Название: Ultimate Data Science Programming in Python: Master data science libraries with 300+ programs, 2 projects, and EDA GUI tools
Автор: Saurabh Chandrakar
Издательство: BPB Publications
Год: 2025
Страниц: 558
Язык: английский
Формат: epub (true)
Размер: 13.2 MB

In today's data-driven world, the ability to extract meaningful insights from vast datasets is crucial for success in various fields. This ultimate book for mastering open-source libraries of Data Science in Python equips you with the essential tools and techniques to navigate the ever-evolving field of data analysis and visualization.

Discover how to use Python libraries like NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization. This book also covers scientific computing with SciPy and integrates ChatGPT to boost your data science workflow. Designed for data scientists, analysts, and beginners, it offers a practical, hands-on approach to mastering data science fundamentals. With real-world applications and exercises, you will turn raw data into actionable insights, gaining a competitive edge. This book covers everything you need, including open-source libraries, Visual Explorer tools, and ChatGPT, making it a one-stop resource for Python-based Data Science.

The first part of the book is dedicated to an in-depth exploration of the NumPy library, laying the groundwork for proficient data manipulation in Python. Readers will explore fundamental concepts such as the creation of NumPy arrays, understanding the distinctions between lists and arrays, and the application of arithmetic operations. The chapter also sheds light on advanced topics like broadcasting and matrix multiplication. With practical examples, this section ensures a comprehensive understanding of NumPy’s capabilities, equipping readers with the skills necessary to efficiently handle numerical data for various data science applications. Then, we will unfold the powerful functionalities of both SciPy and Matplotlib, extending the readers’ capabilities in the realm of scientific computing and data visualization. The exploration of SciPy introduces concepts like optimizers, sparse data handling, graph algorithms, and integration techniques, enabling readers to tackle a broad spectrum of scientific and engineering challenges. Transitioning seamlessly, Matplotlib is unveiled as a quintessential tool for data visualization, covering an array of plots such as line plots, bar plots, pie charts, histograms, scatter plots, and subplots. Various practical examples in this section will provide a comprehensive understanding of how to effectively communicate complex data through visually compelling plots, setting the stage for advanced data exploration.

In the later part of the book, the focus shifts to Pandas, a versatile library for data manipulation and analysis. Readers will master the creation of pandas series and dataframes, along with advanced techniques like filtering, sorting, and aggregation. The exploration extends to the polars library, emphasizing its modern approach to dataframe manipulation and its performance advantages. The chapter on seaborn delves into statistical data visualization, covering essential plots like heatmaps, box plots, and scatter plots. Additionally, readers will discover the capabilities of ChatGPT in conjunction with open libraries of data science. With a rich array of solved examples in this section, readers will acquire a holistic skill set, empowering them to tackle diverse data challenges and innovate in their data science endeavors.

Readers will gain confidence after going through this book and we assure you that all the minute details have been taken into consideration while delivering the content. After reading, learning, and practicing from this book, we are sure that all IT professionals, novices, or job seekers will be able to work on data science projects thus proving their mettle.

What you will learn:

- Learn to work with popular IDEs like VS Code and Jupyter Notebook for efficient Python development.
- Master open-source libraries such as NumPy, SciPy, Matplotlib, and Pandas through advanced, real-world examples.
- Utilize automated EDA tools like PyGWalker and AutoViz to simplify complex data analysis.
- Create sophisticated visualizations like heatmaps, FacetGrid, and box plots using Matplotlib and Seaborn.
- Efficiently handle missing data, outliers, and perform filtering, sorting, grouping, and aggregation using Pandas and Polars.

Who this book is for:
This book is ideal for diploma, undergraduate, and postgraduate students from engineering and science fields to programming and software professionals. It is also perfect for data science, ML, and AI engineers looking to expand their expertise in cutting-edge technologies.






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