Coffee Break NumPy: A Simple Road to Data Science Mastery That Fits Into Your Busy Life
- Length: 226 pages
- Edition: 1
- Language: English
- Publication Date: 2019-08-17
- ISBN-10: B07WHB8FWC
- Sales Rank: #749628 (See Top 100 Books)
Fear of missing out in data science?
Coffee Break NumPy is a new step-by-step system to teach you how to learn Python’s library for data science faster, smarter, and better. You simply solve practical Python NumPy puzzles as you enjoy your morning coffee.
Educational research shows that practical low-stake puzzles and tests help you to learn faster, smarter, and better.
Over 100,000 online Python students have already improved their coding and NumPy skills with the unique Finxter.com puzzle-based learning technique:
“It has some real meat to the problems. Thank you so much for doing this project! I love it!”
—David C.
“Another great little Python book from Christian and his colleagues. As a practitioner in this field, I really appreciate the focus on real-world problems. I can see my coffee breaks will be full for some time to come!”
—Chris C.
As you work through Coffee Break NumPy, your NumPy expertise will grow—one coffee at a time. It’s packed with 46 NumPy puzzles, 10 practical learning tips, 1 compressed cheat sheets, and 1 new way to measure your coding skills.
You will train wildly important NumPy topics such as
- NumPy Arrays: creating, basic array arithmetic, one- and multi-dimensional
- Data Types: float, integer, mixed, access, conversion
- Shape and Reshape: manipulating, accessing, axis argument
- Broadcasting: element-wise operations
- Indexing and Advanced Indexing: filtering, Boolean indexing, list indexing
- Slicing: one-dimensional, multi-dimensional, NumPy-specifics
As a bonus, you will track your individual Python coding skill level throughout the book.
To get most out of this book, you already have basic Python skills. For example, you’ve read my book “Coffee Break Python” or similar introductory Python material.
So how do you spend your Coffee Break? Python!
Contents Introduction Why Learn NumPy? A Case for Puzzle-based Learning Overcome the Knowledge Gap Embrace the Eureka Moment Divide and Conquer Improve From Immediate Feedback Measure Your Skills Individualized Learning Small is Beautiful Active Beats Passive Learning Make Code a First-class Citizen What You See is All There is The Elo Rating for Python—and NumPy How to Use This Book The Ideal Code Puzzle How to Exploit the Power of Habits? How to Test and Train Your Skills? What Can This Book Do For You? A Quick Data Science Tutorial: The NumPy Library What is NumPy? What can NumPy do for me? What are the Limitations of NumPy? What are the Linear Algebra Basics You Need to Know? What are Arrays and Matrices in NumPy? What are Axes and the Shape of an Array? How to Create and Initialize NumPy Arrays? How does indexing and slicing work in Python? How Does Indexing and Slicing Work in NumPy? NumPy Cheat Sheet NumPy Basics NumPy 1D Array Creation NumPy 2D Array Creation Extracting Array Dimensionality Accessing Array Shape Averaging 1D Arrays Working with Not a Number the Wrong Way Working with Not a Number the Right Way Creating Numerical Sequences Creating Numerical Intervals Initializing Multi-Dimensional Arrays Revisiting Linear Algebra Understanding the Hadamard Product Broadcasting Practicing Simple Indexing The Boolean Indexing Trick Slicing Matrices Like Paper Simple Array Logic Mastering Slice Assignments Sorting an Array (Part 1) Sorting an Array (Part 2) Computing Array Element Differences Computing Array of Cumulative Sums Linear Algebra and Statistics Calculating 1D Dot Product Multiplying 2D Matrices Enhancing Vector Operations Linear Algebra Made Simple Revisiting Average Reshaping 1D Arrays Averaging 2D Arrays Weighted Averaging Along Axes Calculating 1D Variance Axis Variance of a 2D Array 1D Axis Standard Deviation Practical Data Science Statistical Operations Data Cleaning or Living in an Unperfect World Understandig the Basics of Filters Creating Filters Mastering the Power of Filters Applying Filters Finding Array Elements Leveraging Data Science to Boost Revenues I Leveraging Data Science to Boost Revenues II Finding and Locating Maximum Elements Computing Number of Hospital Patients Finding Chunks of Allocated Memory Giving Meaning to the Mean Final Remarks Your skill level Where to go from here?
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