data visualization , Federal Data Strategy , geospatial , open data , quality
USA.gov Uses Human-Centered Design to Roll Out AI Chatbot
To improve customer service and give better answers to users of the USA.gov website, the Technology Transformation and Services team at General Services Administration (GSA) created a chatbot using artificial intelligence (AI) and automation.
General Services Administration
AI , Federal Data Strategy
An official website of the Office of Management and Budget, the General Services Administration, and the Office of Government Information Services.
This section contains explanations of common terms referenced on resources.data.gov.
10 Real World Data Science Case Studies Projects with Example
Top 10 Data Science Case Studies Projects with Examples and Solutions in Python to inspire your data science learning in 2023.
BelData science has been a trending buzzword in recent times. With wide applications in various sectors like healthcare , education, retail, transportation, media, and banking -data science applications are at the core of pretty much every industry out there. The possibilities are endless: analysis of frauds in the finance sector or the personalization of recommendations on eCommerce businesses. We have developed ten exciting data science case studies to explain how data science is leveraged across various industries to make smarter decisions and develop innovative personalized products tailored to specific customers.
Walmart Sales Forecasting Data Science Project
Downloadable solution code | Explanatory videos | Tech Support
Table of Contents
Data science case studies in retail , data science case study examples in entertainment industry , data analytics case study examples in travel industry , case studies for data analytics in social media , real world data science projects in healthcare, data analytics case studies in oil and gas, what is a case study in data science, how do you prepare a data science case study, 10 most interesting data science case studies with examples.
So, without much ado, let's get started with data science business case studies !
With humble beginnings as a simple discount retailer, today, Walmart operates in 10,500 stores and clubs in 24 countries and eCommerce websites, employing around 2.2 million people around the globe. For the fiscal year ended January 31, 2021, Walmart's total revenue was $559 billion showing a growth of $35 billion with the expansion of the eCommerce sector. Walmart is a data-driven company that works on the principle of 'Everyday low cost' for its consumers. To achieve this goal, they heavily depend on the advances of their data science and analytics department for research and development, also known as Walmart Labs. Walmart is home to the world's largest private cloud, which can manage 2.5 petabytes of data every hour! To analyze this humongous amount of data, Walmart has created 'Data Café,' a state-of-the-art analytics hub located within its Bentonville, Arkansas headquarters. The Walmart Labs team heavily invests in building and managing technologies like cloud, data, DevOps , infrastructure, and security.
Walmart is experiencing massive digital growth as the world's largest retailer . Walmart has been leveraging Big data and advances in data science to build solutions to enhance, optimize and customize the shopping experience and serve their customers in a better way. At Walmart Labs, data scientists are focused on creating data-driven solutions that power the efficiency and effectiveness of complex supply chain management processes. Here are some of the applications of data science at Walmart:
i) Personalized Customer Shopping Experience
Walmart analyses customer preferences and shopping patterns to optimize the stocking and displaying of merchandise in their stores. Analysis of Big data also helps them understand new item sales, make decisions on discontinuing products, and the performance of brands.
ii) Order Sourcing and On-Time Delivery Promise
Millions of customers view items on Walmart.com, and Walmart provides each customer a real-time estimated delivery date for the items purchased. Walmart runs a backend algorithm that estimates this based on the distance between the customer and the fulfillment center, inventory levels, and shipping methods available. The supply chain management system determines the optimum fulfillment center based on distance and inventory levels for every order. It also has to decide on the shipping method to minimize transportation costs while meeting the promised delivery date.
iii) Packing Optimization
Also known as Box recommendation is a daily occurrence in the shipping of items in retail and eCommerce business. When items of an order or multiple orders for the same customer are ready for packing, Walmart has developed a recommender system that picks the best-sized box which holds all the ordered items with the least in-box space wastage within a fixed amount of time. This Bin Packing problem is a classic NP-Hard problem familiar to data scientists .
Whenever items of an order or multiple orders placed by the same customer are picked from the shelf and are ready for packing, the box recommendation system determines the best-sized box to hold all the ordered items with a minimum of in-box space wasted. This problem is known as the Bin Packing Problem, another classic NP-Hard problem familiar to data scientists.
Here is a link to a sales prediction data science case study to help you understand the applications of Data Science in the real world. Walmart Sales Forecasting Project uses historical sales data for 45 Walmart stores located in different regions. Each store contains many departments, and you must build a model to project the sales for each department in each store. This data science case study aims to create a predictive model to predict the sales of each product. You can also try your hands-on Inventory Demand Forecasting Data Science Project to develop a machine learning model to forecast inventory demand accurately based on historical sales data.
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Amazon is an American multinational technology-based company based in Seattle, USA. It started as an online bookseller, but today it focuses on eCommerce, cloud computing , digital streaming, and artificial intelligence . It hosts an estimate of 1,000,000,000 gigabytes of data across more than 1,400,000 servers. Through its constant innovation in data science and big data Amazon is always ahead in understanding its customers. Here are a few data analytics case study examples at Amazon:
i) Recommendation Systems
Data science models help amazon understand the customers' needs and recommend them to them before the customer searches for a product; this model uses collaborative filtering. Amazon uses 152 million customer purchases data to help users to decide on products to be purchased. The company generates 35% of its annual sales using the Recommendation based systems (RBS) method.
Here is a Recommender System Project to help you build a recommendation system using collaborative filtering.
ii) Retail Price Optimization
Amazon product prices are optimized based on a predictive model that determines the best price so that the users do not refuse to buy it based on price. The model carefully determines the optimal prices considering the customers' likelihood of purchasing the product and thinks the price will affect the customers' future buying patterns. Price for a product is determined according to your activity on the website, competitors' pricing, product availability, item preferences, order history, expected profit margin, and other factors.
Check Out this Retail Price Optimization Project to build a Dynamic Pricing Model.
iii) Fraud Detection
Being a significant eCommerce business, Amazon remains at high risk of retail fraud. As a preemptive measure, the company collects historical and real-time data for every order. It uses Machine learning algorithms to find transactions with a higher probability of being fraudulent. This proactive measure has helped the company restrict clients with an excessive number of returns of products.
You can look at this Credit Card Fraud Detection Project to implement a fraud detection model to classify fraudulent credit card transactions.
Let us explore data analytics case study examples in the entertainment indusry.
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Netflix started as a DVD rental service in 1997 and then has expanded into the streaming business. Headquartered in Los Gatos, California, Netflix is the largest content streaming company in the world. Currently, Netflix has over 208 million paid subscribers worldwide, and with thousands of smart devices which are presently streaming supported, Netflix has around 3 billion hours watched every month. The secret to this massive growth and popularity of Netflix is its advanced use of data analytics and recommendation systems to provide personalized and relevant content recommendations to its users. The data is collected over 100 billion events every day. Here are a few examples of data analysis case studies applied at Netflix :
i) Personalized Recommendation System
Netflix uses over 1300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Some of the data that Netflix collects from its users include Viewing time, platform searches for keywords, Metadata related to content abandonment, such as content pause time, rewind, rewatched. Using this data, Netflix can predict what a viewer is likely to watch and give a personalized watchlist to a user. Some of the algorithms used by the Netflix recommendation system are Personalized video Ranking, Trending now ranker, and the Continue watching now ranker.
ii) Content Development using Data Analytics
Netflix uses data science to analyze the behavior and patterns of its user to recognize themes and categories that the masses prefer to watch. This data is used to produce shows like The umbrella academy, and Orange Is the New Black, and the Queen's Gambit. These shows seem like a huge risk but are significantly based on data analytics using parameters, which assured Netflix that they would succeed with its audience. Data analytics is helping Netflix come up with content that their viewers want to watch even before they know they want to watch it.
iii) Marketing Analytics for Campaigns
Netflix uses data analytics to find the right time to launch shows and ad campaigns to have maximum impact on the target audience. Marketing analytics helps come up with different trailers and thumbnails for other groups of viewers. For example, the House of Cards Season 5 trailer with a giant American flag was launched during the American presidential elections, as it would resonate well with the audience.
Here is a Customer Segmentation Project using association rule mining to understand the primary grouping of customers based on various parameters.
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In a world where Purchasing music is a thing of the past and streaming music is a current trend, Spotify has emerged as one of the most popular streaming platforms. With 320 million monthly users, around 4 billion playlists, and approximately 2 million podcasts, Spotify leads the pack among well-known streaming platforms like Apple Music, Wynk, Songza, amazon music, etc. The success of Spotify has mainly depended on data analytics. By analyzing massive volumes of listener data, Spotify provides real-time and personalized services to its listeners. Most of Spotify's revenue comes from paid premium subscriptions. Here are some of the examples of case study on data analytics used by Spotify to provide enhanced services to its listeners:
i) Personalization of Content using Recommendation Systems
Spotify uses Bart or Bayesian Additive Regression Trees to generate music recommendations to its listeners in real-time. Bart ignores any song a user listens to for less than 30 seconds. The model is retrained every day to provide updated recommendations. A new Patent granted to Spotify for an AI application is used to identify a user's musical tastes based on audio signals, gender, age, accent to make better music recommendations.
Spotify creates daily playlists for its listeners, based on the taste profiles called 'Daily Mixes,' which have songs the user has added to their playlists or created by the artists that the user has included in their playlists. It also includes new artists and songs that the user might be unfamiliar with but might improve the playlist. Similar to it is the weekly 'Release Radar' playlists that have newly released artists' songs that the listener follows or has liked before.
ii) Targetted marketing through Customer Segmentation
With user data for enhancing personalized song recommendations, Spotify uses this massive dataset for targeted ad campaigns and personalized service recommendations for its users. Spotify uses ML models to analyze the listener's behavior and group them based on music preferences, age, gender, ethnicity, etc. These insights help them create ad campaigns for a specific target audience. One of their well-known ad campaigns was the meme-inspired ads for potential target customers, which was a huge success globally.
iii) CNN's for Classification of Songs and Audio Tracks
Spotify builds audio models to evaluate the songs and tracks, which helps develop better playlists and recommendations for its users. These allow Spotify to filter new tracks based on their lyrics and rhythms and recommend them to users like similar tracks ( collaborative filtering). Spotify also uses NLP ( Natural language processing) to scan articles and blogs to analyze the words used to describe songs and artists. These analytical insights can help group and identify similar artists and songs and leverage them to build playlists.
Here is a Music Recommender System Project for you to start learning. We have listed another music recommendations dataset for you to use for your projects: Dataset1 . You can use this dataset of Spotify metadata to classify songs based on artists, mood, liveliness. Plot histograms, heatmaps to get a better understanding of the dataset. Use classification algorithms like logistic regression, SVM, and Principal component analysis to generate valuable insights from the dataset.
Below you will find case studies for data analytics in the travel and tourism industry.
Airbnb was born in 2007 in San Francisco and has since grown to 4 million Hosts and 5.6 million listings worldwide who have welcomed more than 1 billion guest arrivals in almost every country across the globe. Airbnb is active in every country on the planet except for Iran, Sudan, Syria, and North Korea. That is around 97.95% of the world. Using data as a voice of their customers, Airbnb uses the large volume of customer reviews, host inputs to understand trends across communities, rate user experiences, and uses these analytics to make informed decisions to build a better business model. The data scientists at Airbnb are developing exciting new solutions to boost the business and find the best mapping for its customers and hosts. Airbnb data servers serve approximately 10 million requests a day and process around one million search queries. Data is the voice of customers at AirBnB and offers personalized services by creating a perfect match between the guests and hosts for a supreme customer experience.
i) Recommendation Systems and Search Ranking Algorithms
Airbnb helps people find 'local experiences' in a place with the help of search algorithms that make searches and listings precise. Airbnb uses a 'listing quality score' to find homes based on the proximity to the searched location and uses previous guest reviews. Airbnb uses deep neural networks to build models that take the guest's earlier stays into account and area information to find a perfect match. The search algorithms are optimized based on guest and host preferences, rankings, pricing, and availability to understand users’ needs and provide the best match possible.
ii) Natural Language Processing for Review Analysis
Airbnb characterizes data as the voice of its customers. The customer and host reviews give a direct insight into the experience. The star ratings alone cannot be an excellent way to understand it quantitatively. Hence Airbnb uses natural language processing to understand reviews and the sentiments behind them. The NLP models are developed using Convolutional neural networks .
Practice this Sentiment Analysis Project for analyzing product reviews to understand the basic concepts of natural language processing.
iii) Smart Pricing using Predictive Analytics
The Airbnb hosts community uses the service as a supplementary income. The vacation homes and guest houses rented to customers provide for rising local community earnings as Airbnb guests stay 2.4 times longer and spend approximately 2.3 times the money compared to a hotel guest. The profits are a significant positive impact on the local neighborhood community. Airbnb uses predictive analytics to predict the prices of the listings and help the hosts set a competitive and optimal price. The overall profitability of the Airbnb host depends on factors like the time invested by the host and responsiveness to changing demands for different seasons. The factors that impact the real-time smart pricing are the location of the listing, proximity to transport options, season, and amenities available in the neighborhood of the listing.
Here is a Price Prediction Project to help you understand the concept of predictive analysis which is widely common in case studies for data analytics.
Uber is the biggest global taxi service provider. As of December 2018, Uber has 91 million monthly active consumers and 3.8 million drivers. Uber completes 14 million trips each day. Uber uses data analytics and big data-driven technologies to optimize their business processes and provide enhanced customer service. The Data Science team at uber has been exploring futuristic technologies to provide better service constantly. Machine learning and data analytics help Uber make data-driven decisions that enable benefits like ride-sharing, dynamic price surges, better customer support, and demand forecasting. Here are some of the real world data science projects used by uber:
i) Dynamic Pricing for Price Surges and Demand Forecasting
Uber prices change at peak hours based on demand. Uber uses surge pricing to encourage more cab drivers to sign up with the company, to meet the demand from the passengers. When the prices increase, the driver and the passenger are both informed about the surge in price. Uber uses a predictive model for price surging called the 'Geosurge' ( patented). It is based on the demand for the ride and the location.
ii) One-Click Chat
Uber has developed a Machine learning and natural language processing solution called one-click chat or OCC for coordination between drivers and users. This feature anticipates responses for commonly asked questions, making it easy for the drivers to respond to customer messages. Drivers can reply with the clock of just one button. One-Click chat is developed on Uber's machine learning platform Michelangelo to perform NLP on rider chat messages and generate appropriate responses to them.
iii) Customer Retention
Failure to meet the customer demand for cabs could lead to users opting for other services. Uber uses machine learning models to bridge this demand-supply gap. By using prediction models to predict the demand in any location, uber retains its customers. Uber also uses a tier-based reward system, which segments customers into different levels based on usage. The higher level the user achieves, the better are the perks. Uber also provides personalized destination suggestions based on the history of the user and their frequently traveled destinations.
You can take a look at this Python Chatbot Project and build a simple chatbot application to understand better the techniques used for natural language processing. You can also practice the working of a demand forecasting model with this project using time series analysis. You can look at this project which uses time series forecasting and clustering on a dataset containing geospatial data for forecasting customer demand for ola rides.
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LinkedIn is the largest professional social networking site with nearly 800 million members in more than 200 countries worldwide. Almost 40% of the users access LinkedIn daily, clocking around 1 billion interactions per month. The data science team at LinkedIn works with this massive pool of data to generate insights to build strategies, apply algorithms and statistical inferences to optimize engineering solutions, and help the company achieve its goals. Here are some of the real world data science projects at LinkedIn:
i) LinkedIn Recruiter Implement Search Algorithms and Recommendation Systems
LinkedIn Recruiter helps recruiters build and manage a talent pool to optimize the chances of hiring candidates successfully. This sophisticated product works on search and recommendation engines. The LinkedIn recruiter handles complex queries and filters on a constantly growing large dataset. The results delivered have to be relevant and specific. The initial search model was based on linear regression but was eventually upgraded to Gradient Boosted decision trees to include non-linear correlations in the dataset. In addition to these models, the LinkedIn recruiter also uses the Generalized Linear Mix model to improve the results of prediction problems to give personalized results.
ii) Recommendation Systems Personalized for News Feed
The LinkedIn news feed is the heart and soul of the professional community. A member's newsfeed is a place to discover conversations among connections, career news, posts, suggestions, photos, and videos. Every time a member visits LinkedIn, machine learning algorithms identify the best exchanges to be displayed on the feed by sorting through posts and ranking the most relevant results on top. The algorithms help LinkedIn understand member preferences and help provide personalized news feeds. The algorithms used include logistic regression, gradient boosted decision trees and neural networks for recommendation systems.
iii) CNN's to Detect Inappropriate Content
To provide a professional space where people can trust and express themselves professionally in a safe community has been a critical goal at LinkedIn. LinkedIn has heavily invested in building solutions to detect fake accounts and abusive behavior on their platform. Any form of spam, harassment, inappropriate content is immediately flagged and taken down. These can range from profanity to advertisements for illegal services. LinkedIn uses a Convolutional neural networks based machine learning model. This classifier trains on a training dataset containing accounts labeled as either "inappropriate" or "appropriate." The inappropriate list consists of accounts having content from "blocklisted" phrases or words and a small portion of manually reviewed accounts reported by the user community.
Here is a Text Classification Project to help you understand NLP basics for text classification. You can find a news recommendation system dataset to help you build a personalized news recommender system. You can also use this dataset to build a classifier using logistic regression, Naive Bayes, or Neural networks to classify toxic comments.
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Pfizer is a multinational pharmaceutical company headquartered in New York, USA. One of the largest pharmaceutical companies globally known for developing a wide range of medicines and vaccines in disciplines like immunology, oncology, cardiology, and neurology. Pfizer became a household name in 2010 when it was the first to have a COVID-19 vaccine with FDA. In early November 2021, The CDC has approved the Pfizer vaccine for kids aged 5 to 11. Pfizer has been using machine learning and artificial intelligence to develop drugs and streamline trials, which played a massive role in developing and deploying the COVID-19 vaccine. Here are a few data analytics case studies by Pfizer :
i) Identifying Patients for Clinical Trials
Artificial intelligence and machine learning are used to streamline and optimize clinical trials to increase their efficiency. Natural language processing and exploratory data analysis of patient records can help identify suitable patients for clinical trials. These can help identify patients with distinct symptoms. These can help examine interactions of potential trial members' specific biomarkers, predict drug interactions and side effects which can help avoid complications. Pfizer's AI implementation helped rapidly identify signals within the noise of millions of data points across their 44,000-candidate COVID-19 clinical trial.
ii) Supply Chain and Manufacturing
Data science and machine learning techniques help pharmaceutical companies better forecast demand for vaccines and drugs and distribute them efficiently. Machine learning models can help identify efficient supply systems by automating and optimizing the production steps. These will help supply drugs customized to small pools of patients in specific gene pools. Pfizer uses Machine learning to predict the maintenance cost of equipment used. Predictive maintenance using AI is the next big step for Pharmaceutical companies to reduce costs.
iii) Drug Development
Computer simulations of proteins, and tests of their interactions, and yield analysis help researchers develop and test drugs more efficiently. In 2016 Watson Health and Pfizer announced a collaboration to utilize IBM Watson for Drug Discovery to help accelerate Pfizer's research in immuno-oncology, an approach to cancer treatment that uses the body's immune system to help fight cancer. Deep learning models have been used recently for bioactivity and synthesis prediction for drugs and vaccines in addition to molecular design. Deep learning has been a revolutionary technique for drug discovery as it factors everything from new applications of medications to possible toxic reactions which can save millions in drug trials.
You can create a Machine learning model to predict molecular activity to help design medicine using this dataset . You may build a CNN or a Deep neural network for this data analyst case study project.
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9) Shell Data Analyst Case Study Project
Shell is a global group of energy and petrochemical companies with over 80,000 employees in around 70 countries. Shell uses advanced technologies and innovations to help build a sustainable energy future. Shell is going through a significant transition as the world needs more and cleaner energy solutions to be a clean energy company by 2050. It requires substantial changes in the way in which energy is used. Digital technologies, including AI and Machine Learning, play an essential role in this transformation. These include efficient exploration and energy production, more reliable manufacturing, more nimble trading, and a personalized customer experience. Using AI in various phases of the organization will help achieve this goal and stay competitive in the market. Here are a few data analytics case studies in the petrochemical industry:
i) Precision Drilling
Shell is involved in the processing mining oil and gas supply, ranging from mining hydrocarbons to refining the fuel to retailing them to customers. Recently Shell has included reinforcement learning to control the drilling equipment used in mining. Reinforcement learning works on a reward-based system based on the outcome of the AI model. The algorithm is designed to guide the drills as they move through the surface, based on the historical data from drilling records. It includes information such as the size of drill bits, temperatures, pressures, and knowledge of the seismic activity. This model helps the human operator understand the environment better, leading to better and faster results will minor damage to machinery used.
ii) Efficient Charging Terminals
Due to climate changes, governments have encouraged people to switch to electric vehicles to reduce carbon dioxide emissions. However, the lack of public charging terminals has deterred people from switching to electric cars. Shell uses AI to monitor and predict the demand for terminals to provide efficient supply. Multiple vehicles charging from a single terminal may create a considerable grid load, and predictions on demand can help make this process more efficient.
iii) Monitoring Service and Charging Stations
Another Shell initiative trialed in Thailand and Singapore is the use of computer vision cameras, which can think and understand to watch out for potentially hazardous activities like lighting cigarettes in the vicinity of the pumps while refueling. The model is built to process the content of the captured images and label and classify it. The algorithm can then alert the staff and hence reduce the risk of fires. You can further train the model to detect rash driving or thefts in the future.
Here is a project to help you understand multiclass image classification. You can use the Hourly Energy Consumption Dataset to build an energy consumption prediction model. You can use time series with XGBoost to develop your model.
10) Zomato Case Study on Data Analytics
Zomato was founded in 2010 and is currently one of the most well-known food tech companies. Zomato offers services like restaurant discovery, home delivery, online table reservation, online payments for dining, etc. Zomato partners with restaurants to provide tools to acquire more customers while also providing delivery services and easy procurement of ingredients and kitchen supplies. Currently, Zomato has over 2 lakh restaurant partners and around 1 lakh delivery partners. Zomato has closed over ten crore delivery orders as of date. Zomato uses ML and AI to boost their business growth, with the massive amount of data collected over the years from food orders and user consumption patterns. Here are a few examples of data analyst case study project developed by the data scientists at Zomato:
i) Personalized Recommendation System for Homepage
Zomato uses data analytics to create personalized homepages for its users. Zomato uses data science to provide order personalization, like giving recommendations to the customers for specific cuisines, locations, prices, brands, etc. Restaurant recommendations are made based on a customer's past purchases, browsing history, and what other similar customers in the vicinity are ordering. This personalized recommendation system has led to a 15% improvement in order conversions and click-through rates for Zomato.
You can use the Restaurant Recommendation Dataset to build a restaurant recommendation system to predict what restaurants customers are most likely to order from, given the customer location, restaurant information, and customer order history.
ii) Analyzing Customer Sentiment
Zomato uses Natural language processing and Machine learning to understand customer sentiments using social media posts and customer reviews. These help the company gauge the inclination of its customer base towards the brand. Deep learning models analyze the sentiments of various brand mentions on social networking sites like Twitter, Instagram, Linked In, and Facebook. These analytics give insights to the company, which helps build the brand and understand the target audience.
iii) Predicting Food Preparation Time (FPT)
Food delivery time is an essential variable in the estimated delivery time of the order placed by the customer using Zomato. The food preparation time depends on numerous factors like the number of dishes ordered, time of the day, footfall in the restaurant, day of the week, etc. Accurate prediction of the food preparation time can help make a better prediction of the Estimated delivery time, which will help delivery partners less likely to breach it. Zomato uses a Bidirectional LSTM-based deep learning model that considers all these features and provides food preparation time for each order in real-time.
Data scientists are companies' secret weapons when analyzing customer sentiments and behavior and leveraging it to drive conversion, loyalty, and profits. These 10 data science case studies projects with examples and solutions show you how various organizations use data science technologies to succeed and be at the top of their field! To summarize, Data Science has not only accelerated the performance of companies but has also made it possible to manage & sustain their performance with ease.
FAQs on Data Analysis Case Studies
A case study in data science is an in-depth analysis of a real-world problem using data-driven approaches. It involves collecting, cleaning, and analyzing data to extract insights and solve challenges, offering practical insights into how data science techniques can address complex issues across various industries.
To create a data science case study, identify a relevant problem, define objectives, and gather suitable data. Clean and preprocess data, perform exploratory data analysis, and apply appropriate algorithms for analysis. Summarize findings, visualize results, and provide actionable recommendations, showcasing the problem-solving potential of data science techniques.
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Qualitative case study data analysis: an example from practice
- 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
- PMID: 25976531
- DOI: 10.7748/nr.22.5.8.e1307
Aim: To illustrate an approach to data analysis in qualitative case study methodology.
Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.
Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.
Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.
Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.
Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.
Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.
- Case-Control Studies*
- Data Interpretation, Statistical*
- Nursing Research / methods*
- Qualitative Research*
- Research Design
Data Analysis Case Study: Learn From Humana’s Automated Data Analysis Project
Lillian Pierson, P.E.
Got data? Great! Looking for that perfect data analysis case study to help you get started using it? You’re in the right place.
If you’ve ever struggled to decide what to do next with your data projects, to actually find meaning in the data, or even to decide what kind of data to collect, then KEEP READING…
Deep down, you know what needs to happen. You need to initiate and execute a data strategy that really moves the needle for your organization. One that produces seriously awesome business results.
But how you’re in the right place to find out..
As a data strategist who has worked with 10 percent of Fortune 100 companies, today I’m sharing with you a case study that demonstrates just how real businesses are making real wins with data analysis.
In the post below, we’ll look at:
- A shining data success story;
- What went on ‘under-the-hood’ to support that successful data project; and
- The exact data technologies used by the vendor, to take this project from pure strategy to pure success
If you prefer to watch this information rather than read it, it’s captured in the video below:
Here’s the url too: https://youtu.be/xMwZObIqvLQ
3 Action Items You Need To Take
To actually use the data analysis case study you’re about to get – you need to take 3 main steps. Those are:
- Reflect upon your organization as it is today (I left you some prompts below – to help you get started)
- Review winning data case collections (starting with the one I’m sharing here) and identify 5 that seem the most promising for your organization given it’s current set-up
- Assess your organization AND those 5 winning case collections. Based on that assessment, select the “QUICK WIN” data use case that offers your organization the most bang for it’s buck
Step 1: Reflect Upon Your Organization
Whenever you evaluate data case collections to decide if they’re a good fit for your organization, the first thing you need to do is organize your thoughts with respect to your organization as it is today.
Before moving into the data analysis case study, STOP and ANSWER THE FOLLOWING QUESTIONS – just to remind yourself:
- What is the business vision for our organization?
- What industries do we primarily support?
- What data technologies do we already have up and running, that we could use to generate even more value?
- What team members do we have to support a new data project? And what are their data skillsets like?
- What type of data are we mostly looking to generate value from? Structured? Semi-Structured? Un-structured? Real-time data? Huge data sets? What are our data resources like?
Jot down some notes while you’re here. Then keep them in mind as you read on to find out how one company, Humana, used its data to achieve a 28 percent increase in customer satisfaction. Also include its 63 percent increase in employee engagement! (That’s such a seriously impressive outcome, right?!)
Step 2: Review Data Case Studies
Here we are, already at step 2. It’s time for you to start reviewing data analysis case studies (starting with the one I’m sharing below). I dentify 5 that seem the most promising for your organization given its current set-up.
Humana’s Automated Data Analysis Case Study
The key thing to note here is that the approach to creating a successful data program varies from industry to industry .
Let’s start with one to demonstrate the kind of value you can glean from these kinds of success stories.
Humana has provided health insurance to Americans for over 50 years. It is a service company focused on fulfilling the needs of its customers. A great deal of Humana’s success as a company rides on customer satisfaction, and the frontline of that battle for customers’ hearts and minds is Humana’s customer service center.
Call centers are hard to get right. A lot of emotions can arise during a customer service call, especially one relating to health and health insurance. Sometimes people are frustrated. At times, they’re upset. Also, there are times the customer service representative becomes aggravated, and the overall tone and progression of the phone call goes downhill. This is of course very bad for customer satisfaction.
Humana wanted to use artificial intelligence to improve customer satisfaction (and thus, customer retention rates & profits per customer).
Humana wanted to find a way to use artificial intelligence to monitor their phone calls and help their agents do a better job connecting with their customers in order to improve customer satisfaction (and thus, customer retention rates & profits per customer ).
In light of their business need, Humana worked with a company called Cogito, which specializes in voice analytics technology.
Cogito offers a piece of AI technology called Cogito Dialogue. It’s been trained to identify certain conversational cues as a way of helping call center representatives and supervisors stay actively engaged in a call with a customer.
The AI listens to cues like the customer’s voice pitch.
If it’s rising, or if the call representative and the customer talk over each other, then the dialogue tool will send out electronic alerts to the agent during the call.
Humana fed the dialogue tool customer service data from 10,000 calls and allowed it to analyze cues such as keywords, interruptions, and pauses, and these cues were then linked with specific outcomes. For example, if the representative is receiving a particular type of cues, they are likely to get a specific customer satisfaction result.
Customers were happier, and customer service representatives were more engaged..
This automated solution for data analysis has now been deployed in 200 Humana call centers and the company plans to roll it out to 100 percent of its centers in the future.
The initiative was so successful, Humana has been able to focus on next steps in its data program. The company now plans to begin predicting the type of calls that are likely to go unresolved, so they can send those calls over to management before they become frustrating to the customer and customer service representative alike.
What does this mean for you and your business?
Well, if you’re looking for new ways to generate value by improving the quantity and quality of the decision support that you’re providing to your customer service personnel, then this may be a perfect example of how you can do so.
Humana’s Business Use Cases
Humana’s data analysis case study includes two key business use cases:
- Analyzing customer sentiment; and
- Suggesting actions to customer service representatives.
Analyzing Customer Sentiment
First things first, before you go ahead and collect data, you need to ask yourself who and what is involved in making things happen within the business.
In the case of Humana, the actors were:
- The health insurance system itself
- The customer, and
- The customer service representative
As you can see in the use case diagram above, the relational aspect is pretty simple. You have a customer service representative and a customer. They are both producing audio data, and that audio data is being fed into the system.
Humana focused on collecting the key data points, shown in the image below, from their customer service operations.
By collecting data about speech style, pitch, silence, stress in customers’ voices, length of call, speed of customers’ speech, intonation, articulation, silence, and representatives’ manner of speaking, Humana was able to analyze customer sentiment and introduce techniques for improved customer satisfaction.
Having strategically defined these data points, the Cogito technology was able to generate reports about customer sentiment during the calls.
Suggesting actions to customer service representatives.
The second use case for the Humana data program follows on from the data gathered in the first case.
In Humana’s case, Cogito generated a host of call analyses and reports about key call issues.
In the second business use case, Cogito was able to suggest actions to customer service representatives, in real-time , to make use of incoming data and help improve customer satisfaction on the spot.
The technology Humana used provided suggestions via text message to the customer service representative, offering the following types of feedback:
- The tone of voice is too tense
- The speed of speaking is high
- The customer representative and customer are speaking at the same time
These alerts allowed the Humana customer service representatives to alter their approach immediately , improving the quality of the interaction and, subsequently, the customer satisfaction.
The preconditions for success in this use case were:
- The call-related data must be collected and stored
- The AI models must be in place to generate analysis on the data points that are recorded during the calls
Evidence of success can subsequently be found in a system that offers real-time suggestions for courses of action that the customer service representative can take to improve customer satisfaction.
Thanks to this data-intensive business use case, Humana was able to increase customer satisfaction, improve customer retention rates, and drive profits per customer.
The Technology That Supports This Data Analysis Case Study
I promised to dip into the tech side of things. This is especially for those of you who are interested in the ins and outs of how projects like this one are actually rolled out.
Here’s a little rundown of the main technologies we discovered when we investigated how Cogito runs in support of its clients like Humana.
- For cloud data management Cogito uses AWS, specifically the Athena product
- For on-premise big data management, the company used Apache HDFS – the distributed file system for storing big data
- They utilize MapReduce, for processing their data
- And Cogito also has traditional systems and relational database management systems such as PostgreSQL
- In terms of analytics and data visualization tools, Cogito makes use of Tableau
- And for its machine learning technology, these use cases required people with knowledge in Python, R, and SQL, as well as deep learning (Cogito uses the PyTorch library and the TensorFlow library)
These data science skill sets support the effective computing, deep learning , and natural language processing applications employed by Humana for this use case.
If you’re looking to hire people to help with your own data initiative, then people with those skills listed above, and with experience in these specific technologies, would be a huge help.
Step 3: S elect The “Quick Win” Data Use Case
Still there? Great!
It’s time to close the loop.
Remember those notes you took before you reviewed the study? I want you to STOP here and assess. Does this Humana case study seem applicable and promising as a solution, given your organization’s current set-up…
YES ▶ Excellent!
Earmark it and continue exploring other winning data use cases until you’ve identified 5 that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that.
NO , Lillian – It’s not applicable. ▶ No problem.
Discard the information and continue exploring the winning data use cases we’ve categorized for you according to business function and industry. Save time by dialing down into the business function you know your business really needs help with now. Identify 5 winning data use cases that seem like great fits for your businesses needs. Evaluate those against your organization’s needs, and select the very best fit to be your “quick win” data use case. Develop your data strategy around that data use case.
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Data Analytics Case Study Guide (Updated for 2023)
What are data analytics case study interviews.
When you’re trying to land a data analyst job, the last thing to stand in your way is the data analytics case study interview.
One reason they’re so challenging is that case studies don’t typically have a right or wrong answer.
Instead, case study interviews require you to come up with a hypothesis for an analytics question and then produce data to support or validate your hypothesis. In other words, it’s not just about your technical skills; you’re also being tested on creative problem-solving and your ability to communicate with stakeholders.
This article provides an overview of how to answer data analytics case study interview questions. You can find an in-depth course in the data analytics learning path .
How to Solve Data Analytics Case Questions
Check out our video below on How to solve a Data Analytics case study problem:
With data analyst case questions, you will need to answer two key questions:
- What metrics should I propose?
- How do I write a SQL query to get the metrics I need?
In short, to ace a data analytics case interview, you not only need to brush up on case questions, but you also should be adept at writing all types of SQL queries and have strong data sense.
These questions are especially challenging to answer if you don’t have a framework or know how to answer them. To help you prepare, we created this step-by-step guide to answering data analytics case questions.
We show you how to use a framework to answer case questions, provide example analytics questions, and help you understand the difference between analytics case studies and product metrics case studies .
Data Analytics Cases vs Product Metrics Questions
Product case questions sometimes get lumped in with data analytics cases.
Ultimately, the type of case question you are asked will depend on the role. For example, product analysts will likely face more product-oriented questions.
Product metrics cases tend to focus on a hypothetical situation. You might be asked to:
Investigate Metrics - One of the most common types will ask you to investigate a metric, usually one that’s going up or down. For example, “Why are Facebook friend requests falling by 10 percent?”
Measure Product/Feature Success - A lot of analytics cases revolve around the measurement of product success and feature changes. For example, “We want to add X feature to product Y. What metrics would you track to make sure that’s a good idea?”
With product data cases, the key difference is that you may or may not be required to write the SQL query to find the metric.
Instead, these interviews are more theoretical and are designed to assess your product sense and ability to think about analytics problems from a product perspective. Product metrics questions may also show up in the data analyst interview , but likely only for product data analyst roles.
Data Analytics Case Study Question: Sample Solution
Let’s start with an example data analytics case question :
You’re given a table that represents search results from searches on Facebook. The query column is the search term, the position column represents each position the search result came in, and the rating column represents the human rating from 1 to 5, where 5 is high relevance, and 1 is low relevance.
Each row in the search_events table represents a single search, with the has_clicked column representing if a user clicked on a result or not. We have a hypothesis that the CTR is dependent on the search result rating.
Write a query to return data to support or disprove this hypothesis.
Step 1: With Data Analytics Case Studies, Start by Making Assumptions
Hint: Start by making assumptions and thinking out loud. With this question, focus on coming up with a metric to support the hypothesis. If the question is unclear or if you think you need more information, be sure to ask.
Answer. The hypothesis is that CTR is dependent on search result rating. Therefore, we want to focus on the CTR metric, and we can assume:
- If CTR is high when search result ratings are high, and CTR is low when the search result ratings are low, then the hypothesis is correct.
- If CTR is low when the search ratings are high, or there is no proven correlation between the two, then our hypothesis is not proven.
Step 2: Provide a Solution for the Case Question
Hint: Walk the interviewer through your reasoning. Talking about the decisions you make and why you’re making them shows off your problem-solving approach.
Answer. One way we can investigate the hypothesis is to look at the results split into different search rating buckets. For example, if we measure the CTR for results rated at 1, then those rated at 2, and so on, we can identify if an increase in rating is correlated with an increase in CTR.
First, I’d write a query to get the number of results for each query in each bucket. We want to look at the distribution of results that are less than a rating threshold, which will help us see the relationship between search rating and CTR.
This CTE aggregates the number of results that are less than a certain rating threshold. Later, we can use this to see the percentage that are in each bucket. If we re-join to the search_events table, we can calculate the CTR by then grouping by each bucket.
Step 3: Use Analysis to Backup Your Solution
Hint: Be prepared to justify your solution. Interviewers will follow up with questions about your reasoning, and ask why you make certain assumptions.
Answer. By using the CASE WHEN statement, I calculated each ratings bucket by checking to see if all the search results were less than 1, 2, or 3 by subtracting the total from the number within the bucket and seeing if it equates to 0.
I did that to get away from averages in our bucketing system. Outliers would make it more difficult to measure the effect of bad ratings. For example, if a query had a 1 rating and another had a 5 rating, that would equate to an average of 3. Whereas in my solution, a query with all of the results under 1, 2, or 3 lets us know that it actually has bad ratings.
Product Data Case Question: Sample Solution
In product metrics interviews, you’ll likely be asked about analytics, but the discussion will be more theoretical. You’ll propose a solution to a problem, and supply the metrics you’ll use to investigate or solve it. You may or may not be required to write a SQL query to get those metrics.
We’ll start with an example product metrics case study question :
Let’s say you work for a social media company that has just done a launch in a new city. Looking at weekly metrics, you see a slow decrease in the average number of comments per user from January to March in this city.
The company has been consistently growing new users in the city from January to March.
What are some reasons why the average number of comments per user would be decreasing and what metrics would you look into?
Step 1: Ask Clarifying Questions Specific to the Case
Hint: This question is very vague. It’s all hypothetical, so we don’t know very much about users, what the product is, and how people might be interacting. Be sure you ask questions upfront about the product.
Answer: Before I jump into an answer, I’d like to ask a few questions:
- Who uses this social network? How do they interact with each other?
- Has there been any performance issues that might be causing the problem?
- What are the goals of this particular launch?
- Has there been any changes to the comment features in recent weeks?
For the sake of this example, let’s say we learn that it’s a social network similar to Facebook with a young audience, and the goals of the launch are to grow the user base. Also, there have been no performance issues and the commenting feature hasn’t been changed since launch.
Step 2: Use the Case Question to Make Assumptions
Hint: Look for clues in the question. For example, this case gives you a metric, “average number of comments per user.” Consider if the clue might be helpful in your solution. But be careful, sometimes questions are designed to throw you off track.
Answer: From the question, we can hypothesize a little bit. For example, we know that user count is increasing linearly. That means two things:
- The decreasing comments issue isn’t a result of a declining user base.
- The cause isn’t loss of platform.
We can also model out the data to help us get a better picture of the average number of comments per user metric:
- January: 10000 users, 30000 comments, 3 comments/user
- February: 20000 users, 50000 comments, 2.5 comments/user
- March: 30000 users, 60000 comments, 2 comments/user
One thing to note: Although this is an interesting metric, I’m not sure if it will help us solve this question. For one, average comments per user doesn’t account for churn. We might assume that during the three-month period users are churning off the platform. Let’s say the churn rate is 25% in January, 20% in February and 15% in March.
Step 3: Make a Hypothesis About the Data
Hint: Don’t worry too much about making a correct hypothesis. Instead, interviewers want to get a sense of your product initiation and that you’re on the right track. Also, be prepared to measure your hypothesis.
Answer. I would say that average comments per user isn’t a great metric to use, because it doesn’t reveal insights into what’s really causing this issue.
That’s because it doesn’t account for active users, which are the users who are actually commenting. A better metric to investigate would be retained users and monthly active users.
What I suspect is causing the issue is that active users are commenting frequently and are responsible for the increase in comments month-to-month. New users, on the other hand, aren’t as engaged and aren’t commenting as often.
Step 4: Provide Metrics and Data Analysis
Hint: Within your solution, include key metrics that you’d like to investigate that will help you measure success.
Answer: I’d say there are a few ways we could investigate the cause of this problem, but the one I’d be most interested in would be the engagement of monthly active users.
If the growth in comments is coming from active users, that would help us understand how we’re doing at retaining users. Plus, it will also show if new users are less engaged and commenting less frequently.
One way that we could dig into this would be to segment users by their onboarding date, which would help us to visualize engagement and see how engaged some of our longest-retained users are.
If engagement of new users is the issue, that will give us some options in terms of strategies for addressing the problem. For example, we could test new onboarding or commenting features designed to generate engagement.
Step 5: Propose a Solution for the Case Question
Hint: In the majority of cases, your initial assumptions might be incorrect, or the interviewer might throw you a curveball. Be prepared to make new hypotheses or discuss the pitfalls of your analysis.
Answer. If the cause wasn’t due to a lack of engagement among new users, then I’d want to investigate active users. One potential cause would be active users commenting less. In that case, we’d know that our earliest users were churning out, and that engagement among new users was potentially growing.
Again, I think we’d want to focus on user engagement since the onboarding date. That would help us understand if we were seeing higher levels of churn among active users, and we could start to identify some solutions there.
Tip: Use a Framework to Solve Data Analytics Case Questions
Analytics case questions can be challenging, but they’re much more challenging if you don’t use a framework. Without a framework, it’s easier to get lost in your answer, to get stuck, and really lose the confidence of your interviewer. Find helpful frameworks for data analytics questions in our data analytics learning path and our product metrics learning path .
Once you have the framework down, what’s the best way to practice? Mock interviews with our coaches are very effective, as you’ll get feedback and helpful tips as you answer. You can also learn a lot by practicing P2P mock interviews with other Interview Query students. No data analytics background? Check out how to become a data analyst without a degree .
Finally, if you’re looking for sample data analytics case questions and other types of interview questions, see our guide on the top data analyst interview questions .
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- What Is a Case Study? | Definition, Examples & Methods
What Is a Case Study? | Definition, Examples & Methods
Published on May 8, 2019 by Shona McCombes . Revised on June 22, 2023.
A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.
A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .
Table of contents
When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.
A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.
Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.
You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.
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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:
- Provide new or unexpected insights into the subject
- Challenge or complicate existing assumptions and theories
- Propose practical courses of action to resolve a problem
- Open up new directions for future research
TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.
Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.
Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.
However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.
Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.
While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:
- Exemplify a theory by showing how it explains the case under investigation
- Expand on a theory by uncovering new concepts and ideas that need to be incorporated
- Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions
To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.
There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.
Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.
The aim is to gain as thorough an understanding as possible of the case and its context.
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In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.
How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .
Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).
In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
- Normal distribution
- Degrees of freedom
- Null hypothesis
- Discourse analysis
- Control groups
- Mixed methods research
- Non-probability sampling
- Quantitative research
- Ecological validity
- Rosenthal effect
- Implicit bias
- Cognitive bias
- Selection bias
- Negativity bias
- Status quo bias
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Case Study Research in Software Engineering: Guidelines and Examples by Per Runeson, Martin Höst, Austen Rainer, Björn Regnell
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DATA ANALYSIS AND INTERPRETATION
Once data has been collected the focus shifts to analysis of data. It can be said that in this phase, data is used to understand what actually has happened in the studied case, and where the researcher understands the details of the case and seeks patterns in the data. This means that there inevitably is some analysis going on also in the data collection phase where the data is studied, and for example when data from an interview is transcribed. The understandings in the earlier phases are of course also valid and important, but this chapter is more focusing on the separate phase that starts after the data has been collected.
Data analysis is conducted differently for quantitative and qualitative data. Sections 5.2 – 5.5 describe how to analyze qualitative data and how to assess the validity of this type of analysis. In Section 5.6 , a short introduction to quantitative analysis methods is given. Since quantitative analysis is covered extensively in textbooks on statistical analysis, and case study research to a large extent relies on qualitative data, this section is kept short.
5.2 ANALYSIS OF DATA IN FLEXIBLE RESEARCH
As case study research is a flexible research method, qualitative data analysis methods are commonly used . The basic objective of the analysis is, as in any other analysis, to derive conclusions from the data, keeping a clear chain of evidence. The chain of evidence means that a reader ...
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5 Data Analytics Projects for Beginners
Build a job-ready portfolio with these five beginner-friendly data analysis projects.
If you’re getting ready to launch a new career as a data analyst , chances are you’ve encountered an age-old dilemma. Job listings ask for experience, but how do you get experience if you’re looking for your first data analyst job?
This is where your portfolio comes in. The projects you include in your portfolio demonstrate your skills and experience—even if it’s not from a previous data analytics job—to hiring managers and interviewers. Populating your portfolio with the right projects can go a long way toward building confidence that you’re the right person for the job, even without previous work experience.
In this article, we’ll discuss five types of projects you should include in your data analytics portfolio , especially if you’re just starting out. You’ll see some examples of how these projects are presented in real portfolios, and find a list of public data sets you can use to start completing projects.
Tip: When you’re just starting out, think in terms of “mini projects.” A portfolio project doesn’t need to feature a complete analysis end-to-end. Instead, complete smaller projects based on individual data analytics skills or steps in the data analysis process .
Data analysis project ideas
As an aspiring data analyst, you’ll want to demonstrate a few key skills in your portfolio. These data analytics project ideas reflect the tasks often fundamental to many data analyst roles.
1. Web scraping
While you’ll find no shortage of excellent (and free) public data sets on the internet, you might want to show prospective employers that you’re able to find and scrape your own data as well. Plus, knowing how to scrape web data means you can find and use data sets that match your interests, regardless of whether or not they’ve already been compiled.
If you know some Python , you can use tools like Beautiful Soup or Scrapy to crawl the web for interesting data. If you don’t know how to code, don’t worry. You’ll also find several tools that automate the process (many offer a free trial), like Octoparse or ParseHub.
If you’re unsure where to start, here are some websites with interesting data options to inspire your project:
Tip: Anytime you’re scraping data from the internet, remember to respect and abide by each website’s terms of service. Limit your scraping activities so as not to overwhelm a company’s servers, and always cite your sources when you present your data findings in your portfolio.
Example web scraping project: Todd W. Schneider of Wedding Crunchers scraped some 60,000 New York Times wedding announcements published from 1981 to 2016 to measure the frequency of specific phrases.
2. Data cleaning
A significant part of your role as a data analyst is cleaning data to make it ready to analyze. Data cleaning (also called data scrubbing) is the process of removing incorrect and duplicate data, managing any holes in the data, and making sure the formatting of data is consistent.
As you look for a data set to practice cleaning, look for one that includes multiple files gathered from multiple sources without much curation. Some sites where you can find “dirty” data sets to work with include:
Example data cleaning project: This Medium article outlines how data analyst Raahim Khan cleaned a set of daily-updated statistics on trending YouTube videos.
3. Exploratory data analysis (EDA)
Data analysis is all about answering questions with data. Exploratory data analysis, or EDA for short, helps you explore what questions to ask. This could be done separate from or in conjunction with data cleaning. Either way, you’ll want to accomplish the following during these early investigations.
Ask lots of questions about the data.
Discover the underlying structure of the data.
Look for trends, patterns, and anomalies in the data.
Test hypotheses and validate assumptions about the data.
Think about what problems you could potentially solve with the data.
Example exploratory data analysis project: This data analyst took an existing dataset on American universities in 2013 from Kaggle and used it to explore what makes students prefer one university over another .
10 free public datasets for EDA
An EDA project is an excellent time to take advantage of the wealth of public datasets available online. Here are 10 fun and free datasets to get you started in your explorations.
1. National Centers for Environmental Information : Dig into the world’s largest provider of weather and climate data.
2. World Happiness Report 2021 : What makes the world’s happiest countries so happy?
3. NASA : If you’re interested in space and earth science, see what you can find among the tens of thousands of public datasets made available by NASA.
4. US Census : Learn more about the people and economy of the United States with the latest census data from 2020.
5. FBI Crime Data Explorer (CDE) : Explore crime data collected by more than 18,000 law enforcement agencies.
6. World Health Organization COVID-19 Dashboard : Track the latest coronavirus numbers by country or WHO region.
7. Latest Netflix Data : This Kaggle dataset (updated in April 2021) includes movie data broken down into 26 attributes.
8. Google Books Ngram : Download the raw data from the Google Books Ngram to explore phrase trends in books published from 1960 to 2015.
9. NYC Open Data : Discover New York City through its many publicly available datasets on topics like the Central Park squirrel population to motor vehicle collisions.
10. Yelp Open Dataset : See what you can find while exploring this collection of Yelp user reviews, check ins, and business attributes.
4. Sentiment analysis
Sentiment analysis, typically performed on textual data, is a technique in natural language processing (NLP) for determining whether data is neutral, positive, or negative. It may also be used to detect a particular emotion based on a list of words and their corresponding emotions (known as a lexicon).
This type of analysis works well with public review sites and social media platforms, where people are likely to offer public opinions on various subjects.
To get started exploring what people feel about a certain topic, you can start with sites like:
Amazon (product reviews)
Rotten Tomato (movie reviews)
Example sentiment analysis project: This blog post on Towards Data Science explores the use of linguistic markers in Tweets to help diagnose depression.
5. Data visualization
Humans are visual creatures. This makes data visualization a powerful tool for transforming data into a compelling story to encourage action. Great visualizations are not only fun to create, they also have the power to make your portfolio look beautiful.
Example data visualization project: Data analyst Hannah Yan Han visualizes the skill level required for 60 different sports to find out which is toughest.
Five free data visualization tools
You don’t need to pay for advanced visualization software to start creating stellar visuals either. These are just a few of the free visualization tools you can use to start telling a story with data:
1. Tableau Public: Tableau ranks among the most popular visualization tools. Use the free version to transform spreadsheets or files into interactive visualizations ( here are some examples from April 2021).
3. Datawrapper: Copy and paste your data from a spreadsheet or upload a CSV file to generate charts, maps, or tables—no coding required. The free version allows you to create unlimited visualizations to export as PNG files.
5. RAW Graphs: This open source web app makes it easy to turn spreadsheets or CSV files into a range of chart types that might otherwise be difficult to produce. The app even provides sample data sets for you to experiment with.
Bonus: End-to-end project
There’s nothing wrong with populating your portfolio with mini projects highlighting individual skills. But if you’ve scraped the web for your own data, you might also consider using that same data to complete an end-to-end project. To do this, take the data you scraped and apply the main steps of data analysis to it—clean, analyze, and interpret.
This can show a potential employer that you not only have the essential skills of a data analyst but that you know how they fit together.
Three data analysis projects you can complete today
There’s a lot of data out there, and a lot you can do with it. Trying to figure out where to start can be overwhelming. If you need a little direction for your next project, consider one of these data analysis Guided Projects on Coursera that you can complete in under two hours. Each includes split-screen video instruction, and you don’t have to download or own any special software.
1. Exploratory Data Analysis with Python and Pandas : Apply EDA techniques to any table of data using Python.
2. Twitter Sentiment Analysis Tutorial : Clean thousands of tweets and use them to predict whether a customer is happy or not.
3. COVID19 Data Visualization Using Python : Visualize the global spread of COVID-19 using Python, Plotly, and a real data set.
Next steps: Get started in data analysis
Another great way to build some portfolio-ready projects is through a project-based online course. By completing the Google Data Analytics Professional Certificate on Coursera, you can complete hands-on projects and a case study to share with potential employers.
Frequently asked questions (FAQ)
What are some books on data analytics for beginners .
There are many great books for those just starting out in data analytics. The following three books, in particular, offer accessible introductions to key aspects of field: Data Analytics Made Accessible by Dr. Anil Maheshwari Numsense! Data Science for the Layman: No Math Added by Annalyn Ng and Kenneth Soo Python for Everybody: Exploring Data in Python 3 by Dr. Charles Russell Severance
To supplement their reading, beginners may also consider taking the online Python for Everybody Specialization offered by the University of Michigan and taught by Dr. Severance himself.
What is data visualization?
Data visualization is the process of graphically representing data through visual means. Common forms of data visualization include the use of graphs, charts, and diagrams to visually represent otherwise abstract data sets. Today, data visualization is considered a key skill in the world of data analytics.
What skills do data analysts need?
Beginning data analysts should make sure they have a solid technical understanding of Structured Query Language (SQL), Microsoft Excel, and either R or Python. Additionally, they should be able to think critically, present confidently, and know how to tell their data’s story visually. Read more about these and other key data analyst skills .
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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ORIGINAL RESEARCH article
Spatiotemporal characteristic analysis of pm 2.5 in central china and modeling of driving factors based on mgwr: a case study of henan province.
- 1 Zhengzhou University of Light Industry, China
- 2 School of Geographic Sciences, Xinyang Normal University, China
- 3 Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources, China
The final, formatted version of the article will be published soon.
Since the start of the twenty-first century, China's economy has grown at a high or moderate rate, and air pollution has become increasingly severe. The study was conducted using data from remote sensing observations between 1998 and 2019, employing the standard deviation ellipse model and spatial autocorrelation analysis, to explore the spatiotemporal distribution characteristics of PM2.5 in Henan Province. Additionally, a multiscale geographically weighted regression model (MGWR) was applied to explore the impact of 12 driving factors (e.g., mean surface temperature and CO2 emissions) on PM2.5 concentration. The research revealed that (1) Over a period of 22 years, the yearly mean PM2.5 concentrations in Henan Province demonstrated a trend resembling the shape of the letter "M.", and the general trend observed in Henan Province demonstrated that the spatial center of gravity of PM2.5 concentrations shifted towards the north. (2) Distinct spatial clustering patterns of PM2.5 were observed in Henan Province, with the northern region showing a primary concentration of spatial hot spots, while the western and southern areas were predominantly characterized as cold spots. (3) MGWR is more effective than GWR in unveiling the spatial heterogeneity of influencing factors at various scales, thereby making it a more appropriate approach for investigating the driving mechanisms behind PM2.5 concentration. (4) The results acquired from the MGWR model indicate that there are varying degrees of spatial heterogeneity in the effects of various factors on PM2.5 concentration.
Keywords: PM2.5, Spatio-temporal variation, MGWR, Central China, Air Quality
Received: 16 Sep 2023; Accepted: 14 Nov 2023.
Copyright: © 2023 Wang, Zhang, Niu and Zheng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Mx. Jiqiang Niu, School of Geographic Sciences, Xinyang Normal University, Xinyang, 464031, China
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Safety of cinacalcet in children and adolescents with chronic kidney disease-mineral bone disorder: systematic review and proportional meta-analysis of case series
- Nephrology - Review
- Open access
- Published: 15 November 2023
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- Soraya Mayumi Sasaoka Zamoner ORCID: orcid.org/0000-0001-9550-3265 1 , 3 ,
- Henrique Mochida Takase 1 , 3 ,
- Marcia Camegaçava Riyuzo 1 , 3 ,
- Jacqueline Costa Teixeira Caramori 2 , 3 &
- Luis Gustavo Modelli de Andrade 2 , 3
Cite this article
Mineral and bone disease in children with chronic kidney disease can cause abnormalities in calcium, phosphorus, parathyroid hormone, and vitamin D and when left untreated can result in impaired growth, bone deformities, fractures, and vascular calcification. Cinacalcet is a calcimimetic widely used as a therapy to reduce parathyroid hormone levels in the adult population, with hypocalcemia among its side effects. The analysis of safety in the pediatric population is questioned due to the scarcity of randomized clinical trials in this group.
To assess the onset of symptomatic hypocalcemia or other adverse events (serious or non-serious) with the use of cinacalcet in children and adolescents with mineral and bone disorder in chronic kidney disease.
Data sources and study eligibility criteria
The bibliographic search identified 2699 references from 1927 to August/2023 (57 LILACS, 44 Web of Science, 686 PubMed, 131 Cochrane, 1246 Scopus, 535 Embase). Four references were added from the bibliography of articles found and 12 references from the gray literature (Clinical Trials). Of the 77 studies analyzed in full, 68 were excluded because they did not meet the following criteria: population, types of studies, medication, publication types and 1 article that did not present results (gray literature).
Participants and interventions
There were 149 patients aged 0–18 years old with Chronic Kidney Disease and mineral bone disorder who received cinacalcet.
Study appraisal and synthesis methods
Nine eligible studies were examined for study type, size, intervention, and reported outcomes.
There was an incidence of 0.2% of fatal adverse events and 16% of serious adverse events ( p < 0.01 and I 2 = 69%), in addition to 10.7% of hypocalcemia, totaling 45.7% of total adverse events.
There was a bias in demographic information and clinical characteristics of patients in about 50% of the studies and the majority of the studies were case series.
Conclusions and implications of key findings
If used in the pediatric population, the calcimimetic cinacalcet should be carefully monitored for serum calcium levels and attention to possible adverse events, especially in children under 50 months.
Systematic review registration number (PROSPERO register)
Avoid common mistakes on your manuscript.
Mineral and bone disorder (MBD) is a common complication in children with chronic kidney disease (CKD), characterized by abnormalities of calcium, phosphorus, parathyroid hormone (PTH), vitamin D, fibroblast growth factor (FGF) 23, vascular calcifications, impairment of linear growth, changes in bone histology and bone deformities [ 1 , 2 , 3 ]. The current guideline KDIGO 2017 for the treatment of adults with CKD-MBD includes approved drugs by the US Food and Drug Administration (FDA) [ 4 ] and European Medicines Agency (EMA) such as sterols, vitamin D analogs, phosphate binders and calcimimetics. Cinacalcet is an allosteric calcium-sensing receptor (CaSR) modulator that increases the sensitivity of CaSR, especially in the parathyroid glands, to serum calcium, resulting in the suppression of PTH secretion.
In 2017, the EMA approved cinacalcet in children over 3 years of age with CKD-MBD on dialysis who did not achieve control of hyperparathyroidism with traditional therapies. Additionally, in 2020 the European Society of Pediatric Nephrology and the ERA-EDTA Group [ 5 ] published a document with 22 positions regarding the use of cinacalcet in children on dialysis. However, the FDA [ 4 ], in a recent document of 2020, has not approved the drug in the same population. The KDIGO 2017 guidelines also do not recommend the drug in children because of the scarcity of information on the safety and efficacy of cinacalcet in this population.
The aim of study was to evaluate the onset of symptomatic hypocalcemia or other adverse events (severe and non-serious) with the use of cinacalcet in children and adolescents with CKD-MBD.
Search strategy and study assessment
A search was performed in Pubmed, Embase, Lilacs, Scopus, Web of Science and Cochrane from 1927 to August/2023 without language restriction. Keywords, “MeSH”, “Emtree terms”, DeCS and uncontrolled vocabulary were used in order to select all articles related to the use of cinacalcet. The literature search identified 2699 published articles and 16 records were added from gray literature and other references. Duplicated articles were removed, and 1548 records were excluded based on the Title or Abstract. Two independent reviewers analyzed full-text articles ( n = 77) and excluded ( n = 68) articles who did not meet eligibility criteria (Fig. 1 ). Finally, 9 studies were included for qualitative and quantitative synthesis (Fig. 1 ).
Selection of eligible papers and reasons for exclusion
Metafor package of software R [ 6 ] version 4.0.2 was used. A proportion meta-analysis technique was performed using the inverse variance method and the Random effects model to estimate the effect. Heterogeneity was quantified by the DerSimonian-Laird Estimator for τ 2 . Outcomes of interest were treated as dichotomous variables, with their respective 95% confidence intervals (95% CI).
We included five case series [ 7 , 8 , 9 , 10 , 11 ], one published RCT [ 12 ], and three non-published RCTs [ 13 , 14 , 15 ] had the data extracted from the Clinical Trials [ 16 ], totaling 149 patients who received cinacalcet. The control group was excluded from the RCTs due to the nature of the work. Patient´s mean age ranged from 35.9 to 204 months. Only two studies included non-dialytic patients [ 7 , 9 ]. Underlying disease was not reported in the RCTs, and in the case series it was, for the most part, secondary to CAKUT (Congenital Anomalies of the Kidney and Urinary Tract), ranging from 33.33 [ 7 ] to 83% [ 10 ]. Mean pre-cinacalcet PTH ranged from 932 to 1931 pg/ml (Table 1 ).
Risk of bias of included studies
Joanna Briggs Institute Collaboration’s tools [ 17 ] were used to analyze the risk of bias. Four studies showed severe bias in the presentation of demographic data and clinical information of patients, and two studies showed severe bias in the presentation of outcomes during follow-up, causing bias in the demographic information and clinical characteristics of the patients in 50% of the studies (Fig. 2 ).
Methodological quality: authors’ assessment of the methodological quality of each item, presented as a percentage of all included studies
Results of individual studies
The studies showed important variations in terms of doses (0.2–0.63 mg/kg/day) and duration of therapy (1–24 months) (Table 2 ). One of the studies did not report the onset of serious or fatal adverse events, 4 reported serious adverse events in 16% of patients to 52.97% and only 2 studies had fatal adverse events as described on Table 2 . The serious adverse events were described on Table 3 .
Summary of results
We found an incidence of 0.2% fatal adverse event [95% CI 0–3.1%; I 2 = 0%, p = 0.96] (Fig. 3 a), 16% of serious adverse events [95% CI 4.1–32%; I 2 = 69%, p value < 0.01] (Fig. 3 b), 10.7% of hypocalcemia [95% CI 2.8–21.6%; I 2 = 58%; p value = 0.01] (Fig. 3 c), totaling 45.7% of total adverse events [95% CI 16.5–76.4%; I 2 92%; p value < 0.01] (Fig. 3 d).
Forest plot (random effect model). a Fatal adverse event. b Serious adverse event. c Hypocalcemia. d Total adverse events
A meta-regression was performed considering serious adverse event and age in months (Fig. 4 ). The older the patient, the lower the percentage of serious adverse events (Y-axis) occurred, without reaching significance ( p = 0.38).
Meta-regression. Age (months) versus serious adverse event (Y-axis). The size of the circle refers to the importance of the study
Cinacalcet is a medication widely used to treat BMD in adult patients with CKD. However, safety analyses of cinacalcet in pediatric patients are scarce, limiting its use in this group. In our review, we found an incidence of 0.2% of fatal events reported in two studies and 16% of serious adverse events ( p < 0.01). Serious adverse events with the highest incidence were hypertension, diarrhea, ileus, and dialysis catheter-related events (Table 3 ). Three studies reported no serious adverse events but described treatment discontinuation due to persistent hypocalcemia [ 10 ], generalized tonic–clonic seizure [ 11 ], and six deaths attributed to CKD [ 7 ]. The incidence of hypocalcemia and total events were 10.7% ( p 0.01) and 45.7%, respectively.
A systematic review conducted by Ballinger et al. [ 18 ] showed an increased risk of hypocalcemia in adults on dialysis who received cinacalcet (12 studies, 6415 participants, RR 6.98, 95% CI 5.10–9.53; I 2 = 0%).
In the EVOLVE trial [ 19 ] hypocalcemia was found in 12% and 1.7% in the cinacalcet and placebo groups, respectively. The percentage of treatment-related serious adverse events was similar between the groups (3.6% and 2.3%, respectively).
Four RCTs [ 20 , 21 , 22 , 23 ] reported no serious adverse events and an average percentage of reduction in calcium values of 4% [ 23 ], 6.8% [ 20 ] and 4.7% [ 22 ]. Most adverse events were considered mild to moderate in these studies and transient episodes of hypocalcemia in patients who received cincacalcet were reported in one study [ 21 ].
The incidence of hypocalcemia found in the present study was similar to that reported in the adult population [ 19 , 20 , 21 , 22 , 23 ]; however, serious adverse events were five times higher. Additionally, two deaths were reported in the pediatric population, but it was not possible to rule out cinacalcet as a causal factor [ 12 , 15 , 24 , 25 ] Two studies that reported high rates of adverse events [ 14 , 15 ] were not published but had data retrieved from the Clinical Trials platform [ 16 ].
The main side effects of cinacalcet are the gastrointestinal intolerance and the potential incidence of symptomatic hypocalcemia, so caution should be exercised in patients with risk factors to present a interval QT prolongation or patients with epilepsy. A certain degree of asymptomatic hypocalcemia induced by calcimimetics is considered tolerable and could even be beneficial. In addition, with a relatively low calcium, FGF23 decreases, as long as phosphate is controlled [ 26 , 27 ].
Warady et al. [ 24 ] performed a recent comprehensive review. Cinacalcet pharmacokinetics data are similar between pediatric and adult subjects with CKD and secondary HPT receiving dialysis and between pediatric age groups (28 days to < 6 years and 6 years to < 18 years). The most common adverse events (occurring in > 10% of subjects) were hypocalcemia (22.8%), vomiting (16.5%), nausea (15.2%), systemic hypertension (11.4%), pyrexia (10.1%), and muscle spasms (10.1%).
Calcimimetics may be considered with extreme caution in infants who have persistent and severe hyperparathyroidism in the presence of high or high-normal calcium levels, despite optimized conventional management, including active vitamin D, as an alternative to parathyroidectomy in individual cases after informed consent of the family, provided a close follow-up of ionized Ca and Ca levels and the subsequent risk of hypocalcemia [ 24 ]. A closer monitorization may be necessary in patients under treatment with calcimimetics, especially during the period of dose adjustment [ 26 ].
We found high rates of serious adverse events, but the main serious events reported were hypertension, diarrhea, and dialysis catheter-related events. In addition, the meta-regression (Fig. 4 ) indicates that the younger the age, the higher the incidence of adverse events. Despite not reaching statistical significance, possibly due to the reduced number of cases, the incidence of serious adverse events can reach 80% at 50 months (Fig. 4 ).
This study is limited by the number of participants and studies nature (case series). However, this is the first systematic review with a proportional meta-analysis of case series on the safety of cinacalcet use in children and adolescents with hyperparathyroidism secondary to CKD. Additionally, we expanded the search to gray literature sources to include unpublished works that had data retrieved.
If used in the pediatric population, cinacalcet should have careful monitoring of serum calcium levels and attention to possible adverse events, especially in children younger than 50 months.
The data used to support the results and conclusion of this manuscript were presented by the authors.
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The authors thank all patients, team members and investigators who participated in the study.
This article was not supported by any source and represents an original effort by the authors.
Authors and affiliations.
Botucatu School of Medicine, Pediatrics Department - Pediatric Nephrology, University São Paulo State-UNESP, District of Rubiao Junior, Botucatu, SP, 18618-970, Brazil
Soraya Mayumi Sasaoka Zamoner, Henrique Mochida Takase & Marcia Camegaçava Riyuzo
Botucatu School of Medicine, Internal Medicine Department – Nephrology, University São Paulo State-UNESP, District of Rubiao Junior, Botucatu, SP, 18618-970, Brazil
Jacqueline Costa Teixeira Caramori & Luis Gustavo Modelli de Andrade
Clinics Hospital - Botucatu School of Medicine, District of Rubiao Junior, Botucatu, SP, 18618-970, Brazil
Soraya Mayumi Sasaoka Zamoner, Henrique Mochida Takase, Marcia Camegaçava Riyuzo, Jacqueline Costa Teixeira Caramori & Luis Gustavo Modelli de Andrade
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The authors contributed equally to the writing of this article. All authors have participated in the drafting of the manuscript and have read and approved its final version.
Correspondence to Soraya Mayumi Sasaoka Zamoner .
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Zamoner, S.M.S., Takase, H.M., Riyuzo, M.C. et al. Safety of cinacalcet in children and adolescents with chronic kidney disease-mineral bone disorder: systematic review and proportional meta-analysis of case series. Int Urol Nephrol (2023). https://doi.org/10.1007/s11255-023-03844-2
Received : 18 June 2023
Accepted : 08 October 2023
Published : 15 November 2023
DOI : https://doi.org/10.1007/s11255-023-03844-2
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