1st Edition

# Statistics and Data Visualisation with Python

554 Pages 4 Color & 73 B/W Illustrations
by Chapman & Hall

554 Pages 4 Color & 73 B/W Illustrations
by Chapman & Hall

554 Pages 4 Color & 73 B/W Illustrations
by Chapman & Hall

Also available as eBook on:

This book is intended to serve as a bridge in statistics for graduates and business practitioners interested in using their skills in the area of data science and analytics as well as statistical analysis in general. On the one hand, the book is intended to be a refresher for readers who have taken some courses in statistics, but who have not necessarily used it in their day-to-day work. On the other hand, the material can be suitable for readers interested in the subject as a first encounter with statistical work in Python. Statistics and Data Visualisation with Python aims to build statistical knowledge from the ground up by enabling the reader to understand the ideas behind inferential statistics and begin to formulate hypotheses that form the foundations for the applications and algorithms in statistical analysis, business analytics, machine learning, and applied machine learning. This book begins with the basics of programming in Python and data analysis, to help construct a solid basis in statistical methods and hypothesis testing, which are useful in many modern applications.

1. Data, Stats and Stories - An Introduction

2. Python Programming Primer

3. Snakes, Bears & Other Numerical Beasts: NumPy, SciPy & Pandas

4. The Measure of All Things - Statistics

5. Definitely Maybe: Probability and Distributions

6. Alluring Arguments and Ugly Facts - Statistical Modelling and Hypothesis Testing

7. Delightful Details - Data Visualisation

8. Dazzling Data Designs - Creating Charts

A. Variance: Population v Sample

B. Sum of First n Integers

C. Sum of Squares of the First n Integers

D. The Binomial Coefficient

E. The Hypergeometric Distribution

F. The Poisson Distribution

G. The Normal Distribution

H. Skewness and Kurtosis

I. Kruskal-Wallis Test - No Ties

### Biography

Dr. Jesús Rogel-Salazar is an accomplished technologist with over 20 years' experience in the area of data science and machine learning. He obtained his PhD in quantum atom optics at Imperial College in the group of Professor Geoff New and in collaboration with the Bose–Einstein Condensation Group in Oxford with Professor Keith Burnett. After completion of his doctorate, he worked in the Centre for Cold Matter at Imperial and moved on to the Department of Mathematics in the Applied Analysis and Computation Group. Further to his academic career, Dr. Rogel-Salazar has held positions as a data scientist with AKQA, IBM Data Science Studios, Barclays, Dow Jones, Prudential and Tympa Health Technologies. He is the author of three books published with CRC Press; the latest two are entitled Data Science and Analytics with Python and Advanced Data Science and Analytics with Python.