Data science is the revolutionary tech for gathering knowledge from data that is either structured or unstructured. Different data are collected using scientific methods, algorithms, and many more ways to make new learning. It is considered the 4th paradigm of science. Various data science books, publications, thesis papers, and magazines are available online, which declare the glory, present basement, future destination, and ways to be with Data Science.
Why is data science required? To make a very important and careful decision based on a lot of information or data in bigger fields like industries, marketing, etc. Data Science is the only solution. The data scientists, especially Ph.D. holders, are highly demanding in these fields, and he is highly paid. This is to show the importance and value of data science.
Best Data Science Books
As per the above discussion, we can easily understand the requirements for learning data science. Therefore, we have gathered some of the best online data science books to help data science knowledge seekers study more easily. We hope these books will be a good foundation for upcoming data scientists.
1. Introducing Data Science
The start of a data science study should be well organized; thus, this book is written to teach introductory data science in an organized fashion. No doubt, this book is different from other data science books available. The book highlights the main factors and benefits that can attract a new reader to the data science world. A discussion of machine learning and the process of data science is included in the book.
Table of Contents
- Data Science in a Big Data World
- Data Science Process
- Machine Learning
- Handling Large Data on a Single Computer
- First Steps in Big Data
- Join the NoSQL Movement
- The Rise of Graph Database
- Text Mining and Text Analytics
- Data Visualization to the End-User
2. Getting Started With Data Science
If you want to start with Data Science without losing interest, this book is the perfect book among all other Data Science books. Numerous interesting and important logics are well discussed in the book. You can know how to speak hypothetically and understand many important decision-making processes. The whole data science is made understandable with different graphical presentations and tables.
Table of Contents
- The Bazaar of Storytellers
- Data in the 27/7 Connected World
- The Deliverable
- Serving Tables
- Graphic Details
- Hypothetically Speaking
- Why Tall Parents Don’t Have Even Taller Children
- To Be or Not To Be
- Categorically Speaking About Categorical Data
- Spatial Data Analytics
- Doing Serious Time with Time Series
- Data Mining for Gold
3. Data Science: Concepts and Practice
All the basic data science books that clearly explain the concept of the topic are vast and detailed. This data science book is also the same, where different topics related to data science are also brought to make the understanding easy and fruitful. Besides many important topics, you can learn how to detect anomalies and how to select features. You will also get the basic knowledge to start with Rapid Miner.
Table of Contents
- AI, Machine Learning, and Data Science
- Data Science Process
- Data Exploration
- Classification
- Regression Methods
- Association Analysis
- Clustering
- Model Evaluation
- Text Mining
- Deep Learning
- Recommended Engines
- Time Series Forecasting
- Anomaly Detection
- Feature Selection
- Getting Started with Rapid Miner
4. Data Science from Scratch
Another great collection from O’Reilly Data Science Books that teaches the topic very interestingly. The gradual development of the book will surely impress you. Many important topics like Linear Algebra, Machine Learning, Neural networks, etc., are clearly discussed. You can learn Natural language processing and know how to analyze the network.
Table of Contents
- The Ascendance of Data
- A Crash Course in Python
- Visualization Data
- Linear Algebra
- Statistics
- Probability
- Hypothesis and Interface
- Gradient Descent
- Getting Data
- Working with Data
- Machine Learning
- K-Nearest Neighbors
- Naive Bayes
- Simple Linear Regression
- Multiple Regression
- etc.
5. Data Science at the Command Line
Data Science at the Command Line is a collection of O’Reilly. Unlike other data science books, this book starts with defining the command line. Then, gradually, it shows different aspects of data science.
All the topics are well covered, and you will get a systematic description of all. You will get an overview of all the topics before you go deeper. At the end of the book, you will get a list of where different tools of command-line are given.
Table of Contents
- What is the Command Line
- Getting Started
- Obtaining Data
- Getting Reusable Command-Line Tools
- Scrubbing Data
- Managing Your Data Workflow
- Exploring Data
- Parallel Pipelines
- Modeling Data
- List of Command-Line Tools
6. The Field Guide to Data Science
This book is an excellent guide for readers who want to know data science properly and genuinely. The beginning of the book contains a concise and concrete description of the topic. Then, there are many guidelines and ways to go deeper into data science.
You can learn basic machine learning and its relation to data science. The book will give you a clear idea about the far-reaching and bright future of data science, motivating and increasing your interest in the field.
Table of Contents
- The Short Version- The Core Concepts of Data Science
- Start Here for the Basics
- Take off the Training Wheels
- Life in the Trenches
- Putting it all Together
- The Feature of Data Science
7. Big Data, Data Mining, and Machine Learning
The book is a combo of three important technologies: Big Data, Data Mining, and Machine learning. The first part of the book discusses Hardware, Distributed systems, and Analytical Tools. Then, the book emphasizes the way to turn data into business. Finally, different case studies are included in the final chapter, where learning from incidents from well-known industries is included.
Table of Contents
- Part I: The Computing Environment
- Hardware
- Distributed System
- Analytical Tools
- Part II: Turning Data into Business Value
- Predictive Modeling
- Common Predictive Modeling Techniques
- Segmentation
- Incremental Response Modeling
- Time Series Data Mining
- Recommendation System
- Text Analytics
- Success Stories of Putting It All Together
- Case Study of Large U.S.-Based Financial Service Company
- Case Study of Major Health Care Provider
- Case Study of Technology Manufacturer
- Case Study of Online Brand Management
- Case Study of High-Tech Product Manufacturer
- Looking to the Future
8. Mastering Python for Data Science
Python is one of the ruling languages of computer science. This book teaches you to explore the data science world via Python. The book is a perfect guide to perfect data sensing. You can consider the book one of the best data science or big data books.
Many tricks and tips for doing hard work are given in the book. You can estimate many of your important calculations before going to a big job after finishing this book.
Table of Contents
- Getting Started with Raw Data
- Inferential Statistics
- Finding a Needle in Haystack
- Advanced Visualization Tools for Decision-Making
- Uncovering Machine Learning
- Performing Predictions with a Linear Regression
- Estimating the Likelihood of Events
- Generating Recommendations with Collaborative Filtering
- Pushing Boundaries with Ensemble Models
- Applying Segmentation with k-means Clustering
- Analyzing Unstructured Data with Text Mining
- Leveraging Python in the World of Big Data
9. Python Data Science Handbook
The O’Reilly collection always brings awesome and outstanding books. They also catered to a book that discussed Data Science through Python. However, the book is so precise and comprehensive that it is named the handbook. The book will take you to the data science world using Python as a medium and take you beyond the limit you imagined before.
Table of Contents
- IPython Beyond Normal Python
- Introduction to NumPy
- Data Manipulation with Pandas
- Visualization with Matplotlib
- Machine Learning
10. R Programming for Data Science
R is an essential programming language used for statistical computations, graph representation, and data analysis. So, as a learner of data science, R programming is a must, and it’s a vast subject. To make it easy and fruitful, R programming for the Data Science book is written. Plenty of necessary and essential topics are discussed in the book.
Table of Contents
- History and overview of R
- Getting Started with R
- R Nuts and Blots
- Getting Data In and Out of R
- Using Textual and Binary Romans for Storing Data
- Interfaces to the Outside World
- Subsetting R Objectives
- Necrotized Operations
- Dates and Times
- Managing Data Frames with the dplyr Package
- Control Structures
- etc.
11. Malware Data Science: Attack Detection and Attribution
Where it is good, there is a threat. Data science is no exception to having threats being good. Therefore, data science books and big data books also project some risk factors in their contents. However, this is the book that has been completely written about threats to data science.
The book nicely introduces the threats to data science and then shows how to get rid of them. There are different detectors, tools, and many more, which the book discusses nicely.
Table of Contents
- Basic Static Malware Analysis
- Beyond Basic Static Analysis: x86 Disassembly
- A Brief Introduction to Dynamic Analysis
- Identifying Attack Campaigns Using Malware Networks
- Shared Code Analysis
- Understanding Maxine Learning-Based Malware Detection System
- Building Machine Learning Detectors
- Visualizing Malware Trends
- Deep Learning Basics
- Building Neural Network Malware Detector with Kiera’s
- Becoming a Data Scientist
12. Practical Statistics for Data Scientists
Data scientists are the mentors, moderators, developers, and guardians of data science. Data scientists are required to know a lot of statistics, and they must know how to manage and process them. O’Reilly Collections has another data science book covering all the statistical requirements that a data scientist may require. The book classifies all the data processes, teaches data analysis, teaches the distribution process of data, and many more.
Table of Contents
- Exploratory Data Analysis
- Data Sampling Distributions
- Statistical Experiments and Significance Testing
- Regression and Prediction
- Classification
- Statistical Machine Learning
- Unsupervised Learning
13. Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence
Statistics with Julia: fundamentals for Data Science, Machine Learning, and Artificial Intelligence is a very good book that covers not only Data Science but also machine learning and artificial intelligence. The book is aimed to help with the research of prediction, analyzing, programming, designing, planning, etc. With many essential topics, the book contains a good list of codes for the learners.
Table of Contents
- Introducing Julia
- Basic Probability
- Probability Distributions
- Processing and Summarizing Data
- Confidence Intervals
- Hypothesis Testing
- Linear Regression and Extensions
- Machine Learning Basics
- Simulation of Dynamic Models
14. The Data Science Design Manual
The author of ‘The Algorithm Design Manual’ now presents you with another fabulous book, ‘ The Data Science Design Manual.’ The book proves that data science is not rocket science but rather an easy topic. It teaches the process of developing mathematical intuition. After reading the book, you can act like you are a good Statistician. The book is a great piece for both students and instructors in data science.
Table of Contents
- What is Data Science
- Mathematical Preliminaries
- Data Munging
- Scores and Rankings
- Statistical Analysis
- Visualizing Data
- Linear and Logistic Regression
- Distance and Logistic Methods
- Machine Learning
- Big Data: Achieving Scale
- Coda
The Ending Remarks
Data Science is like a chain reaction. It creates the created things. The area of data science is enormously used. It is mostly used for big business purposes where an important decision is based on data.
We have tried to gather different categories of data science and big data books. We believe these books will feed knowledge to newbies and advanced readers. All the books are very good for the instructors to use in their teaching process.
Finally, we conclude with the hope that the article has helped you in finding your desired data science and big data books. Please share it with your friends. Enlighten us with your ideas and books, which could be included here.
