Ultimate Data Science Programming In Python
Download Ultimate Data Science Programming In Python PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Ultimate Data Science Programming In Python book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Ultimate Data Science Programming in Python
Author: Saurabh Chandrakar
language: en
Publisher: BPB Publications
Release Date: 2024-09-25
DESCRIPTION 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. 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. KEY FEATURES ● Master key Python libraries like NumPy, Pandas, and Seaborn for effective data analysis and visualization. ● Understand complex data science concepts through simple explanations and practical examples. ● Get hands-on experience with 300+ solved examples to solidify your Python data science skills. 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. TABLE OF CONTENTS 1. Environmental Setup for Using Data Science Libraries in Python 2. Exploring Numpy Library for Data Science in Python 3. Exploring Array Manipulations in Numpy 4. Exploring Scipy Library for Data Science in Python 5. Line Plot exploration with Matplotlib Library 6. Charting Data With Various Visuals Using Matplotlib 7. Exploring Pandas Series for Data Science in Python 8. Exploring Pandas Dataframe for Data Science in Python 9. Advanced Dataframe Filtering Techniques 10. Exploring Polars Library for Data Science in Python 11. Exploring Expressions in Polars 12. Exploring Seaborn Library for Data Science in Python 13. Crafting Seaborn Plots: KDE, Line, Violin and Facets 14. Integrating Data Science Libraries with ChatGPT Prompts 15. Exploring Automated EDA Libraries for Machine Learning 16. Case Study Using Python Data Science Libraries
Python Data Science Handbook
Author: Jake VanderPlas
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-11-21
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
Python and R for the Modern Data Scientist
Author: Rick J. Scavetta
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2021-06-22
Success in data science depends on the flexible and appropriate use of tools. That includes Python and R, two of the foundational programming languages in the field. This book guides data scientists from the Python and R communities along the path to becoming bilingual. By recognizing the strengths of both languages, you'll discover new ways to accomplish data science tasks and expand your skill set. Authors Rick Scavetta and Boyan Angelov explain the parallel structures of these languages and highlight where each one excels, whether it's their linguistic features or the powers of their open source ecosystems. You'll learn how to use Python and R together in real-world settings and broaden your job opportunities as a bilingual data scientist. Learn Python and R from the perspective of your current language Understand the strengths and weaknesses of each language Identify use cases where one language is better suited than the other Understand the modern open source ecosystem available for both, including packages, frameworks, and workflows Learn how to integrate R and Python in a single workflow Follow a case study that demonstrates ways to use these languages together