Statistics and Data Visualisation with Python (Chapman & Hall/CRC The Python Series)

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. Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Dedication -- Contents -- 1. Data, Stats and Stories - An Introduction -- 1.1. From Small to Big Data -- 1.2. Numbers, Facts and Stats -- 1.3. A Sampled History of Statistics -- 1.4. Statistics Today -- 1.5. Asking Questions and Getting Answers -- 1.6. Presenting Answers Visually -- 2. Python Programming Primer -- 2.1. Talking to Python -- 2.1.1. Scripting and Interacting -- 2.1.2. Jupyter Notebook -- 2.2. Starting Up with Python -- 2.2.1. Types in Python -- 2.2.2. Numbers: Integers and Floats -- 2.2.3. Strings -- 2.2.4. Complex Numbers -- 2.3. Collections in Python -- 2.3.1. Lists -- 2.3.2. List Comprehension -- 2.3.3. Tuples -- 2.3.4. Dictionaries -- 2.3.5. Sets -- 2.4. The Beginning of Wisdom: Logic & -- Control Flow -- 2.4.1. Booleans and Logical Operators -- 2.4.2. Conditional Statements -- 2.4.3. While Loop -- 2.4.4. For Loop -- 2.5. Functions -- 2.6. Scripts and Modules -- 3. Snakes, Bears & -- Other Numerical Beasts: NumPy, SciPy & -- pandas -- 3.1. Numerical Python - NumPy -- 3.1.1. Matrices and Vectors -- 3.1.2. N-Dimensional Arrays -- 3.1.3. N-Dimensional Matrices -- 3.1.4. Indexing and Slicing -- 3.1.5. Descriptive Statistics -- 3.2. Scientific Python - SciPy -- 3.2.1. Matrix Algebra -- 3.2.2. Numerical Integration -- 3.2.3. Numerical Optimisation -- 3.2.4. Statistics -- 3.3. Panel Data = pandas -- 3.3.1. Series and Dataframes -- 3.3.2. Data Exploration with pandas -- 3.3.3. Pandas Data Types -- 3.3.4. Data Manipulation with pandas -- 3.3.5. Loading Data to pandas -- 3.3.6. Data Grouping -- 4. The Measure of All Things - Statistics -- 4.1. Descriptive Statistics -- 4.2. Measures of Central Tendency and Dispersion -- 4.3. Central Tendency -- 4.3.1. Mode -- 4.3.2. Median -- 4.3.3. Arithmetic Mean -- 4.3.4. Geometric Mean -- 4.3.5. Harmonic Mean "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 that 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"-- Provided by publisher