10 amazing Data Science Books, You will regret not reading

In this Blog, we will cover those books, you will regret you did not purchase them. Most of the Data science books, research article, and courses require programming skills. There is one of the Playlist you can watch to learn Data Science WITHOUT CODING.

https://www.youtube.com/watch?v=wPbohKvK0Uw

https://www.youtube.com/watch?v=wPbohKvK0Uw

Python Data Science Handbook: Essential Tools for Working with Data

Everyone must have this book because of the wide, and accurate content. The book takes you to a Data Science course using Python. Even if you are not good at python this will teach you most of it. The content is easy to read, and the book is highly rated and a must-have.

For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.

With this handbook, you’ll learn how to use:

• IPython and Jupyter: provide computational environments for data scientists using Python
• NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python
• Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python
• Matplotlib: includes capabilities for a flexible range of data visualizations in Python
• Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms

Data Science For Dummies (For Dummies (Computer/Tech))

Well, if you are a fan of dummies, this one is for you. And already own purchased a book then this one is for you. Not only does it cover most of the important topics of Data science but also, codings, theories, installations, and good photos to elevate the content.

Not sure what data science is yet? Don’t worry! Parts 1 and 2 of Data Science For Dummies will get all the bases covered for you. And if you’re already a data science expert? Then you really won’t want to miss the data science strategy and data monetization gems that are shared in Part 3 onward throughout this book.

Data Science For Dummies demonstrates:

• The only process you’ll ever need to lead profitable data science projects
• Secret, reverse-engineered data monetization tactics that no one’s talking about
• The shocking truth about how simple natural language processing can be
• How to beat the crowd of data professionals by cultivating your own unique blend of data science expertise 

Whether you’re new to the data science field or already a decade in, you’re sure to learn something new and incredibly valuable from Data Science For Dummies. Discover how to generate massive business wins from your company’s data by picking up your copy today.

Data Science from Scratch: First Principles with Python

This one is for coding lovers. Intense python language coding, and a very good explanation by O’Reilly. You can buy this book even as your first O’reilly book. Great to start, and to learn Data science.

If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with the hacking skills you need to get started as a data scientist. Packed with New material on deep learning, statistics, and natural language processing, this updated book shows you how to find the gems in today’s messy glut of data.

• Get a crash course in Python
• Learn the basics of linear algebra, statistics, and probability—and how and when they’re used in data science
• Collect, explore, clean, munge, and manipulate data
• Dive into the fundamentals of machine learning
• Implement models such as k-nearest neighbors, Naïve Bayes, linear and logistic regression, decision trees, neural networks, and clustering
• Explore recommender systems, natural language processing, network analysis, MapReduce, and databases.

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data – R Language

This book is a great starter for aspiring R – Language learners. The cover all important topics, as well as a good book for Dell EMC Proven Professional Data Science Certification. Data Science and Big Data Analytics book is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software.

This book will help you:

• Become a contributor on a data science team
• Deploy a structured lifecycle approach to data analytics problems
• Apply appropriate analytic techniques and tools to analyzing big data
• Learn how to tell a compelling story with data to drive business action
• Prepare for EMC Proven Professional Data Science Certification

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

The book is not for beginners and covers intermediate to advanced levels of Data Science Topics. If you are determined to choose PyTorch and Scikit-Learn libraries then this book is for you!

Table of Contents

1. Giving Computers the Ability to Learn from Data
2. Training Simple Machine Learning Algorithms for Classification
3. A Tour of Machine Learning Classifiers Using Scikit-Learn
4. Building Good Training Datasets – Data Preprocessing
5. Compressing Data via Dimensionality Reduction
6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
7. Combining Different Models for Ensemble Learning
8. Applying Machine Learning to Sentiment Analysis
9. Predicting Continuous Target Variables with Regression Analysis
10. Working with Unlabeled Data – Clustering Analysis

Computational Statistics in Data Science

n Computational Statistics in Data Science, a team of distinguished mathematicians and statisticians delivers an expert compilation of concepts, theories, techniques, and practices in computational statistics for readers who seek a single, standalone sourcebook on statistics in contemporary data science. The book contains multiple sections devoted to key, specific areas in computational statistics, offering modern and accessible presentations of up-to-date techniques.

Computational Statistics in Data Science provides complimentary access to finalized entries in the Wiley StatsRef: Statistics Reference Online compendium. Readers will also find:

• A thorough introduction to computational statistics relevant and accessible to practitioners and researchers in a variety of data-intensive areas
• Comprehensive explorations of active topics in statistics, including big data, data stream processing, quantitative visualization, and deep learning

Perfect for researchers and scholars working in any field requiring intermediate and advanced computational statistics techniques, Computational Statistics in Data Science will also earn a place in the libraries of scholars researching and developing computational data-scientific technologies and statistical graphics.

Business Intelligence, Analytics, and Data Science: A Managerial Perspective

A managerial approach to understanding business intelligence systems. This book is for aspiring Managers, and senior Managers. The book covers a lot of Business Cases, and applications around Data Ecosystem.

To help future managers use and understand analytics, Business Intelligence provides students with a solid foundation of BI that is reinforced with hands-on practice.

Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights (Pearson Business Analytics Series)

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen — why customers buy more, or why they immediately leave your site — so you can get more behaviors you want and less you don’t. 
Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to:

• Develop complex, testable theories for understanding individual and social behavior in web products 
• Think like a social scientist and contextualize individual behavior in today’s social environments 
• Build more effective metrics and KPIs for any web product or system
• Conduct more informative and actionable A/B tests 
• Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation
• Alter user behavior in a complex web product 
• Understand how relevant human behaviors develop, and the prerequisites for changing them
• Choose the right statistical techniques for common tasks such as multistate and uplift modeling 
• Use advanced statistical techniques to model multidimensional systems 
• Do all of this in R (with sample code available in a separate code manual)

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.

Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.

• Understand how data science fits in your organization—and how you can use it for competitive advantage
• Treat data as a business asset that requires careful investment if you’re to gain real value
• Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way
• Learn general concepts for actually extracting knowledge from data
• Apply data science principles when interviewing data science job candidates.

Deep Learning with Structured Data

This book is an Intermediate to advanced levels of Data Science, it covers almost all levels of Machine Learning – Deep learning concepts with Data sets. Higly recommended book for all everyone.

Table of Contents

1 Why deep learning with structured data?

2 Introduction to the example problem and Pandas dataframes

3 Preparing the data, part 1: Exploring and cleansing the data

4 Preparing the data, part 2: Transforming the data

5 Preparing and building the model

6 Training the model and running experiments

7 More experiments with the trained model

8 Deploying the model

9 Recommended next steps