The ONE book you MUST read for Statistics!
Statistics is undeniably one of the pillars of Data Science and it has been around way before programming and computers came around. Statistics are important to be learned because of the wide application of the subject. When you approximate your expenses for a month or when you try to find out what your Body Mass Index should be when compared to everyone else of your age and height, that’s statistics too
Every subject today, NEEDS statistics.
Why?
Because FACTS are believable. If factual knowledge of a subject can be provided with the use of certain statistics, the knowledge gained becomes unparalleled.
Let’s dive right into the one book you must read for Statistics.
P.S. It’s one book but with three parts, bear with me. You’ll see how cool they are soon.
Behold.
Making Sense of Data I: A practical guide to EDA and Data Mining
and
Making Sense of Data II: A practical guide to Data Visualization, Advanced Data Mining Methods, and Applications
by Glenn J Myatt, Wayne P Johnson published by Wiley
The book is true to its title and does do justice to it by including almost all things necessary about descriptive and diagnostic analytics with a touch upon Predictive analytics where required to ensure that the readers are given a holistic view of what can be understood out of raw data whilst showing the demonstrations of methods on numerous examples as well.
The book comes with a third volume too! Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations, which I suggest you check out if you want to understand how to make amazing Interactive data visualizations using ggplot2 and Protovis.
Though Tableau and Power Bi take the throne when it comes to interactive data visualizations, don’t hesitate to check out the third book if you’re interested. Find more about it here:
Let’s get back to the two volumes in consideration. What does each book have to offer? Here’s a list of some amazing things the books touch on.
Making Sense of Data: Contents
Part One
Introduction
Describing Data
Preparing Data Tables
Understanding Relationships
Identifying and Understanding Groups
Building Models from Data
Appendix — Hands-On TutorialsPart Two
Introduction
Data Visualization
Clustering
Predictive Analytics
Applications
Appendix — Matrices
Appendix — Software
What does each book cover specifically?
The first part starts right from what observations are, what are the different types of variables, central tendencies, confidence intervals, and hypothesis testing to cleaning data, typecasting data, visualizing relationships between variables, association rules, and decision trees and ends with touching upon various algorithms like Linear Regression, Logistic Regression, k Nearest neighbors, Classification and Regression trees, etc.
If you think that’s a lot of knowledge, we’ve only covered one part of the book.
The second part kicks off with an introduction, starts with data viz for univariate, bivariate, and multivariate data with examples for each, goes on to talk about Dendrograms and cluster images in data viz for groups, and covers methods like Agglomerative Hierarchical Clustering, Partition based clustering, Fuzzy Clustering, moves on to more advanced algorithms like PCA i.e. Principal Component Analysis, Multilinear Regression, Discriminant Analysis to Naive Bayes Classifiers.
All of this in just 250 pages *part one* and 298 pages *part two*.
This article is my review of the book because it is just so useful. I would recommend anyone starting with Data Science and needs a good “better than average” book on Statistics to read this one.
- You can find the book online in a PDF format if you spend a little time on a good Google Search otherwise it is available on Amazon!
Read the book and let me know what you think of it in the comments below!
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