The problem companies face today is not the lack of data; on the contrary, it is the massive loads of data that data scientists find difficult to deal with. Big data has disrupted the data science industry as we knew it, including the subjects data scientists engage with. While statistics have not been popular among data scientists in the past, it plays a huge underlying role in better data analysis, prediction and inference. It helps comb through the data and present the findings in a simple manner, thereby identifying hidden patterns and aspects of data, which plays a crucial role in data-driven decisions.
Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
An advanced statistics book, Naked Statistics, has been remarked to make ‘statistics come alive’. The book starts with basic concepts such as normal distribution and moves on to complex topics. Filled with examples and case studies, the book takes a small step away from technical details and focuses on the underlying concepts of statistical analysis. It covers topics like inference, correlation, regression, and practical examples.
But data scientists typically tend to lack the in-depth knowledge in statistics that could further their insight generation. Additionally, given the broad nature of statistics, not everything is relevant to data science. Considering this barrier, Analytics India Magazine has identified the top statistics books catered to data science.
The Signal and the Noise: Why Most Predictions Fail but Some Don’t
by Nate Silver
Tagged as ‘One of the more momentous books of the decade’ by The New York Times Book Review, The Signal and the Noise is a comprehensive guide on making better predictions using statistical models. The book has been deemed to prepare data scientists to communicate their findings clearly and precisely. Nate Silver is a popular blogger known for his baseball performance prediction system and his prediction of the 2008 election, among other works. This book draws on his learnings and guides data scientists on distinguishing ‘true signals’ from noisy data, prediction mistakes to avoid, the prediction paradox and more through excerpts from some of the most successful forecasters in different fields and his real-life experiences. Think Stats
by Allen B. Downey
Think Stats introduces probability and statistics for Python programmers and majorly covers concepts directly related to data science. With Python code examples, Think Stats is catered towards programmers with experience, teaching them statistical concepts through practical data analysis examples and encouraging them to work on real datasets. It is based on Bayesian methods and covers topics like statistical thinking, correlation, hypothesis testing regression, time series analysis, survival analysis, distributions and analytical methods. Downey’s other book, Think Bayes, explores solving statistical problems with Python code.Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
An advanced statistics book, Naked Statistics, has been remarked to make ‘statistics come alive’. The book starts with basic concepts such as normal distribution and moves on to complex topics. Filled with examples and case studies, the book takes a small step away from technical details and focuses on the underlying concepts of statistical analysis. It covers topics like inference, correlation, regression, and practical examples.Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
An advanced statistics book, Naked Statistics, has been remarked to make ‘statistics come alive’. The book starts with basic concepts such as normal distribution and moves on to complex topics. Filled with examples and case studies, the book takes a small step away from technical details and focuses on the underlying concepts of statistical analysis. It covers topics like inference, correlation, regression, and practical examples.Naked Statistics: Stripping the Dread from the Data
By Charles Wheelan
An advanced statistics book, Naked Statistics, has been remarked to make ‘statistics come alive’. The book starts with basic concepts such as normal distribution and moves on to complex topics. Filled with examples and case studies, the book takes a small step away from technical details and focuses on the underlying concepts of statistical analysis. It covers topics like inference, correlation, regression, and practical examples.
