12 books to strengthen your Data Science foundation

"Success is not final; failure is not fatal: It is the courage to continue that counts."

— Winston S. Churchill

People, Nowadays, are tending to learn from videos. There is nothing wrong in learning from any platform until you are learning from that. But most of the times, people, me, find it challenging to continue their learning without being distracted. Also, nothing can match the knowledge imparted in the books, as video is just made easier to understand for our brain, which is not a problem, but we lack the use of brain.

When you watch a video, the material is simplified for you, and you do not have to think much to absorb the content, however when you read a book, you must build mental images to understand the content, and your brain must process it to ensure that you are fully acquainted with the textual portion.

Here are my top 12 books that I love to read to strengthen my data science fundamentals. I sent them a link to their download page.

photo-1497633762265-9d179a990aa6.avif

1. Python Crash Course

According to it's writer, Python Crash Course is a fast-paced, thorough introduction to Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, you’ll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You’ll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you’ll put your new knowledge into practice with three substantial projects: a Space Invaders–inspired arcade game, data visualizations with Python’s super-handy libraries, and a simple web app you can deploy online.

You can go and download by clicking here.

2. Python Cookbook

This book is ideal if you need assistance building Python 3 programs or updating previous Python 2 code. This unique cookbook is for experienced Python programmers who wish to focus on contemporary tools and idioms. It is packed with practical recipes created and tested with Python 3.3. Inside, you'll discover entire recipes covering the basic Python language as well as tasks common to a wide range of application fields. Each recipe includes code examples that you can use right away in your projects, as well as a description of how and why the solution works.

You can go and download by clicking here.

3. Automate the boring stuff with Python

You can learn how to use Python to develop programs that perform what would take you hours to do by hand without any prior programming knowledge by reading "Automate the Boring Stuff with Python."

You can go and read by clicking here.

4. Introduction to Machine Learning with Python

Although machine learning has become a crucial component of many commercial applications and research initiatives, big businesses with huge research teams are not the only ones working in this sector. This book will teach you useful techniques for creating your own machine learning solutions if you use Python, even as a novice. The applications of machine learning are only limited by your imagination with the amount of data now accessible. You'll discover how to use Python and the scikit-learn package to build an effective machine-learning application. Instead than focusing on the mathematics underlying machine learning algorithms, authors Andreas Muller and Sarah Guido emphasize the practical aspects of employing them. You will benefit even more from this book if you are familiar with the NumPy and matplotlib libraries.

You can go and download by clicking here.

5. Practical Statistics for Data Scientists: 50 Essential Concepts

Data scientists frequently use statistical approaches, although few of them have received any professional training in statistics. Basic statistics is rarely covered from a data science viewpoint in courses or publications on the subject. This helpful manual teaches how to utilize various statistical techniques for data science, shows you how to avoid misusing them, and offers guidance on what matters and what doesn't. Numerous data science resources use statistical techniques but lack a more comprehensive statistical viewpoint. If you are comfortable using the R programming language and have some statistical knowledge,

You can go and download by clicking here.

6. Elements of statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Robert Tibshirani and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in the fields of data mining and machine learning. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. It would be a valuable resource for statisticians and anyone interested in data mining in science or industry.

You can go and read by clicking here.

7. Hands On Machine Learning with Scikit Learn & TensorFlow

This guide shows you how to code programs capable of learning from data. Uses examples, minimal theory and two production-ready Python frameworks — Scikit-Learn and TensorFlow. You'll learn how to use a range of techniques, starting with simple Linear Regression and progressing to Deep Neural Networks.

You can go and download by clicking here. Also, you can checkout github repo of Ageron (book writer), which contain book codes and exercises.

8. Introduction to Statistical Learning: with applications in R

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Text assumes only a previous course in linear regression and no knowledge of matrix algebra. Each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

You can go and download by clicking here Also, visit github repo for solution of code and exercises of the book.

9. Python Data Analytics: Data Analysis and Science Using Pandas, matplotlib, and the Python

Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and analysis tools for the Python programming language. Inside, you will see how intuitive and flexible it is to discover and communicate meaningful patterns of data using Python scripts and reporting systems.

You can go and read by clicking here.

10. Python for Data Analysis_ Data Wrangling with Pandas, NumPy, and IPython

This book is a practical, modern introduction to data science tools in Python. Get started with data analysis tools in the pandas library. Use flexible tools to load, clean, transform, merge, and reshape data. Data files and related material are available on GitHub. Written by Wes McKinney, the creator of the Python pandas project.

You can go and download by clicking here.

11. Deep learning: adaptive computation and machine learning

Deep learning has already proven useful in many software disciplines including computer vision, speech and audio processing, robotics, bioinformatics and chemistry, video games, search engines, online advertising and finance. This book has been organized into three parts in order to best accommodate a variety of readers. We assume familiarity with programming, basic understanding of computational performance issues, complexity theory, introductory level calculus and some of the terminology of graph theory.

You can go and read by clicking here.

12. Think Like a Data Scientist: Tackle the data science process step-by-step

In Think Like a Data Scientist, you'll learn how to think and act like a data scientist. The book will give you a strong foundation for a lifetime of data science learning and practice. You'll explore powerful data science software and put this knowledge together using a structured process for data science.

You can go and download by clicking here.

“Be a positive energy trampoline – absorb what you need and rebound more back.” — Dave Carolan

Follow me on LinkedIn, Twitter,Github, and Hashnode.

photo-1514466256797-efd55fa1bf4e.avif

Did you find this article valuable?

Support Dristanta Silwal by becoming a sponsor. Any amount is appreciated!