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)
XX, 399 Seiten
Apress (Verlag)
978-1-4842-6399-0 (ISBN)
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.
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.
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 |
| Haben Sie eine Frage zum Produkt? |
PDF (Wasserzeichen)Größe: 8,2 MB
DRM: Digitales Wasserzeichen
Dieses eBook enthält ein digitales Wasserzeichen und ist damit für Sie personalisiert. Bei einer missbräuchlichen Weitergabe des eBooks an Dritte ist eine Rückverfolgung an die Quelle möglich.
Dateiformat: PDF (Portable Document Format)
Mit einem festen Seitenlayout eignet sich die PDF besonders für Fachbücher mit Spalten, Tabellen und Abbildungen. Eine PDF kann auf fast allen Geräten angezeigt werden, ist aber für kleine Displays (Smartphone, eReader) nur eingeschränkt geeignet.
Systemvoraussetzungen:
PC/Mac: Mit einem PC oder Mac können Sie dieses eBook lesen. Sie benötigen dafür einen PDF-Viewer - z.B. den Adobe Reader oder Adobe Digital Editions.
eReader: Dieses eBook kann mit (fast) allen eBook-Readern gelesen werden. Mit dem amazon-Kindle ist es aber nicht kompatibel.
Smartphone/Tablet: Egal ob Apple oder Android, dieses eBook können Sie lesen. Sie benötigen dafür einen PDF-Viewer - z.B. die kostenlose Adobe Digital Editions-App.
Buying eBooks from abroad
For tax law reasons we can sell eBooks just within Germany and Switzerland. Regrettably we cannot fulfill eBook-orders from other countries.
Mehr entdecken
aus dem Bereich
aus dem Bereich
ein kompakter Einstieg für die Praxis
eBook Download (2024)
Springer Vieweg (Verlag)
CHF 34,15
Arbeiten mit NumPy, Matplotlib und Pandas
eBook Download (2023)
Carl Hanser Verlag GmbH & Co. KG
CHF 29,30
eBook Download (2024)
Carl Hanser Verlag GmbH & Co. KG
CHF 29,30
