20 Best Books on Data Science

“Python for Data Science for Dummies” book teaches the reader how to use Python programming to acquire, organize, process, and analyze large amounts of information. It also focuses on using basic statistics concepts to identify trends and patterns. The readers will learn python development, how to manipulate data, and design compelling visualizations. By the end of the book, they will be able to solve scientific computing challenges. The book also explains objects, functions, modules, and libraries and their role in data analysis. The book is useful for anyone interested in learning about data analysis and Python.

“Agile Data Science” book gives a hands-on guide to u recent data scientists to learn how to use the agile data science development methodology to build various data applications with Python, Apache spark, Kafka, and other tools. It illustrates how to build a data platform for building, deploying and refining analytics applications with Apache kafka, MongoDB, ElasticSearch, d3.js, scikit-learn, and Apache Airflow. It helps to learn a different approach that lets quick change in data analysis.

“Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking” Book Review: This book teaches the basic principles of data science and data-analytic thinking necessary for extracting valuable information and business insights from collected data. It presents real-world business problems to explain data science concepts and emphasizes the importance of effective communication between stakeholders and data scientists. It also covers mathematical functions for fitting models, visualization of model performance, and understanding evidence and probabilities. Each chapter ends with a summary for clarity, making it suitable for beginners and intermediate professionals interested in data analytics and how it can support business decision-making.

“Data Science for Dummies” book is for students and professionals. This book covers various aspects of data science. Part 1 introduces data science engineering and its application in business and industry. Part 2 discusses machine learning and statistical modeling, including building models for IoT devices. Part 3 covers data visualization and web-based applications. Part 4 details the use of Python, R, SQL, Excel, and Knime for data science. Part 5 focuses on solving real-world problems using data science. Part 6 provides information on open data resources, data science tools, and their applications. The book provides both theoretical and practical knowledge on data science.

“Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools” Book Review: This book teaches the fundamental tasks of data science using Python programming, covering important aspects of the field. Readers gain hands-on experience with popular Python libraries like Scikit-learn and StatsModels, and learn about machine learning, handling large data, and writing data science algorithms. The book covers a range of topics, including the data science process, big data, NoSQL databases, graph databases, text mining, text analytics, and data visualization. It provides both theoretical knowledge and practical skills for data scientists, making it a valuable resource.

“Data Smart: Using Data Science to Transform Information into Insight” helps those students who are serious about studying the analytic techniques, the math, and the behind big data. Each chapter covers a different technique in spreadsheet including mathematical optimization, nonlinear programming and various genetic algorithms. The book provides nine tutorials on optimization, machine learning, data mining, and forecasting all within the confines of a spreadsheet. Hosting these nine spreadsheets for download will be necessary so that the reader can work the problems along with the book. Each chapter will cover a different technique in a spreadsheet. The tutorials use a real-world problem and the users are guided with query’s the reader might ask as how to craft a solution using the correct data science technique. It demonstrates the clustering using k-means, spherical k-means, and graph modularity. It illustrates data mining tin graphs, such as outlier detection and also discusses supervised AI through logistic regression models. The different techniques are: Mathematical optimization, including non-linear programming and genetic algorithms, Clustering via k-means, spherical and graph modularity, Data mining in graphs, such as outlier detection, Supervised AI through logistic regression, ensemble models, and bag-of-words models, Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation, Moving from spreadsheets into the R programming language.

“Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie or Die” is easily understandable and simple for tech geeks as well as the common man consisting of the latest case studies and various high-end computing techniques. It tries to answer all the questions about situations that can be confirmed but can still be predicted in advance.

“Doing Data Science: Straight Talk from the Frontline” is based on Columbia University’s introduction to data science class which tells everything to know about data science. The book provides many chapter-long lectures which were given by data scientists from big companies such as Google, Microsoft, and eBay on new algorithms, methods, and models by illustrating case studies and the code they use. If we have prior knowledge of linear algebra, probability, and statistics, and have programming experience, this book is an ideal book for introduction to data science. It covers topics including statistical inference, data science process, algorithms, data visualization and data engineering. It also demonstrates the concepts such as Naive Bayes, linear regression, financial modelling and Hadoop Mapreduce.

“Machine Learning for Big Data: Hands-On for Developers and Technical Professionals” book gives an overview of various techniques used to gain insight from data. It gives some practical explanations on how the code is put together. It shows how one can apply the right machine learning techniques to own problems. It explores how data can be powerful and at the same time how this power can be used against us. providing coded solutions for real-world examples. There is a strong focus on data preparation and data cleaning, the core fundamentals of machine learning. Each chapter includes how the code works and running examples.

“Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data” book offers an overview of big data technologies and explains what is needed to succeed with big data. It gives examples of both successful and failed data practices undertaken by startups, online firms and large companies. It provides a comprehensive overview of visualizing and presenting the data in a more reasonable manner. It also discusses in detail the discovering and analyzing of data science and big data analytics. The book is useful for students, researchers and data analysts.