A Python Data Analyst's Toolkit
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics
Published on 22. December 2020
XX, 399 pages
978-1-4842-6399-0 (ISBN)
Description
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
Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.
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
Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.
Reviews / Votes
"It is very well designed for beginners and guides them step by step towards autonomy in using Python. . this book is a good pedagogic tool for those starting to use Python for data analysis, with practical applications and with some review exercises at the end of each chapter. For anyone who wants to start with Python without any knowledge in programming, this book is a good companion and can help the reader to quickly become confident in using Python." (Sébastien Bailly, ISCB News, iscb.info, Vol. 72, December, 2021)More details
Series
Edition
1st ed.
Language
English
Place of publication
Berkeley
United States
Publishing group
APRESS
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
XX, 399 p. 169 illus.
File size
8,24 MB
ISBN-13
978-1-4842-6399-0 (9781484263990)
DOI
10.1007/978-1-4842-6399-0
Schweitzer Classification
Other editions
Additional editions
Gayathri Rajagopalan
A Python Data Analyst's Toolkit
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics
Book
12/2020
APress
€58.84
Shipment within 15-20 days
Person
Gayathri Rajagopalan works for a leading Indian multi-national organization, with ten years of experience in the software and information technology industry. A computer engineer and a certified Project Management Professional (PMP), some of her key focus areas include Python, data analytics, machine learning, and deep learning. She is proficient in Python, Java, and C/C++ programming. Her hobbies include reading, music, and teaching data science to beginners.
Content
Chapter 1: Introduction to Python.- Chapter 2: Exploring Containers, Classes & Objects, and Working with Files.- Chapter 3: Regular Expressions.- Chapter 4: Data Analysis Basics.- Chapter 5: Numpy Library.- Chapter 6: Data wrangling with Pandas.- Chapter 7: Data Visualization.- Chapter 8: Case Studies.- Chapter 9: Essentials of Statistics.
