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data science case study book

The Handbook of Data Science and AI: Generate Value from Data with Machine Learning and Data Analytics

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Data Science, Big Data, and Artificial Intelligence are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them. Featuring: 

- A comprehensive overview of the various fields of application of data science

- Case studies from practice to make the described concepts tangible

- Practical examples to help you carry out simple data analysis projects

The book approaches the topic of data science from several sides. Crucially, it will show you how to build data platforms and apply data science tools and methods. Along the way, it will help you understand - and explain to various stakeholders - how to generate value from these techniques, such as applying data science to help organizations make faster decisions, reduce costs, and open up new markets. Furthermore, it will bring fundamental concepts related to data science to life, including statistics, mathematics, and legal considerations. Finally, the book outlines practical case studies that illustrate how knowledge generated from data is changing various industries over the long term. 

Contains these current issues:

- Mathematics basics: Mathematics for Machine Learning to help you understand and utilize various ML algorithms.

- Machine Learning: From statistical to neural and from Transformers and GPT-3 to AutoML, we introduce common frameworks for applying ML in practice

- Natural Language Processing: Tools and techniques for gaining insights from text data and developing language technologies

- Computer vision: How can we gain insights from images and videos with data science?

- Modeling and Simulation: Model the behavior of complex systems, such as the spread of COVID-19, and do a What-If analysis covering different scenarios.

- ML and AI in production: How to turn experimentation into a working data science product?

- Presenting your results: Essential presentation techniques for data scientists

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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications

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About the authors

Wolfgang weidinger.

Wolfgang Weidinger is a Data Scientist and author and has worked in a wide variety of industries and sectors such as start-ups, finance, consulting, wholesale and insurance. There he led Data Science teams and drove their role as spearheads in digital and data-driven transformation.

He is President of the Vienna Data Science Group (www.vdsg.at), a non-profit association of and for Data Scientists. This brings together both research and practice across a wide range of industries.

Wolfgang is particularly interested in the societal impact of Data Science and AI, as well as the educational side of this topic.

Danko Nikolić

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Top 8 Data Science Case Studies for Data Science Enthusiasts

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data science case study book

Data science has become popular in the last few years due to its successful application in making business decisions. Data scientists have been using data science techniques to solve challenging real-world issues in healthcare, agriculture, manufacturing, automotive, and many more. For this purpose, a data enthusiast needs to stay updated with the latest technological advancements in AI. An excellent way to achieve this is through reading industry case studies. Check out  Knowledgehut  Data Science  With  Python course syllabus  to start your data science journey.  

Let’s discuss some case studies that contain detailed and systematic data analysis of people, objects, or entities focusing on multiple factors present in the dataset. Aspiring and practising data scientists can motivate themselves to learn more about the sector, an alternative way of thinking, or methods to improve their organization based on comparable experiences. Almost every industry uses data science in some way. You can learn more about  data science fundamentals  in this  data science course content . Data scientists may use it to spot fraudulent conduct in insurance claims. Automotive data scientists may use it to improve self-driving cars. In contrast, e-commerce data scientists can use it to add more personalization for their consumers—the possibilities are unlimited and unexplored.  

We will take a look at the top eight  data science case studies  in this article so you can understand how businesses from many sectors have benefitted from data science to boost productivity, revenues, and more. Read on to explore more, or use the following links to go straight to the case study of your choice.  

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data science case study book

Hospitality  

Healthcare  

Covid 19  

Ecommerce  

Supply chain management  

Meteorology  

Entertainment Industry  

Banking and Finance  

8 Data Science Case Studies  

1. data science in hospitality industry.

In the hospitality sector, data analytics assists hotels in better pricing strategies, customer analysis, brand marketing , tracking market trends, and many more.

Airbnb focuses on growth by analyzing customer voice using data science. 

A famous example in this sector is the unicorn '' Airbnb '', a startup that focussed on data science early to grow and adapt to the market faster. This company witnessed a 43000 percent hypergrowth in as little as five years using data science. They included data science techniques to process the data, translate this data for better understanding the voice of the customer, and use the insights for decision making. They also scaled the approach to cover all aspects of the organization. Airbnb uses statistics to analyze and aggregate individual experiences to establish trends throughout the community. These analyzed trends using data science techniques impact their business choices while helping them grow further.  

Travel industry and data science

Predictive analytics benefits many parameters in the travel industry. These companies can use recommendation engines with data science to achieve higher personalization and improved user interactions. They can study and cross-sell products by recommending relevant products to drive sales and increase revenue. Data science is also employed in analyzing social media posts for sentiment analysis, bringing invaluable travel-related insights. Whether these views are positive, negative, or neutral can help these agencies understand the user demographics, the expected experiences by their target audiences, and so on. These insights are essential for developing aggressive pricing strategies to draw customers and provide better customization to customers in the travel packages and allied services. Travel agencies like Expedia and Booking.com use predictive analytics to create personalized recommendations, product development, and effective marketing of their products. Not just travel agencies but airlines also benefit from the same approach. Airlines frequently face losses due to flight cancellations, disruptions, and delays. Data science helps them identify patterns and predict possible bottlenecks, thereby effectively mitigating the losses and improving the overall customer traveling experience.  

How Qantas uses predictive analytics to mitigate losses  

Qantas , one of Australia's largest airlines, leverages data science to reduce losses caused due to flight delays, disruptions, and cancellations. They also use it to provide a better traveling experience for their customers by reducing the number and length of delays caused due to huge air traffic, weather conditions, or difficulties arising in operations. Back in 2016, when heavy storms badly struck Australia's east coast, only 15 out of 436 Qantas flights were cancelled due to their predictive analytics-based system against their competitor Virgin Australia, which witnessed 70 cancelled flights out of 320.  

2. Data Science in Healthcare

The  Healthcare sector  is immensely benefiting from the advancements in AI. Data science, especially in medical imaging, has been helping healthcare professionals come up with better diagnoses and effective treatments for patients. Similarly, several advanced healthcare analytics tools have been developed to generate clinical insights for improving patient care. These tools also assist in defining personalized medications for patients reducing operating costs for clinics and hospitals. Apart from medical imaging or computer vision,  Natural Language Processing (NLP)  is frequently used in the healthcare domain to study the published textual research data.     

Pharmaceutical

Driving innovation with NLP: Novo Nordisk  

Novo Nordisk  uses the Linguamatics NLP platform from internal and external data sources for text mining purposes that include scientific abstracts, patents, grants, news, tech transfer offices from universities worldwide, and more. These NLP queries run across sources for the key therapeutic areas of interest to the Novo Nordisk R&D community. Several NLP algorithms have been developed for the topics of safety, efficacy, randomized controlled trials, patient populations, dosing, and devices. Novo Nordisk employs a data pipeline to capitalize the tools' success on real-world data and uses interactive dashboards and cloud services to visualize this standardized structured information from the queries for exploring commercial effectiveness, market situations, potential, and gaps in the product documentation. Through data science, they are able to automate the process of generating insights, save time and provide better insights for evidence-based decision making.  

How AstraZeneca harnesses data for innovation in medicine  

AstraZeneca  is a globally known biotech company that leverages data using AI technology to discover and deliver newer effective medicines faster. Within their R&D teams, they are using AI to decode the big data to understand better diseases like cancer, respiratory disease, and heart, kidney, and metabolic diseases to be effectively treated. Using data science, they can identify new targets for innovative medications. In 2021, they selected the first two AI-generated drug targets collaborating with BenevolentAI in Chronic Kidney Disease and Idiopathic Pulmonary Fibrosis.   

Data science is also helping AstraZeneca redesign better clinical trials, achieve personalized medication strategies, and innovate the process of developing new medicines. Their Center for Genomics Research uses  data science and AI  to analyze around two million genomes by 2026. Apart from this, they are training their AI systems to check these images for disease and biomarkers for effective medicines for imaging purposes. This approach helps them analyze samples accurately and more effortlessly. Moreover, it can cut the analysis time by around 30%.   

AstraZeneca also utilizes AI and machine learning to optimize the process at different stages and minimize the overall time for the clinical trials by analyzing the clinical trial data. Summing up, they use data science to design smarter clinical trials, develop innovative medicines, improve drug development and patient care strategies, and many more.

Wearable Technology  

Wearable technology is a multi-billion-dollar industry. With an increasing awareness about fitness and nutrition, more individuals now prefer using fitness wearables to track their routines and lifestyle choices.  

Fitness wearables are convenient to use, assist users in tracking their health, and encourage them to lead a healthier lifestyle. The medical devices in this domain are beneficial since they help monitor the patient's condition and communicate in an emergency situation. The regularly used fitness trackers and smartwatches from renowned companies like Garmin, Apple, FitBit, etc., continuously collect physiological data of the individuals wearing them. These wearable providers offer user-friendly dashboards to their customers for analyzing and tracking progress in their fitness journey.

3. Covid 19 and Data Science

In the past two years of the Pandemic, the power of data science has been more evident than ever. Different  pharmaceutical companies  across the globe could synthesize Covid 19 vaccines by analyzing the data to understand the trends and patterns of the outbreak. Data science made it possible to track the virus in real-time, predict patterns, devise effective strategies to fight the Pandemic, and many more.  

How Johnson and Johnson uses data science to fight the Pandemic   

The  data science team  at  Johnson and Johnson  leverages real-time data to track the spread of the virus. They built a global surveillance dashboard (granulated to county level) that helps them track the Pandemic's progress, predict potential hotspots of the virus, and narrow down the likely place where they should test its investigational COVID-19 vaccine candidate. The team works with in-country experts to determine whether official numbers are accurate and find the most valid information about case numbers, hospitalizations, mortality and testing rates, social compliance, and local policies to populate this dashboard. The team also studies the data to build models that help the company identify groups of individuals at risk of getting affected by the virus and explore effective treatments to improve patient outcomes.

4. Data Science in Ecommerce  

In the  e-commerce sector , big data analytics can assist in customer analysis, reduce operational costs, forecast trends for better sales, provide personalized shopping experiences to customers, and many more.  

Amazon uses data science to personalize shopping experiences and improve customer satisfaction.  Amazon  is a globally leading eCommerce platform that offers a wide range of online shopping services. Due to this, Amazon generates a massive amount of data that can be leveraged to understand consumer behavior and generate insights on competitors' strategies. Amazon uses its data to provide recommendations to its users on different products and services. With this approach, Amazon is able to persuade its consumers into buying and making additional sales. This approach works well for Amazon as it earns 35% of the revenue yearly with this technique. Additionally, Amazon collects consumer data for faster order tracking and better deliveries.     

Similarly, Amazon's virtual assistant, Alexa, can converse in different languages; uses speakers and a   camera to interact with the users. Amazon utilizes the audio commands from users to improve Alexa and deliver a better user experience. 

5. Data Science in Supply Chain Management

Predictive analytics and big data are driving innovation in the Supply chain domain. They offer greater visibility into the company operations, reduce costs and overheads, forecasting demands, predictive maintenance, product pricing, minimize supply chain interruptions, route optimization, fleet management , drive better performance, and more.     

Optimizing supply chain with big data analytics: UPS

UPS  is a renowned package delivery and supply chain management company. With thousands of packages being delivered every day, on average, a UPS driver makes about 100 deliveries each business day. On-time and safe package delivery are crucial to UPS's success. Hence, UPS offers an optimized navigation tool ''ORION'' (On-Road Integrated Optimization and Navigation), which uses highly advanced big data processing algorithms. This tool for UPS drivers provides route optimization concerning fuel, distance, and time. UPS utilizes supply chain data analysis in all aspects of its shipping process. Data about packages and deliveries are captured through radars and sensors. The deliveries and routes are optimized using big data systems. Overall, this approach has helped UPS save 1.6 million gallons of gasoline in transportation every year, significantly reducing delivery costs.    

6. Data Science in Meteorology

Weather prediction is an interesting  application of data science . Businesses like aviation, agriculture and farming, construction, consumer goods, sporting events, and many more are dependent on climatic conditions. The success of these businesses is closely tied to the weather, as decisions are made after considering the weather predictions from the meteorological department.   

Besides, weather forecasts are extremely helpful for individuals to manage their allergic conditions. One crucial application of weather forecasting is natural disaster prediction and risk management.  

Weather forecasts begin with a large amount of data collection related to the current environmental conditions (wind speed, temperature, humidity, clouds captured at a specific location and time) using sensors on IoT (Internet of Things) devices and satellite imagery. This gathered data is then analyzed using the understanding of atmospheric processes, and machine learning models are built to make predictions on upcoming weather conditions like rainfall or snow prediction. Although data science cannot help avoid natural calamities like floods, hurricanes, or forest fires. Tracking these natural phenomena well ahead of their arrival is beneficial. Such predictions allow governments sufficient time to take necessary steps and measures to ensure the safety of the population.  

IMD leveraged data science to achieve a record 1.2m evacuation before cyclone ''Fani''   

Most  d ata scientist’s responsibilities  rely on satellite images to make short-term forecasts, decide whether a forecast is correct, and validate models. Machine Learning is also used for pattern matching in this case. It can forecast future weather conditions if it recognizes a past pattern. When employing dependable equipment, sensor data is helpful to produce local forecasts about actual weather models. IMD used satellite pictures to study the low-pressure zones forming off the Odisha coast (India). In April 2019, thirteen days before cyclone ''Fani'' reached the area,  IMD  (India Meteorological Department) warned that a massive storm was underway, and the authorities began preparing for safety measures.  

It was one of the most powerful cyclones to strike India in the recent 20 years, and a record 1.2 million people were evacuated in less than 48 hours, thanks to the power of data science.   

7. Data Science in Entertainment Industry

Due to the Pandemic, demand for OTT (Over-the-top) media platforms has grown significantly. People prefer watching movies and web series or listening to the music of their choice at leisure in the convenience of their homes. This sudden growth in demand has given rise to stiff competition. Every platform now uses data analytics in different capacities to provide better-personalized recommendations to its subscribers and improve user experience.   

How Netflix uses data science to personalize the content and improve recommendations  

Netflix  is an extremely popular internet television platform with streamable content offered in several languages and caters to various audiences. In 2006, when Netflix entered this media streaming market, they were interested in increasing the efficiency of their existing ''Cinematch'' platform by 10% and hence, offered a prize of $1 million to the winning team. This approach was successful as they found a solution developed by the BellKor team at the end of the competition that increased prediction accuracy by 10.06%. Over 200 work hours and an ensemble of 107 algorithms provided this result. These winning algorithms are now a part of the Netflix recommendation system.  

Netflix also employs Ranking Algorithms to generate personalized recommendations of movies and TV Shows appealing to its users.   

Spotify uses big data to deliver a rich user experience for online music streaming  

Personalized online music streaming is another area where data science is being used.  Spotify  is a well-known on-demand music service provider launched in 2008, which effectively leveraged big data to create personalized experiences for each user. It is a huge platform with more than 24 million subscribers and hosts a database of nearly 20million songs; they use the big data to offer a rich experience to its users. Spotify uses this big data and various algorithms to train machine learning models to provide personalized content. Spotify offers a "Discover Weekly" feature that generates a personalized playlist of fresh unheard songs matching the user's taste every week. Using the Spotify "Wrapped" feature, users get an overview of their most favorite or frequently listened songs during the entire year in December. Spotify also leverages the data to run targeted ads to grow its business. Thus, Spotify utilizes the user data, which is big data and some external data, to deliver a high-quality user experience.  

8. Data Science in Banking and Finance

Data science is extremely valuable in the Banking and  Finance industry . Several high priority aspects of Banking and Finance like credit risk modeling (possibility of repayment of a loan), fraud detection (detection of malicious or irregularities in transactional patterns using machine learning), identifying customer lifetime value (prediction of bank performance based on existing and potential customers), customer segmentation (customer profiling based on behavior and characteristics for personalization of offers and services). Finally, data science is also used in real-time predictive analytics (computational techniques to predict future events).    

How HDFC utilizes Big Data Analytics to increase revenues and enhance the banking experience    

One of the major private banks in India,  HDFC Bank , was an early adopter of AI. It started with Big Data analytics in 2004, intending to grow its revenue and understand its customers and markets better than its competitors. Back then, they were trendsetters by setting up an enterprise data warehouse in the bank to be able to track the differentiation to be given to customers based on their relationship value with HDFC Bank. Data science and analytics have been crucial in helping HDFC bank segregate its customers and offer customized personal or commercial banking services. The analytics engine and SaaS use have been assisting the HDFC bank in cross-selling relevant offers to its customers. Apart from the regular fraud prevention, it assists in keeping track of customer credit histories and has also been the reason for the speedy loan approvals offered by the bank.  

Where to Find Full Data Science Case Studies?  

Data science is a highly evolving domain with many practical applications and a huge open community. Hence, the best way to keep updated with the latest trends in this domain is by reading case studies and technical articles. Usually, companies share their success stories of how data science helped them achieve their goals to showcase their potential and benefit the greater good. Such case studies are available online on the respective company websites and dedicated technology forums like Towards Data Science or Medium.  

Additionally, we can get some practical examples in recently published research papers and textbooks in data science.  

What Are the Skills Required for Data Scientists?  

Data scientists play an important role in the data science process as they are the ones who work on the data end to end. To be able to work on a data science case study, there are several skills required for data scientists like a good grasp of the fundamentals of data science, deep knowledge of statistics, excellent programming skills in Python or R, exposure to data manipulation and data analysis, ability to generate creative and compelling data visualizations, good knowledge of big data, machine learning and deep learning concepts for model building & deployment. Apart from these technical skills, data scientists also need to be good storytellers and should have an analytical mind with strong communication skills.    

Conclusion  

These were some interesting  data science case studies  across different industries. There are many more domains where data science has exciting applications, like in the Education domain, where data can be utilized to monitor student and instructor performance, develop an innovative curriculum that is in sync with the industry expectations, etc.   

Almost all the companies looking to leverage the power of big data begin with a swot analysis to narrow down the problems they intend to solve with data science. Further, they need to assess their competitors to develop relevant data science tools and strategies to address the challenging issue. This approach allows them to differentiate themselves from their competitors and offer something unique to their customers.  

With data science, the companies have become smarter and more data-driven to bring about tremendous growth. Moreover, data science has made these organizations more sustainable. Thus, the utility of data science in several sectors is clearly visible, a lot is left to be explored, and more is yet to come. Nonetheless, data science will continue to boost the performance of organizations in this age of big data.  

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Devashree Madhugiri

Devashree holds an M.Eng degree in Information Technology from Germany and a background in Data Science. She likes working with statistics and discovering hidden insights in varied datasets to create stunning dashboards. She enjoys sharing her knowledge in AI by writing technical articles on various technological platforms. She loves traveling, reading fiction, solving Sudoku puzzles, and participating in coding competitions in her leisure time.

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Frequently Asked Questions (FAQs)

A case study in data science requires a systematic and organized approach for solving the problem. Generally, four main steps are needed to tackle every data science case study: 

Getting data for a case study starts with a reasonable understanding of the problem. This gives us clarity about what we expect the dataset to include. Finding relevant data for a case study requires some effort. Although it is possible to collect relevant data using traditional techniques like surveys and questionnaires, we can also find good quality data sets online on different platforms like Kaggle, UCI Machine Learning repository, Azure open data sets, Government open datasets, Google Public Datasets, Data World and so on.  

Data science projects involve multiple steps to process the data and bring valuable insights. A data science project includes different steps - defining the problem statement, gathering relevant data required to solve the problem, data pre-processing, data exploration & data analysis, algorithm selection, model building, model prediction, model optimization, and communicating the results through dashboards and reports.  

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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 2021. Last Updated: 27 Apr 2023

Data 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.

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Walmart Sales Forecasting Data Science Project

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Table of Contents

Data science case studies in retail , data science case studies in entertainment industry , data science case studies in travel industry , data science case studies in social media , data science case studies in healthcare, data science case studies in oil and gas, 10 most interesting data science case studies with examples.

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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.

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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 project 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 project 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 science applications 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.

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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 how data science is 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 Data science models used my 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.

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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. 

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 Data science-driven products 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.

Explore More  Data Science and Machine Learning Projects for Practice. Fast-Track Your Career Transition with ProjectPro

7) LinkedIn

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 products developed by data scientists 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 applications of Data science used 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 task.

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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 applications of AI and data science used 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.

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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 applications 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.

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10 Best Data Science Books for Beginners and Advanced Data Scientist

Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more. There will be enough data science jobs that can fetch you a handsome salary as well as opportunities to grow.

That said, there is nothing better than reading data science books to get the ball rolling.

Learning data science through books will help you get a holistic view of Data Science as data science is not just about computing, it also includes mathematics, probability, statistics, programming, machine learning, and much more.

Here are some of the best books that you can read to better understand the concepts of data science –

1. Head First Statistics: A Brain-Friendly Guide

Head First Statistics- A Brain-Friendly Guide

Just like other books of Headfirst, the tone of this book is friendly and conversational and the best book for data science to start with. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. There are a lot of pictures and graphics and bits on the sides that are easy to remember. You can find some good real-life examples to keep you hooked on to the book. Overall a great book to begin your data science journey.

You can buy the book here.

2. Practical Statistics for Data Scientists

Practical Statistics for Data Scientists

If you are a beginner, this book will give you a good overview of all the concepts that you need to learn to master data science. The book is not too detailed but gives good enough information about all the high-level concepts like randomization, sampling, distribution, sample bias, etc… Each of these concepts is explained well and there are examples along with an explanation of how the concepts are relevant in data science. The book also surprises one with a survey of ML models.

This book covers all the topics that are needed for data science. It is a quick and easy reference, however, is not sufficient for mastering the concepts in-depth as the explanations and examples are not detailed.

3. Introduction to Probability

Introduction to Probability

If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on.

This is perhaps the best book to learn about probability. The explanations are pretty neat and resemble real-life problems. If you have studied probability in school, this book is a must-have to further your knowledge of the basic concepts. If you are going to learn probability for the first time – this book can help you build a strong foundation in the core concepts, though you will have to work for a little longer with the book.

The book has been one of the most popular books for about 5 decades and that is one more reason why it should definitely be on your bookshelf.

4. Introduction to Machine Learning with Python: A Guide for Data Scientists

Introduction to Machine Learning with Python- A Guide for Data Scientists

This is a book that can get you kick-started on your ML journey with Python. The concepts are explained as if to a layman and with sufficient examples for a better understanding. The tone is friendly and easy to understand. ML is quite a complex topic, however, after practicing along with the book, you should be able to build your own ML models. You will get a good grasp of ML concepts. The book has examples in Python but you wouldn’t need any prior knowledge of either maths or Programming languages for reading this book.

This book is for beginners and covers basic topics in detail. However, reading this book alone won’t be sufficient as you get deeper into ML and coding.

5. Python Machine Learning By Example

Python Machine Learning By Example

As the name says, this book is the easiest way to get into machine learning. The book gets you started with Python and machine learning in a detailed and interesting way with some classy examples like the spam email detection using Bayes and predictions using regression and tree-based algorithms. The author shares his experiences in the various areas of ML such as ad optimization, conversion rate prediction, click fraud detection, etc. which beautifully adds to the reading experience.

Though the book covers the basics of Python, you might want to start the book after you gain some basic knowledge of Python. The book will help you through the process of setting up the required software until the creation, update, and monitoring of models. Overall, a great book for beginners as well as advanced users.

6. Pattern recognition and machine learning

Pattern recognition and machine learning

This book is for all age groups, whether you are an undergraduate, graduate or advanced level researcher, there is something for everyone. If you have a Kindle subscription, this book will cost you nothing. Get the international edition that has colorful pictures and graphs making your reading experience totally worth it.

Coming to the content, this is one book that covers machine learning inside out. It is thorough and explains the concepts with examples in a simple way. Few readers could find some of the terms tough to understand but you should be able to get through using other free resources like web articles or videos. The book is a must-have if you are serious about getting into machine learning, especially the mathematical (data analytics) part is exhaustive in nature.

Though you can use the book for self-learning, it would be a better idea to read it alongside some machine learning courses.

7. Python for data analysis

Python for data analysis

True to its name, the book covers all the possible methods of data analysis. It is a great start for a beginner and covers basics about Python before moving on to Python’s role in data analysis and statistics. The book is fast-paced and explains everything in a super simple manner. You can build some real applications within a week of reading the book. This book can also give you a guideline or be a reference for the topics that you will be otherwise lost for when you search for online courses.

With focussed learning of both Python and data science, this book gives you a fair idea of what you can expect by being a data analyst or data scientist when you actually start working. The author also gives a lot of references in the book and points to useful resources that you will enjoy going through. Overall, a well-organized book with a thorough explanation of data analysis concepts.

8. Naked statistics

Naked statistics

This book brings out the beauty of statistics and makes statistics come alive. The tone is witty and conversational. You will not get bored reading this book or feel the heaviness of math! The author explains all the concepts of statistics – basic and advanced with real-life examples. The book starts with very basic stuff like the normal distribution, central theorem and goes on to complex real-life problems and correlating data analysis and machine learning.

While the book explains the basics well, it will be good to have some prior knowledge of statistics with some of these courses , so that you can quickly get on with the book.

9. Data Science and big data analytics

Data Science and big data analytics

This book gently introduces big data and how it is important in today’s digitally competitive world. The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system. The structure and flow of the book are very good and well organized. You can easily understand the entire big picture of how analytics is done as each step is like one chapter in the book. The book includes clustering, regression, association rules and much more along with simple, everyday examples that one can relate to. Advanced analytics using MapReduce, Hadoop, and SQL are also introduced to the reader.

If you are planning to learn data science with R , this is the book for you.

10. R for data science

R for data science

Another book for beginners who want to learn data science using R . R with data science explains not just the concepts of statistics but also the kind of data you would see in real life, how to transform it using the concepts like median, average, standard deviation etc. and how to plot the data, filter and clean it. The book will help you understand how messy and raw real data is and how it is processed. Transformation of data is one of the most time-consuming tasks and this book will help you gain a lot of knowledge on different methods of transforming data for processing so that meaningful insights can be taken from it. If you want to learn R before you start with the book, you can do so with simple online courses , however, the book has enough basics covered so that you can start off right away.

Here We are listing a few more good books which you might be interested in:

11. Inflection point

Inflection point

This is not a technical book. However, since you have decided to move into Data science career path, it will be necessary to know why data science and big data holds such an important place today. The book is written from a business perspective and offers a lot of insight into how all the technologies like cloud, big data, IT, mobility, infrastructure, and others are transforming the way businesses work today along with interesting stories and personal experiences to share. The changing times and how we should cope with it are described beautifully in this book.

It is a good read and will keep you motivated during your data science learning journey.

12. Storytelling with Data

Storytelling with data

Anything told as a story and shown as graphics fit into our mind easily and stays there permanently. The book is quite impactful and deals with the fundamental concepts of data visualization for you to understand how to make the most of the huge chunks of data available in the real world. The author’s way of explaining every concept is totally unique as he tells it in the form of a compelling story. You wouldn’t even realize how many concepts you can grasp in a day of reading the book – getting to know the context and audience, using the right graph for the right situation, recognizing and removing the clutter to get only the important information, utilize the most significant parts of the data and present them to users – all of these and more.

13. Big Data – A revolution

Big Data – A revolution

This is a must-have book, a primer to your big data, data science, and AI journey. It is not a technical book but will give you the whole picture of how big data is captured, converted and processed into sales and profits even without users like us knowing about it. It explains how companies are using our data and the information that we share over the internet is used to create new business innovations and solutions that make our lives easier and connect all of us. It also talks about the risks and implications involved in doing so, and how security measures are placed to avoid breach or misuse of data. There are technical papers in the end that are quite helpful. A good, simple read for everyone.

14. Practical data science with R

Practical data science with R

This is a medium level book, a good balance of basic principles and advanced data science principles. The keen focus is on business demands which is what makes the book very practical and interesting. It also explains statistics thoroughly which is one of the foundations of data science. Most books just explain how things are done – this book explains how and why! That helps motivate the readers to get into deep learning and machine learning. This is a good book for beginners and advanced level data scientists alike. It gets tougher as the advance of the topic but you can follow most of the book easily.

15. The data science handbook

The data science handbook

This is an advanced book. If you have a little knowledge about statistics and data science through other books or tutorials, you will be able to appreciate the content of the book. It is not a purely technical book but a quick reference as it contains information in the form of questions and answers from various leading data scientists. The questions flow in an organized manner and help you understand each aspect of data science like data preparation, the importance of big data, the process of automation and how data science is the future of the digital world. The book lacks real case-studies though, however, if you have a business mindset, you will get to know a lot of strategies and tips from renowned data scientists who have been there, done that.

16. Business analytics – the science of data-driven decision making

Business analytics – the science of data-driven decision making

This is an awesome in-depth book that explains the theory as well as practical applications to give wholesome knowledge. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow. The book has everything from economics, statistics, finance and all you need to start learning data science. The book has been written with a lot of effort and experience and the way insights have been presented shows the same. It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. You will also learn about scholastic models and six sigma towards the end of the book.

17. Data mining techniques

Data mining techniques

A wonderful book that explains data mining from scratch. So much so, that you need not be a computer science graduate to understand this book. It starts with explaining about the digital age, data mining and then moves to explain the kinds of data that can be mined, the patterns that can be mined, for example, cluster analysis, predictive analysis, correlations, etc., and the technologies that are used – statistics, machine learning, and database. The book is purely technical and you can go step-by-step to fully enjoy the book. The book is detailed – a must-have on your collection.

It has a lot of basic and advanced techniques for classification, cluster analysis and also talks about the trends and on-going research in the field of data mining.

18. Thinking with data

Thinking with data

This is a small book that can be read along with other reading materials and online courses. It provides a lot of useful insights and enables critical business thinking in the reader. It helps you relate to why things are happening the way they are. Through the chapters, you will learn how to ask good meaningful questions, note down the important details of an idea and get key information to focus on. It nicely covers data-specific patterns of reasoning. The book will help you think ‘why’ and not just ‘how’. It covers what is called as CoNVO – context, needs, vision, and outcome.

19. Machine learning with PySpark

Machine learning with PySpark

The book covers in detail about machine learning models, NLP (Natural language processing) applications and recommender systems using PySpark. It helps you understand the real-world business challenges and solve them. It covers linear regression, decision tree, logistic regression, and other supervised learning techniques. This book will enrich your knowledge greatly especially if you don’t just read it, rather work with the book and practice. You will also be able to appreciate the rich libraries of PySpark that are ideal for machine learning and data analysis. A great book to learn recommender systems using Spark – neat and simple.

20. Generative Deep learning

Generative Deep learning

The book is like any other fiction book that keeps you hooked up till the last page. If you have read Harry Potter, you will know what we are talking about. The author has done an exceptional job in penning all the concepts in the form of stories that are easy to comprehend. The subjects of statistics and intuitive learning are a bit dry otherwise and this book does its best to make it as interactive and interesting as possible. If you read other books, you will realize how complex neural networks and probability are. This book makes it simple. Before starting the book, familiarise yourself with Python through some courses or tutorials . One of the best books for deep learning techniques from scratch.

21. Data Science for business

Data Science for business

Purely business-oriented, this is one book to start with if you are not able to make up your mind into the field of data science. It clearly explains why you should learn data science and why it is the right choice for you. There are beautiful examples like the recommendation system, telecom churn rate, automated stock market analysis and more. The book keeps you motivated. It is not a book that will preach though. It is practical and gives you enough references to start with your technical journey too. The book emphasizes on discovering new business cases rather than just processing and analyzing data.

Check out a preview of the book on Amazon to know the concepts that are taken up in the book.

22. Designing data-intensive applications

Designing data-intensive applications

Last, but not least, this book helps understand the architecture of today’s data systems and how they can be fit into applications that are data-driven and data-intensive. It doesn’t go into depth on management, security, installation and other things but explains data retrieval, database systems and fundamental concepts at length. This book is for you if you are an architect. The author discusses various aspects of designing database and data solutions and gives loads of other resources too (at the end of every chapter!) for you to further your knowledge on the topic.

There are hundreds or more books related to data analytics and data science and don’t be overwhelmed with the huge chunk of books. You don’t have to read them all. We have carefully selected these and you should be able to build real-world models and get in-depth knowledge of data science with these books and the other resources mentioned in the blog. A few more reference books that can be helpful are Teach yourself SQL, too big to ignore, the hundred-page machine learning book, communicating data with Tableau and data analytics made accessible. Start your data science journey with any of the 22 books we have suggested and let us know how you liked reading them!

If you want to be an expert in Data Science then Data Science Course: Complete Data Science Bootcamp course can be a great asset for you.

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data science case study book

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data science case study book

Rafiya Khan

great job and nice list of data science book for different languages :) keep it up.

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data science case study book

Data Science Projects with Python: A case study approach to successful data science projects using Python, pandas, and scikit-learn 1st Edition, Kindle Edition

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python

Key Features

Book Description

Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools, by applying them to realistic data problems. You will learn how to use pandas and Matplotlib to critically examine datasets with summary statistics and graphs, and extract the insights you seek to derive. You will build your knowledge as you prepare data using the scikit-learn package and feed it to machine learning algorithms such as regularized logistic regression and random forest. You'll discover how to tune algorithms to provide the most accurate predictions on new and unseen data. As you progress, you'll gain insights into the working and output of these algorithms, building your understanding of both the predictive capabilities of the models and why they make these predictions.

By then end of this book, you will have the necessary skills to confidently use machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data.

What you will learn

Who this book is for

If you are a data analyst, data scientist, or business analyst who wants to get started using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful.

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Data Science Projects with Python: A case study approach to gaining valuable insights from real data with machine learning, 2nd Edition

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About the author

Stephen klosterman.

Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a PhD in Biology from Harvard University, where he was assistant teacher of the Data Science course. Currently he works in the health care industry. At work, he likes to research and develop machine learning solutions that stakeholders understand and value. In his spare time, he enjoys running, biking, sailing, and music. For blog posts on Data Science and Machine Learning, as well as errata and Q&A about the book, visit www.steveklosterman.com.

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data science case study book

Principles of Data Science by Sinan Ozdemir

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Data science case studies

The combination of math, computer programming, and domain knowledge is what makes data science so powerful. Often, it is difficult for a single person to master all three of these areas. That's why it's very common for companies to hire teams of data scientists instead of a single person. Let's look at a few powerful examples of data science in action and their outcome.

Case study – automating government paper pushing

Social security claims are known to be a major hassle for both the agent reading it and for the person who wrote the claim. Some claims take over 2 years to get resolved in their entirety, and that's absurd! Let's look at what goes into a claim:

Sample social security form

Not bad. It's mostly just text, though. ...

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data science case study book

The Best Data Science Interview Books

The Best Data Science Interview Books

8 best data science interview books.

There’s been a boom in data science interview books over the last few years as publishers fight to establish the latest go-to data interview guide.

Data science interview books typically include insights into the conduction of interviews and have practice interview questions (with solutions), interviewing strategies, and tips. However, one thing to remember is that prep books aren’t the end-all-be-all, and there isn’t a guide that will replace the need to do mock interviews or practice various SQL, Python, and statistics questions.

The best prep books, though, will demystify the interview process and provide a high-level overview of how to answer questions. After reviewing a variety of books, these are the best data interview books for 2022:

1. Cracking the Data Science Interview

2. Heard in Data Science Interviews

3. 120 Data Science Interview Questions

4. Becoming a Data Head

5. The Data Science Handbook

6. Be the Outlier

7. Data Science Interviews Exposed

8. Ace the Data Science Interview

Cracking the Data Science Interview

Several authors have taken the best-selling Cracking the PM Interview formula and applied it to just about every field. Cracking the Data Science Interview is one book that isn’t affiliated with Gayle Laakmann McDowell’s original.

Cracking the Data Science Interview was written by Maverick Lin, a data scientist who compiled the book while completing data science interviews in 2019. Lin said his goal was to gather a cheatsheet of concepts he saw most frequently, which is the basis of this book: A high-level overview of core data science concepts, along with 100+ interview questions.

This book provides a solid review of data science concepts, and before an interview, it’s always helpful to brush up on the basics. The questions are also practical. However, you’ll find more profound collections of data science interview questions online or in other references, and these alternatives typically have more in-depth explanations and solutions.

Heard in Data Science Interviews 2018

Heard in Data Science Interviews boasts a wide selection of 650+ data science interview questions across all the major topics, like algorithms, statistics, computer science, and data modeling.

Written by Kal Mishra, a data scientist with more than ten years of industry experience, this guide is intended to cut out “fluff” portions often found in interviews, focusing mainly on “genuine AI questions.”

While this book may be helpful for new interviewees looking for a comprehensive guide, persistent complaints of the errors in Heard in Data Science Interviews’ answer key make us hesitant to recommend it wholeheartedly.

At almost $50 (one of the highest price points on our list!), there is sure to be a better option without the glaring flaws in this text.

120 Data Science Interview Questions 2014

Written by data scientists for data scientists, this collection of questions covers specific data science topics: programming, stats, probability, etc.

Unique across all the other books on our list, “120 Data Science Interview Questions” also lists a Communication section designed to tackle those infamous interview questions asking you to describe certain concepts in non-technical terms.

Out of all of the guides reviewed in this article, 120 Data Science Interview Questions offer the most fleshed out, interview-Esque questions typically found in data science interviews. This guide may be perfect for those looking to practice talking through solutions! For data scientists trying to establish a firm content foundation, you may need to look elsewhere for a more comprehensive reference with a complete answer key.

Becoming a Data Head 2021

Becoming a Data Head: How to Think, Speak, and Understand Data Science, Statistics, and Machine Learning isn’t an interview book per se. However, it will help you think more critically about data and learn to ask the right questions, a skill that’s super beneficial in data science interviews.

Written by award-winning data scientists Alex Gutman and Jordan Goldmeier, this book will help you learn to avoid common data interpretation mistakes and provide an idea of the “types of personalities you will meet in the workplace.”

Becoming a Data Head will help you level up your data science vocabulary and brush up on critical data thinking skills. The book provides a solid accounting of real-world data science applications. It will help you embrace a mindset to ask better questions about the situations you encounter on the job.

This book won’t offer benefits to content review or practice questions for last-minute studying before an important interview. This book requires solid data science and machine learning knowledge to grasp the content thoroughly.

If you’re looking for help preparing for an incoming interview, this book may not be your best leading resource. While it provides valuable insights into data science and its application in modern businesses, it doesn’t dwell on interview structures or style. However, the book is super insightful and exciting, and it’s an excellent resource for building your data sense, a skill you will want to display in any interview.

The Data Science Handbook 2015

From the same authors of 120 Data Science Interview Questions comes “ The Data Science Handbook ,” a collection of 25 interviews with well-established data scientists about their perspectives in the field.

Unlike the other selections in this article, “The Data Science Handbook” doesn’t cover interviewing techniques or topics but discusses the career trajectories of successful data scientists navigating the industry.

If you’re looking for help preparing for an incoming interview, this book may not be your best primary resource. While it provides valuable insights into the careers of many famous data scientists, candidates would better spend their time with other guides that focus directly on interview structures and style.

For those looking for general information about data science or those that may be interested in how different data scientists had their breakthroughs, read away!

Be the Outlier: How to Ace Data Science Interviews 2020

Written by data scientist Shrilata Murthy, Be the Outlier takes a different approach than most interview prep books; this book will help you understand how to position yourself as an outlier to land the job. In addition to a helpful concept review, the book also includes powerful resume-writing tips and in-depth accounting of what to expect in various interview formats like take-homes, presentations, case studies, and more.

This text is a solid prep book on data science, and it gives you a sense of what you can expect. The book also lets you know why questions get asked and what interviewers are looking for in your response. The one knock on this book is that the question bank is limited; you’d need to supplement your learning with additional questions.

Data Science Interviews Exposed 2015

Written by a collective of data scientists, “Data Science Interviews Exposed” was one of the first data science interview-guide books available on the market. In addition to the standard technical interview topics in many similar texts, this book reviews job search procedures and traditional screening interview processes.

At a price point of $50, this book isn’t the most cost-effective or efficient way to review for your data science interview. Newer data scientists may appreciate insights into job searches and soft skills. However, a new data scientist can also find these insights this information in collated online blog posts and resources from current data scientists. From a technical standpoint, this guidebook may not be the best resource depending on your experience level and the number of questions.

Ace the Data Science Interview 2021

Since its release in 2021, Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street has quickly become a favorite. The resource, co-authored by ex-Facebook employees, features the most in-depth question set in our list, helpful interviewing tips, resume writing advice, and tips for crafting a portfolio.

The guide’s 201 questions feature detailed step-by-step solutions (some of the most comprehensive of these books). The material covered includes probability, statistics, machine learning, SQL, Python, product metrics, database design, and A/B testing.

Use this book as a benchmarking tool; you can use it to understand where your strengths and weaknesses lie before you jump into the interviewing process. The book is a solid premier, especially for early-career data scientists. There’s much helpful information about how to land interviews, build your resume, what to wear, and what you can expect in the interview room.

Are Interview Books Still Useful in Data Science?

After reviewing these prep guides, it’s evident that no book covers all the possible topics for a successful data science interview. However, interview books can be helpful for data scientists, but they’re just one tool.

Mock interviews, coaching, and practicing SQL, Python, statistics, and other real data science interview questions are all vitally important. Because the field constantly evolves, you won’t find the most current information in a textbook.

Therefore, as you prepare for data science interviews, diversify how you study. Use books to benchmark where you’re at, then find more tailored interview resources to practice and brush up on the skills that need work.

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Stephen Klosterman is a Machine Learning Data Scientist with a background in math, environmental science, and ecology. His education includes a Ph.D. in Biology from Harvard University, where he was an assistant teacher of the Data Science course. His professional experience includes work in the environmental, health care, and financial sectors. At work, he likes to research and develop machine learning solutions that create value, and that stakeholders understand. In his spare time, he enjoys running, biking, paddleboarding, and music.

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Machine Learning

100+ free data science books.

Pulled from the web, here is a our collection of the best, free books on Data Science, Big Data, Data Mining, Machine Learning, Python, R, SQL, NoSQL and more.

If you’re looking for even more learning materials, be sure to also check out an online data science course through our comprehensive courses list.

Looking for more books? Go back to our main books page .

Note that while every book here is provided for free, consider purchasing the hard copy if you find any particularly helpful. In many cases you will find Amazon links to the printed version, but bear in mind that these are affiliate links, and purchasing through them will help support not only the authors of these books, but also LearnDataSci. Thank you for reading, and thank you in advance for helping support this website.

Instantly find the books you are looking for, just start typing below.

Artificial Intelligence A Modern Approach, 1st Edition

Artificial Intelligence A Modern Approach, 1st Edition

Comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

The LION Way: Machine Learning plus Intelligent Optimization

The LION Way: Machine Learning plus Intelligent Optimization

Learning and Intelligent Optimization (LION) is the combination of learning from data and optimization applied to solve complex and dynamic problems. Learn about increasing the automation level and connecting data directly to decisions and actions.

Disruptive Possibilities: How Big Data Changes Everything

Disruptive Possibilities: How Big Data Changes Everything

This book provides an historically-informed overview through a wide range of topics, from the evolution of commodity supercomputing and the simplicity of big data technology, to the ways conventional clouds differ from Hadoop analytics clouds.

Online Data Science Courses

Comprehensive list of top data science courses for 2021

Computer Vision

Computer Vision

Challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which you can use on you own personal media

Natural Language Processing with Python

Natural Language Processing with Python

This book offers a highly accessible introduction to natural language processing, the field that supports a variety of language technologies, from predictive text and email filtering to automatic summarization and translation.

Programming Computer Vision with Python

Programming Computer Vision with Python

If you want a basic understanding of computer vision’s underlying theory and algorithms, this hands-on introduction is the ideal place to start. You’ll learn techniques for object recognition, 3D reconstruction, stereo imaging, augmented reality, etc

The Elements of Data Analytic Style

The Elements of Data Analytic Style

Data analysis is at least as much art as it is science. This book is focused on the details of data analysis that sometimes fall through the cracks in traditional statistics classes and textbooks.

A Course in Machine Learning

A Course in Machine Learning

A First Encounter with Machine Learning

A First Encounter with Machine Learning

Algorithms for Reinforcement Learning

Algorithms for Reinforcement Learning

This book gives a very quick but still thorough introduction to reinforcement learning, and includes algorithms for quite a few methods. This is everything a graduate student could ask for in a text.

A Programmer's Guide to Data Mining

A Programmer's Guide to Data Mining

A guide to practical data mining, collective intelligence, and building recommendation systems by Ron Zacharski. This work is licensed under a Creative Commons license.

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning

For final-year undergraduates and master's students with limited background in linear algebra and calculus. Comprehensive and coherent, it develops everything from basic reasoning to advanced techniques within the framework of graphical models.

Data Mining Algorithms In R

Data Mining Algorithms In R

Data Mining and Analysis: Fundamental Concepts and Algorithms

Data Mining and Analysis: Fundamental Concepts and Algorithms

The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks, and also covers many more cutting-edge data mining topics.

Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques

Offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations.

Data Mining with Rattle and R

Data Mining with Rattle and R

This book aims to get you into data mining quickly. Load some data (e.g., from a database) into the Rattle toolkit and within minutes you will have the data visualised and some models built.

Deep Learning

Deep Learning

The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular.

Gaussian Processes for Machine Learning

Gaussian Processes for Machine Learning

A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Information Theory, Inference, and Learning Algorithms

Information Theory, Inference, and Learning Algorithms

"Essential reading for students of electrical engineering and computer science; also a great heads-up for mathematics students concerning the subtlety of many commonsense questions." Choice

Introduction to Machine Learning

Introduction to Machine Learning

Introduction to Machine Learning

KB – Neural Data Mining with Python Sources

Machine Learning

Machine Learning, Neural and Statistical Classification

Machine Learning – The Complete Guide

Machine Learning – The Complete Guide

Mining of Massive Datasets

Mining of Massive Datasets

Essential reading for students and practitioners, this book focuses on practical algorithms used to solve key problems in data mining, with exercises suitable for students from the advanced undergraduate level and beyond.

Modeling With Data

Modeling With Data

Modeling with Data offers a useful blend of data-driven statistical methods and nuts-and-bolts guidance on implementing those methods. --Pat Hall, founder of Translation Creation

Neural Networks and Deep Learning

Neural Networks and Deep Learning

Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you concepts behind neural networks and deep learning.

Bayesian Methods for Hackers

Probabilistic Programming & Bayesian Methods for Hackers

illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments.

Real-World Active Learning

Real-World Active Learning

Applications and Strategies for Human-in-the-loop Machine Learning.

Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction

A clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.

Social Media Mining An Introduction

Social Media Mining An Introduction

Suitable for use in advanced undergraduate and beginning graduate courses as well as professional short courses, the text contains exercises of different degrees of difficulty that improve understanding and help apply concepts in social media mining

Theory and Applications for Advanced Text Mining

Theory and Applications for Advanced Text Mining

This book is composed of 9 chapters introducing advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language.

Understanding Machine Learning: From Theory to Algorithms

Understanding Machine Learning: From Theory to Algorithms

The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

An introduction to data science

An Introduction to Data Science

This book was developed for the Certificate of Data Science pro- gram at Syracuse University’s School of Information Studies.

Data Jujitsu: The Art of Turning Data into Product

Data Jujitsu: The Art of Turning Data into Product

Learn how to use a problem's "weight" against itself. Learn more about the problems before starting on the solutions—and use the findings to solve them, or determine whether the problems are worth solving at all.

School of Data Handbook

School of Data Handbook

The School of Data Handbook is a companion text to the School of Data. Its function is something like a traditional textbook – it will provide the detail and background theory to support the School of Data courses and challenges.

Art of Data Science

The Art of Data Science

This book describes the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and this book is a distillation of their experience...

D3 Tips and Tricks

D3 Tips and Tricks

D3 Tips and Tricks is a book written to help those who may be unfamiliar with JavaScript or web page creation get started turning information into visualization.

Interactive Data Visualization for the Web

Interactive Data Visualization for the Web

Create and publish your own interactive data visualization projects on the Web—even if you have little or no experience with data visualization or web development. It’s easy and fun with this practical, hands-on introduction.

Data-Intensive Text Processing with MapReduce

Data-Intensive Text Processing with MapReduce

MapReduce [45] is a programming model for expressing distributed computations on massive amounts of data and an execution framework for large-scale data processing on clusters of commodity servers. It was originally developed by Google...

Hadoop Illuminated

Hadoop Illuminated

'Hadoop illuminated' is the open source book about Apache Hadoop™. It aims to make Hadoop knowledge accessible to a wider audience, not just to the highly technical.

Hadoop Tutorial as a PDF

Hadoop Tutorial as a PDF

Intro to Hadoop - An open-source framework for storing and processing big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines.

Programming Pig

Programming Pig

This guide is an ideal learning tool and reference for Apache Pig, the open source engine for executing parallel data flows on Hadoop.

Building Data Science Teams

Building Data Science Teams

In this in-depth report, data scientist DJ Patil explains the skills,perspectives, tools and processes that position data science teams for success.

Data Driven: Creating a Data Culture

Data Driven: Creating a Data Culture

In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.

The Data Science Handbook

The Data Science Handbook

The Data Science Handbook is a compilation of in-depth interviews with 25 remarkable data scientists, where they share their insights, stories, and advice.

A Byte of Python

A Byte of Python

‘A Byte of Python’ is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

Advanced R

Useful tools and techniques for attacking many types of R programming problems, helping you avoid mistakes and dead ends. With ten+ years of experience programming in R, the author illustrates the elegance, beauty, and flexibility at the heart of R.

A Little Book of R for Time Series

A Little Book of R for Time Series

This is a simple introduction to time series analysis using the R statistics software.

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Automate the Boring Stuff with Python: Practical Programming for Total Beginners

Practical programming for total beginners. In Automate the Boring Stuff with Python, you'll learn how to use Python to write programs that do in minutes what would take you hours to do by hand-no prior programming experience required.

Dive Into Python 3

Dive Into Python 3

This is a hands-on guide to Python 3 and its differences from Python 2. Each chapter starts with a real, complete code sample, picks it apart and explains the pieces, and then puts it all back together in a summary at the end.

Ecological Models and Data in R

Ecological Models and Data in R

The first truly practical introduction to modern statistical methods for ecology. In step-by-step detail, the book teaches ecology graduate students and researchers everything they need to know to analyze their own data using the R language.

Invent with Python

Invent with Python

"Invent Your Own Computer Games with Python" teaches you computer programming in the Python programming language. Each chapter gives you the complete source code for a new game and teaches the programming concepts from these examples.

Learning Statistics with R

Learning Statistics with R

I (Dani) started teaching the introductory statistics class for psychology students offered at the University of Adelaide, using the R statistical package as the primary tool. These are my own notes for the class which were trans-coded to book form.

Learning with Python 3

Learning with Python 3

Introduction to computer science using the Python programming language. It covers the basics of computer programming in the first part while later chapters cover basic algorithms and data structures.

Learn Python, Break Python: A Beginner's Guide to Programming

Learn Python, Break Python

This is a hands-on introduction to the Python programming language, written for people who have no experience with programming whatsoever. After all, everybody has to start somewhere.

Learn Python the Hard Way

Learn Python the Hard Way

This is a free sample of Learn Python 2 The Hard Way with 8 exercises and Appendix A available for you to review.

Practical Regression and Anova using R

Practical Regression and Anova using R

This book is NOT introductory. The emphasis of this text is on the practice of regression and analysis of variance. The objective is to learn what methods are available and more importantly, when they should be applied.

python for everybody cover.jpg

Python for Everybody

This book is designed to introduce students to programming and computational thinking through the lens of exploring data. You can think of Python as your tool to solve problems that are far beyond the capability of a spreadsheet.

Python for You and Me

Python for You and Me

This is a simple book to learn the Python programming language, it is for the programmers who are new to Python.

Python Practice Book

Python Practice Book

This book is prepared from the training notes of Anand Chitipothu.

Python Programming

Python Programming

This book describes Python, an open-source general-purpose interpreted programming language available for a broad range of operating systems. This book describes primarily version 2, but does at times reference changes in version 3.

R by Example

R by Example

R Programming

R Programming

The aim of this Wikibook is to be the place where anyone can share his or her knowledge and tricks on R. It is supposed to be organized by task but not by discipline. We try to make a cross-disciplinary book, i.e. a book that can be used by all.

R Programming for Data Science

R Programming for Data Science

This book is about the fundamentals of R programming. You will get started with the basics of the language, learn how to manipulate datasets, how to write functions, and how to debug and optimize code.

Spatial Epidemiology Notes: Applications and Vignettes in R

Spatial Epidemiology Notes: Applications and Vignettes in R

My intent is to present a relatively brief, non-jargony overview of how practicing epidemiologists can apply some of the extremely powerful spatial analytic tools that are easily available to them.

The R Inferno

The R Inferno

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks.

The R Manuals

The R Manuals

The R Manuals.

Think Python second edition

Think Python 2nd Edition

This hands-on guide takes you through Python a step at a time, beginning with basic programming concepts before moving on to functions, recursion, data structures, and object-oriented design. Updated to Python 3.

A First Course in Linear Algebra

A First Course in Linear Algebra

This is an introduction to the basic concepts of linear algebra, along with an introduction to the techniques of formal mathematics. It has numerous worked examples, exercises and complete proofs, ideal for independent study.

Elementary Applied Topology

Elementary Applied Topology

This text gives a brisk and engaging introduction to the mathematics behind the recently established field of Applied Topology.

Elementary Differential Equations

Elementary Differential Equations

This text has been written in clear and accurate language that students can read and comprehend. The author has minimized the number of explicitly state theorems and definitions, in favor of dealing with concepts in a more conversational manner.

Introduction to Probability

Introduction to Probability

This book is designed for an introductory probability course at the university level for sophomores, juniors, and seniors in mathematics, physical and social sciences, engineering, and computer science.

Linear Algebra

Linear Algebra

Linear Algebra: An Introduction to Mathematical Discourse

Linear Algebra: An Introduction to Mathematical Discourse

Linear Algebra, Theory And Applications

Linear Algebra, Theory And Applications

This book gives a self- contained treatment of linear algebra with many of its most important applications. It is very unusual if not unique in being an elementary book which does not neglect arbitrary fields of scalars and the proofs of the theorems

Ordinary Differential Equations

Ordinary Differential Equations

Probabilistic Models in the Study of Language

Probabilistic Models in the Study of Language

Probability and Statistics Cookbook

Probability and Statistics Cookbook

The probability and statistics cookbook is a succinct representation of various topics in probability theory and statistics. It provides a comprehensive mathematical reference reduced to its essence, rather than aiming for elaborate explanations.

Cassandra Tutorial as a PDF

Cassandra Tutorial as a PDF

Extracting Data from NoSQL Databases

Extracting Data from NoSQL Databases

Graph Databases

Graph Databases

Get started with O'Reilly's Graph Databases and discover how graph databases can help you manage and query highly connected data.

NoSQL Databases

NoSQL Databases

SQL for Web Nerds

SQL for Web Nerds

SQL Tutorial as a PDF

SQL Tutorial as a PDF

This tutorial will give you a quick start to SQL. It covers most of the topics required for a basic understanding of SQL and to get a feel of how it works.

The Little MongoDB Book

The Little MongoDB Book

MongoDB is an open source NoSQL database, easily scalable and high performance. It retains some similarities with relational databases which, in my opinion, makes it a great choice for anyone who is approaching the NoSQL world.

A First Course in Design and Analysis of Experiments

A First Course in Design and Analysis of Experiments

Suitable for either a service course for non-statistics graduate students or for statistics majors. Unlike most texts for the one-term grad/upper level course on experimental design, this book offers a superb balance of both analysis and design.

An Introduction to Statistical Learning with Applications in R

An Introduction to Statistical Learning with Applications in R

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, and much more.

Artificial Intelligence: Foundations of Computational Agents

Artificial Intelligence: Foundations of Computational Agents

This is a textbook aimed at junior to senior undergraduate students and first-year graduate students. It presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents.

Intro Stat with Randomization and Simulation

Intro Stat with Randomization and Simulation

The foundations for inference are provided using randomization and simulation methods. Once a solid foundation is formed, a transition is made to traditional approaches, where the normal and t distributions are used for hypothesis testing and...

OpenIntro Statistics

OpenIntro Statistics

Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org.

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.

Think Bayes: Bayesian Statistics Made Simple

Think Bayes: Bayesian Statistics Made Simple

Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics.

Think Stats: Exploratory Data Analysis in Python

Think Stats: Exploratory Data Analysis in Python

This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

Pattern Recognition and Machine Learning book cover

Pattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible.

Well, there you have it. Thousands of e-pages to read through. We hope there's a data science book here for everyone, no matter what level you're starting at. If you have any suggestions of free books to include or want to review a book mentioned, please comment below and let us know!

We are against illegal distribution of materials, so if you find that one of these books is a pirated copy, please inform us so that we can remove it from the list immediately.

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