Studies show that an average person can learn any trick, however small it may be through reading books in the similar way, as they can by watching various videos and tutorials. Reading books or anything that is written down, becomes easier to understand and comprehend as it is taken in a very subjective form of the reader.
In the more IT terms, it is the way people try and install new software into their brains. The field of data science today has been facing a huge demand for professionals, who have the specific skill set of handing great amounts of data and converting them to value. This, in addition to the fact that careers in data science field have been attracting a lot of individuals, which has led to increase in the number of institutions, offering specialized programs in data analytics tools. While institutes like Imarticus Learning offer intensive programs, which meet all the industry standards, there is also a need for a
candidate to depend on books, which enrich their knowledge. Because it is only with books, that one is able to get deeper insights and thus expand their knowledge by applying it to practical learning.
Here are 5 books which every data aspirant must read, while studying to be a Data Scientist.
- Hands On Programming With R
Written by Garrett Grolemund, this book is best suited for beginners who are very new to R Programming. It introduces the reader to various small details of the R environment, with the help of various interesting projects like weighted dice, playing cards, slot machine etc.
- R For Everyone: Advanced Analytics and Graphics
Written by Jared P. Lander, this book covers all the aspects of data science namely, data visualization, data manipulation, predictive modeling, in certain detail. A best read to supplement your learning process, while taking any R Programming specialization course. This book lays a lot of emphasis on usage criteria of algorithms and their examples of implementation.
- Applied Predictive Modeling
Written by Max Kuhn and Kjell Johnson, it is considered to be one of the best books, when it comes to theoretical as well as practical knowledge. There are a number of very important topics discussed in great detail within this book namely, over fitting, feature selection, linear and non linear models, tree methods etc. Max Kuhn is also the creator of the very useful caret package, thus it goes without saying that the book presents great insights on the usage of the same.
- Introduction To Statistical Learning
Written by a number of authors including, Trevor Hastie and Robert Tibshirani, this book is said to be one of the most detailed book on statistical modeling. In this book, a lot of topics are dealt with in detail like linear regression, logistical regression, SVM, unsupervised learning etc. The best part about this book, it is free and as it is all about just basic introductions, any novice can follow this book and find it helpful. Its a great recommendation for anyone new to machine learning, specifically in R.