Data Scientists extract unstructured and structured data and process it through analytics into the proper format. For being a Data Scientist one needs to have proper thinking and analytical skills or else you’ll fail to become a data scientist. Data Science is a conceptual interpretation to unite statistics and analyse data with the related methods. The brainchild is to understand and employ techniques and methods that used by a data scientist to structurize the data to make it useful.
The broader fields of understanding what data science includes mathematics, statistics, computer science and information science. For those who want to make their career as a Data Scientist or in Data Analytics then you need to have a very strong background in statistics and mathematics as the big companies will always give preference to those with good analytical and statistical skills. Therefore, if one already has those skills, good but otherwise you’ll have to develop those skills and for that, you can read books that would enhance your knowledge about data science statistics.
Data Science Statistics Books are Mentioned as Follows:
1) Data Science from Scratch 
Author- Joel Grus
About the Book- Joel Grus is a software engineer at Google. He has previously been a Data Scientist at startups as well. This book is for people who’ve just started to develop their skills for Data Science. So this book is going to be a great help to freshers. You’ll get a basic knowledge of Data Science and machine learning as and when you complete this book.
2) A Layperson’s Guide to Understanding Research and Data Analysis
Author- Lynda Rose Bruce Edd
About the Book- This book is made for people who have been busy and still want to dig their mind into becoming a Data Scientist. The idea behind writing this book is to make people understand what they can do with so much of information that’s been flooding them and what all they can make out of it. This book when completely read is going to give you a proper analytical and statistical skill of what you can do with the information you have.
3) Data Analytics: Essentials to M
aster Data Analytics and Get Your Business to the Next Level
aster Data Analytics and Get Your Business to the Next LevelAuthor- Scott Harvey
About the Book- This is an in-depth description of what and when you can make of data analytics and how can you use it in form of Data Science. This is not exactly about work of a Data Scientist but is going to help you in going about it. You’ll also get a chance to utilise the tips and techniques in business at large.
4) Data Analytics: Practical Guide to Leveraging the Power of Algorithms, Data Science, Data Mining, Statistics, Big Data, and Predictive Analysis to Improve Business, Work, and Life
Author- Arthur Zhang
About the Book- Data science is expanding in breadth and growing rapidly in importance as technology rapidly integrates ever deeper into business and our daily lives. The need for a succinct and informal guide to this important field has never been greater. Therefore, this book is going to make you engross all the vital information regarding the same.
5) Principle of Data Science
Author- Sinan Ozdemir
About the Book- This book is a must if you want to turn your programming skills effective in relation to Data Science. This going to help you in joining dots in relation to mathematics, programming and business analysis.
6) Statistical Methods for Spatial Data Analysis
Author- Carol A. Gotway
About the Book- This book is going to give you an understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialised analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data.
7) Statistics: A Very Short Edition 
Author- David J. Hand
About the Book- Statistical ideas and methods underlie just about every aspect of modern life. From randomised clinical trials in medical research to statistical models of risk in banking and hedge fund industries to the statistical tools used to probe vast astronomical databases, the field of statistics has become centrally important to how we understand our world. But the discipline underlying all these is not the dull statistics of the popular imagination. Long gone are the days of manual arithmetic manipulation.
Must Read- Data Science Statistics Blogs
1) Blog About Stats
This blog by Armin Grossenbacher is going to help you with all the professional help that you need while disseminating the official statistics. To know more, go to his blog (https://blogstats.wordpress.com/).
2) DecisionStats
The author of this blog is Ajay Ohri and he is very active on the blog which makes it altogether more effective in learning for the reader as he is always updated with the new things. Go and find more about his (blog https://decisionstats.com/).
3) Error Statistics Philosophy
This blog is regulated by Virginia Tech statistical philosopher Deborah G. Mayo and is going to be very useful in your professional life. Find more on (https://errorstatistics.com/).
4) R Statistics
This blog by Tal Galili, a PhD student in Statistics at the Tel Aviv University and also works as an assistant for teaching statistics courses in the university. This blog is going to help you with the language R and the statistical knowledge related to it. ( https://www.r-statistics.com/)
Is Statistics Needed for Data Science
Statistics has a very wide horizon and that is how one can applicate it in data science statistics. Statistics is the study of the collection, organisation analysis, interpretation and organisation of data. Therefore, data scientists need to know statistics. Data Analysis requires descriptive statistics and probability theory that can help one in making better business decisions.
The conceptual inclusion of key areas includes statistical significance, hypothesis testing, regression and probability. Machine learning is also another thing that one would need to get into the field. There is so much that you need to explore but do not worry we are going to help you with it.
The Best Way to learn Data Science Statistics
Learning Data Science Statistics is not a very difficult thing, you just need to follow these simple steps. Also, key statistical concepts are to be learned with the help of coding and this is definitely going to be very interesting. The steps are mentioned as follows:
- Core Statistics Concepts – These include descriptive statistics, distributions, hypothesis testing, and regression.
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Bayesian Thinking- This step will include Conditional probability, priors, posteriors, and maximum likelihood.
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Intro to Statistical Machine Learning- This going to be about learning basic machine concepts and how statistics fits in.
Once you finish these three steps, then you can jump onto more serious issues and you’ll focus mostly on machine learning.
1) Core Statistics Concepts
A data scientist might need a few things that are going to help you in working well as a data scientist.
- Experimental Design– If your company is introducing a brand new product line and is selling it through retail stores then you need to estimate that how and where to put the product across geographies for best results.
- Regression Modelling– You need to bud a better product demand and for that, you need to predict better individual product lines to fit in according to the requirements. For this, you need to build multiple regression models.
- Data Transformation– To put in efforts to structure multiple machine learning models that can be intensified and used properly.
2) Bayesian Thinking
There is always a philosophical debate in between Bayesian and frequentist but bayesian is more relevant for data science. Frequentists use probability only to model sampling processes. This means they only assign probabilities to describe data they’ve already collected.
On the other hand, Bayesians use probability models and quantify the uncertainty before the collection of data. In this level of uncertainty before the collection of data is called the prior probability.
3) Introduction to Statistical Machine Learning
If you want to learn statistics for data science, there is no better way than playing with machine learning models. Once you are well versed with Bayesian you can jump onto this but make sure that you’ve learned the core concepts of the earlier one. Implement a few machine learning models from the beginning, this will help you grip the understanding of their underlying mechanics. When at this stage, it is acceptable to just copy steps, line by line. This is the best way to open up towards machine learning and grip on the understanding of statistics that is required for data science.
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