Pearson Foundational Python for Data Science
By: Kennedy Behrman | Publisher: Pearson India | Year: 2023
ISBN: 9788119847327
MRP: Rs. 450.00
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Description
Data science and machine learning—two of the worlds hottest fields—are attracting talent from a wide variety of technical, business, and liberal arts disciplines. Python, the world's #1 programming language, is also the most popular language for data science and machine learning. This is the first guide specifically designed to help millions of people with widely diverse backgrounds learn Python so they can use it for data science and machine learning.
Leading data science instructor and practitioner Kennedy Behrman first walks through the process of learning to code for the first time with Python and Jupyter notebook, then introduces key libraries every Python data science programmer needs to master. Once you've learned these foundations, Behrman introduces intermediate and applied Python techniques for real-world problem-solving.
Features –
- Master Google colab notebook Data Science programming
- Manipulate data with popular Python libraries such as: pandas and numpy
- Apply Python Data Science recipes to real world projects
- Learn functional programming essentials unique to Data Science
About the Author
Kennedy Behrman is a veteran consultant specializing in architecting and implementing cloud solutions for early-stage startups. He has both undergraduate and graduate degrees from the University of Pennsylvania, including an MS in Computer Information Technology and post-graduate work in the Computer Graphics and Game Programming program. Kennedy has recently acted both as an SME (Subject Matter Expert) for AWS machine learning certification development and as a technical editor for the book Pragmatic AI: An introduction to Cloud-Based Machine Learning (Pearson, 2018).
Content –
I: Learning Python in a Notebook Environment
1. Introduction to Notebooks, 2. Fundamentals of Python, 3. Sequences, 4. Other Data Structures, 5. Execution Control, 6. Functions,
II: Data Science Libraries
7. NumPy, 8. SciPy, 9. Pandas, 10. Visualization Libraries, 11. Machine Learning Libraries, 12. Natural Language Toolkit
III: Intermediate Python
13. Functional Programming, 14. Object-Oriented Programming, 15. Other Topics,