A Python Data Analyst’s Toolkit - Gayathri Rajagopalan

A Python Data Analyst’s Toolkit (eBook)

Learn Python and Python-based Libraries with Applications in Data Analysis and Statistics
eBook Download: PDF
2020 | 1st ed.
XX, 399 Seiten
Apress (Verlag)
978-1-4842-6399-0 (ISBN)
Systemvoraussetzungen
56,99 inkl. MwSt
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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 For

Professionals working in the field of data science interested in enhancing skills in Python, data analysis and statistics.




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.


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 LearnFurther your programming and analytical skills with PythonSolve mathematical problems in calculus, and set theory and algebra with PythonWork with various libraries in Python to structure, analyze, and visualize dataTackle real-life case studies using PythonReview 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.
Erscheint lt. Verlag 22.12.2020
Zusatzinfo XX, 399 p. 169 illus.
Sprache englisch
Themenwelt Mathematik / Informatik Informatik Datenbanken
Informatik Programmiersprachen / -werkzeuge Python
Mathematik / Informatik Mathematik Statistik
Schlagworte Bayes Theorem • data analytics • Jupyter • matplotlib • NumPy • Pandas • Python • Regular Expressions • Statistics
ISBN-10 1-4842-6399-5 / 1484263995
ISBN-13 978-1-4842-6399-0 / 9781484263990
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