Best Books To Read In Data Science And Machine LearningDecember 2, 2016
Now the above title must be really very confusing and contradictory according to a lot of us. The major reason for this would be the fact that, data science in all its singularity, is a field which deals with the entire of virtual space. There are various data analytical tools such as SAS Programming, R Programming, Hadoop, Tableau, SQL, the list is endless; these tools are used to turn volumes of data into valuable insights, which help businesses and firms grow manifold. While many may argue that id the whole landscape of the field of data science is so virtual and online, then why would there be a need to have paper books written and refereed to? While attending various webinars, reading e-books, watching tutorials and so on makes absolute sense, similar is the case with books. First of all gaining information is the most important part, when it comes to researching any field, even more so with this new and upcoming field of data science. Books offer an individual to dedicate all their attention and focus to one source, devoid of any sort of distractions, including ads, pop ups and so on.
While it isn’t common knowledge, that there is a tonne of informative literature present on data science, which lets both beginners and seasoned professionals, get deeper understanding of the field of data science.
Here’s a list of books, which are highly recommended by Imarticus Learning, is a award winning education institute of Data Analytics In India. There recommendations are to both beginners as well as seasoned professionals in the field of Data Science.
Big Data A Revolution That Will Transform How We Live, Work And Think
This book is jointly authored by Viktor Mayer-Schonberger and Kenneth Cukier, both of who also happen to be two of the most popular experts in the field of data science. It consists of various intricacies of big data and the reality of the data driven world is explored herein.
Doing Data Science: Straight Talk From The Frontline
Authored by Cathy O’Neil and Rachel Schutt, this book is the go-to piece of reading for anyone who wants to know more about statistical inference, algorithms, spam filters, Naive Bayes, Data Visualization, Social Networks, Data Journalism, Financial Modeling, Logistic Regression, Data Engineering, MapReduce, Pregel, Hadoop and so on. This makes for a great read for those interested in finance as well as analytics industry.
Data Science For Business
This book is an essential read, as it explores the various real-time business problems that will arise in the worlds and how one can effectively make use of data analytics to tackle the same. This book is written by Foster Provost and is definitely a must read for data science enthusiasts.
Machine Learning For Hackers
Written by Drew Conway and John Myles White, this book is extremely ideal for programmers from any background of data science, which includes business, government, academics and so on.
The Signal And the Noise: Why So Many Predictions Fail-But Some Don’t
This is a book that makes for a must read for anyone who is interested in the intricacies of predictive analytics and why it works the way it does. Authored by Nate Silver, who is also known as the best political forecaster of his time, this book goes on to highlight the major mistakes, in the process of predictive analysis.