Using Python's Math, Science, and Engineering Libraries

By Larry Fritts

This course covers the following topics: Math, NumPy, SciPy, Matplotlib, Pandas, and SciKit-Image.

5 hours
  • 23 Lessons
  • 5 Hands-On Labs

About the course

This course covers "Exam block #2: Math, Science, and Engineering Tools" for the certification exam: PCPP-32-1: Certified Professional in Python Programming 1 Certification

Topics, as called out in the exam syllabus, are:

  • Math: A basic tool for elementary evaluations
  • NumPy: A fundamental package for scientific computing
  • SciPy: An ecosystem for mathematics, science, and engineering
  • Matplotlib: A 2D plotting library producing publication-quality figures
  • Pandas: A library providing high-performance and data analysis tools
  • SciKit-image: A collection of algorithms for image processing
  • Chapter 1 2 Lessons Getting Started 1:38

    Course Introduction

    0:44

    About the Training Architect

    0:54
  • Chapter 2 1 Lesson Environment Setup 7:23

    Installing Python 3.8 on a Cloud Playground Server

    7:23
  • Chapter 3 2 Lessons Simple Math Using the `math` Module 19:37

    Common `math` Functions and Constants

    19:37

    Calculating the Circumference of a Circle For a List of Radii in Python

    0:00 Hands-On Lab
  • Chapter 4 1 Lesson Using SciPy 3:02

    Overview of SciPy

    3:02
  • Chapter 5 4 Lessons Using NumPy 30:45

    What are NumPy Arrays?

    15:52

    Reshaping a Numpy Array Into a Matrix

    12:28

    Math Operations on Arrays/Matrices

    2:25

    Create a Matrix From Three Arrays in Python

    0:00 Hands-On Lab
  • Chapter 6 5 Lessons Using Pandas 29:09

    Creating and Using DataFrame

    10:08

    Slicing and Dicing DataFrame

    8:43

    Creating Pivot Tables

    3:21

    Stats With Dataframes

    6:57

    Examine a Dataframe and Create a Pivot Table in Python

    0:00 Hands-On Lab
  • Chapter 7 3 Lessons Using Matplotlib 28:11

    What Makes a Good Chart?

    1:14

    Bar Plots, Histograms, and Scatter Plots

    26:57

    Generate Three Types of Charts From Given Data in Python

    0:00 Hands-On Lab
  • Chapter 8 4 Lessons Using SciKit-image 13:15

    NumPy and Scikit-Image

    4:48

    Image Data Types

    2:24

    Transforming Images

    6:03

    Edit a Photo in Python

    0:00 Hands-On Lab
  • Chapter 9 1 Lesson Final Steps 0:34

    What's Next?

    0:34

What are Hands-on Labs

What's the difference between theoretical knowledge and real skills? Practical real-world experience. That's where Hands-on Labs come in! Hands-on Labs are guided, interactive experiences that help you learn and practice real-world scenarios in real cloud environments. Hands-on Labs are seamlessly integrated in courses, so you can learn by doing.

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  • $499 USD per seat per year
  • Billed Annually
  • Renews in 12 months

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