NumPy for Data Analysis and Data Science: A Complete Hands-On Guide to Fast Numerical Computing, Array Programming, and Real-World Data Projects Using ... Series - Learn. Build. Master. Book) by Muhammad Sohail
English | October 27, 2025 | ISBN: N/A | ASIN: B0FXY3D6XG | 153 pages | EPUB | 3.12 Mb
Mastering NumPy for Data Science and Analysis
A Complete Hands-On Guide to Fast Numerical Computing, Array Programming, and Real-World Data Projects Using Python.
NumPy is the foundation of modern data science, powering libraries like Pandas, MatDescriptionlib, and Scikit-learn.
This book provides a comprehensive, hands-on journey from the basics of NumPy to advanced techniques - helping you build confidence in numerical computing, data manipulation, and efficient analysis using Python.
Each chapter is structured to move you from concepts → code → real-world application, ensuring a smooth learning curve for beginners and a depth of understanding for intermediate learners.
What You'll Learn InsideChapter 1: Introduction to NumPy - why it's essential, how it works, and how to set it up.Chapter 2: Creating and manipulating arrays using functions like array(), arange(), linspace(), zeros(), and ones().Chapter 3: Indexing, slicing, and iterating through arrays with practical selection and filtering techniques.Chapter 4: Mathematical operations - from element-wise arithmetic to universal functions and statistical aggregations.Chapter 5: Advanced array operations - broadcasting, vectorization, combining and splitting arrays.Chapter 6: Random number generation, shuffling, and simulation for testing and data modeling.Chapter 7: Linear algebra with NumPy - matrix multiplication, dot products, determinants, and eigenvalues.Chapter 8: Working with real-world data - loading, saving, cleaning, and preprocessing CSV datasets.Chapter 9: Mini projects - normalization, recommendation matrix, image processing, and stock price simulation.Why This Book Stands OutWritten for beginners to advanced learners - each topic builds progressively.Filled with real-world, data-science-style examples using mini datasets.Every concept is paired with explained code and output walkthroughs.Ends with hands-on mini projects to reinforce learning and develop intuition.Helps you connect NumPy concepts to broader data workflows (like Pandas and MatDescriptionlib).About the Author:Sohail is a Data Scientist and MLOps Engineer with over four years of practical experience. He has worked on projects involving data analysis, machine learning, and automation - and now shares that expertise through this focused, project-driven NumPy guide.
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