Huge amount of study material is available on internet. Thousands of books on Data science are being sold. Many are embedding the copy paste material and some are even including ready-made codes. However, a good balance of the study material in layman language is required for every beginner to start with data science. Best books for Data Science are difficult to track and going through each and every book is really very difficult. We intend to enlist some very important books which one should go through and in chronological order. As inspired by some Best data scientist of the world, we intend to recommend best books for data science.
Learning Python and R programming language is subjective decision and it has nothing to do with learning data science concepts.
We are going to keep few aspects of data science and how one should go with it, in the beginning. Best books for Data science have been recommended by many different institutes and universities, 70% of the time on the basis of word of mouth and feasibility to purchase. However, in this list, we are going to specify the areas which one should target. The information in this blog will be related to beginning of the course and how to go about it with different study material and standard blogs or tutorials.
Best Books for Data Science
The best books for data science have been enlisted in the circular wheel as you can rotate it. Among all the given books, your first pick should be NumSense: Data Science for Layman: No Math Added. This book I have referred to understand the basics. If you are from computer science background, you need not read this book. But to know the crux of data science field, every engineering guy and mathematician should have knowledge about WHAT DATA SCIENCE ACTUALLY IS!
We have already enlisted the skill set which is required for one to be a data scientist. This gives you the summary of job profiles which one can have as a data scientist. You need to develop this skill set very quickly but steadily. The very next skill set required by any data scientist would be information about linear algebra. This book by Gilbert namely Linear Algebra and it’s Applications has been accepted by wider audience. It has helped me to revise and improve my concepts on linear algebra.
Another important book which I have followed is Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques to Build Intelligent Systems. This book has helped me and all my fellow researchers to understand the concept of machine learning using Python platform. This book eventually boost up your confidence by explaining implementation details of concepts. Also, this has proved to be helpful in research as well as industry.
I would recommend the above three books. After you read these three books, go to next three. Good Luck for a start!
Learning Programming Languages for Data Science
In earlier article, we have already discussed about the Data Science Tools which one should learn in 2019. However, to implement machine learning and deep learning based concepts, we intend to handle the data more dynamically. The programming languages which are most widely used for data science are Python and R. The Python programming language is used usually by computer science aspirants and other engineering aspirants for they have rough idea about programming concepts which they have/ might have read in their first year of engineering. Also, many other MCA/ M.Sc (IT)/ BCA/ B.Sc(IT)/ B.Sc(CS)/ M.Sc.(CS) focus on Python languages. Together all these group of people along with mathematicians and statisticians follow R programming language. This is because R programming language has proved to be best statistical tool to analyse and mine data.
Although all six books enlisted in this list are great picks and the most popular ones. But this depends upon your level of knowledge to pick which book. The most influential book has been Python Machine Learning by Example. This is eventually for those who are starting with machine learning, which has been the biggest platform for analyzing and mining data. Also, in-build libraries in Python helps to fetch results with minimal efforts. Same goes with R programming. However, this is good for those who are working with huge amount of numeric data. For beginners, 83.6% (2834) of the researchers have recommended Hands-On Programming with R: Write Your Own Functions and Simulations book for R programming language.