Explore the fundamentals of data analysis and statistics with case studies using Python. This
book will show you how to confidently write code in Python and use various Python libraries
and functions for analyzing any dataset. The code is presented in Jupyter notebooks that can
further be adapted and extended. This book is divided into three parts - programming with
Python data analysis and visualization and statistics. You'll start with an introduction to
Python - the syntax functions conditional statements data types and different types of
containers. You'll then review more advanced concepts like regular expressions handling of
files and solving mathematical problems with Python. The second part of the book will cover
Python libraries used for data analysis. There will be an introductory chapter covering basic
concepts and terminology and one chapter each on NumPy(the scientific computation library)
Pandas (the data wrangling library) and visualization libraries like Matplotlib and Seaborn.
Case studies will be included as examples to help readers understand some real-world
applications of data analysis. The final chapters of book focus on statistics elucidating
important principles in statistics that are relevant to data science. These topics include
probability Bayes theorem permutations and combinations and hypothesis testing (ANOVA
Chi-squared test z-test and t-test) and how the Scipy library enables simplification of
tedious calculations involved in statistics. What You'll Learn Further your programming and
analytical skills with Python Solve mathematical problems in calculus and set theory and
algebra with Python Work with various libraries in Python to structure analyze and visualize
data Tackle real-life case studies using Python Review essential statistical concepts and use
the Scipy library to solve problems in statistics Who This Book Is ForProfessionals working in
the field of data science interested in enhancing skills in Python data analysis and
statistics.
