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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 2)
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, Matplotlib, 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 Inside
Chapter 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 Out
Written 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 Matplotlib).
About the
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.
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, Matplotlib, 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 Inside
Chapter 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 Out
Written 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 Matplotlib).
About the
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.
153 pages, Kindle Edition
Published October 27, 2025
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