A Python Data Analyst’s Toolkit
Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics
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Beschreibung
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.
You will:
- 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
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
Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.
Explains important data analytics concepts with real-life applications using Python Includes multiple-choice and practice questions to bridge the gap between theory and practice Contains case studies to demonstrate how data analysis skills can be applied to make informed decisions and solve problems
Autor*in
Gayathri Rajagopalan
Themen in »A Python Data Analyst’s Toolkit«
Python Data Analytics Pandas Bayes Theorem Jupyter Regular Expressions Numpy Matplotlib Statistics
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“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)
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