Data Science Platform Market was valued at USD 95,3 billion in 2021 and the data science market is estimated to reach – worth $322.9 billion by 2026.
“Torture the data, and it will confess to anything.” (Ronald Coase, Economics, Nobel prize Laureate)
“Without big data analytics, companies are blind and deaf, wandering out onto the web like deer on a freeway.”
~ Geoffrey Moore, management consultant and author of Crossing the Chasm
The U.S. Bureau of Labor Statistics reports that the demand for data science skills will drive a 27.9 percent rise in employment in the field through 2026. Not only is there a huge demand, but there is also a noticeable shortage of qualified data scientists.
What is data science?
Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that combines principles and practices from the fields of mathematics, statistics, artificial intelligence, and computer engineering to analyze large amounts of data.
Read more: Data Science Tips For Beginners
Learn more: Data Science history and Timeline
In-Demand Data Science Careers
If you have a passion for computers, math, and discovering answers through data analysis, then earning an advanced degree in data science or data analytics might be your next step. Data science experts are needed in virtually every job sector—not just in technology. In fact, the five biggest tech companies—Google, Amazon, Apple, Microsoft, and Facebook—only employ one-half of one percent of U.S. employees. However—in order to break into these high-paying, in-demand roles—an advanced education is generally required. Here are some of the leading data science careers like Data scientist, machine learning engineer, machine learning scientist, Applications architect, Enterprise architect, Data architect, infrastructure architect, Data engineer, BI Developer, Data analyst, statistician you can break into with an advanced degree.
Here are the top free Data Science Books for students and people must add to their list in 2023 in order to improve data science skills and to get data science jobs.
1. Practical statistics for data science By Peter Bruce & Andrew Bruce
Statistical methods are a key part of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
2. Learn the Python the right way
Python is the programming language of choice for data science. Learning the fundamentals of programming in this language is therefore one of the first things beginners in the field should learn. This book is not specific to programming for data science but covers the general concepts of writing Python code.
This book assumes no prior knowledge of programming and gives an introduction to the Python language and basic general coding principles. Each chapter contains a complementary Youtube video which helps to further explain the concepts covered.
The authors of the book have also made available a set of hands-on tutorials, containing over 15 practical Python projects to put your learnings into practice.
3. Deep learning for coders with fastai and pytorch: Ai applications without phd
This book is a practical first introduction to deep learning. It is aimed at coders so an understanding of Python programming is essential before diving into this book. However, it does not assume a deep understanding of maths and statistics and includes some excellent and simple explanations of the theory behind deep learning.
4. Python data science handbook
This book is a good and broad introduction to the Python data science toolkit. It covers an introduction to the NumPy library including concepts such as arrays, computations on arrays and data types in Python.
5. Pandas: powerful python data analysis toolkit
The Python package known as Pandas is the tool of choice for exploring, transforming, cleaning and processing data for data science. This book is a complete user guide to the tool. Pandas is a fast, powerful, flexible and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.
6. Hands-on machine learning with scikit-learn, keras and tensorflow
This book provides a detailed and hands-on introduction to machine learning using some of the most popular Python machine learning packages Scikit-learn, Keras and Tensorflow.
This book does a great job of introducing important theoretical concepts of machine learning including types of machine learning systems, overfitting and underfitting, and descriptions of how the common algorithms work.
7. The Field Guide to Data Science by Booz Allen Hamilton
This book built by several hands by Booz Allen Hamilton employees introduces the theme of Data Science, presents the tools necessary to work with the area, and expands the background a little. It basically works as an introduction to the subject, but it is very well written, with infographics and illustrations that are especially creative. And there is a section that should be printed by everyone working in the field, a guide on how to choose the right technique for each piece of the problem.
8. Data Science for Business by Tom Fawcett and Foster Provost
The book 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.
9. Deep Learning with Python by Francois Chollet
This book is an alternative way to learn Deep Learning than from learning through the book written by the Keras creator and Google AI Researcher Francois Chollet? The book deep dives into Deep Learning and teaches concepts by practically implementing in python in Python.
10. Artificial Intelligence: Foundations of Computational Agents, 2nd Edition by David Poole, Alan Mackworth
The book balances theory and experiment, showing how to link them intimately together. It develops the science of AI together with its engineering applications.
11. Deep Learning (Adaptive Computation and Machine Learning series) by IAN Goodfellow
If a man like Elon Musk says that this is the most comprehensive book on the topic, we don’t think you need to refer to any further sources for the topic.
12. An Introduction to Statistical Learning
by Gareth M. James, Daniela Witten, Trevor Hastie, Robert Tibshirani
An all-time classic. This book is recommended or referenced in most machine learning courses I’ve come across, it’s just that well written. It covers basic statistics as well as machine learning techniques.