YoVDO

NumPy Essential Training: 2 MatPlotlib and Linear Algebra Capabilities

Offered By: LinkedIn Learning

Tags

NumPy Courses Data Visualization Courses Python Courses Linear Algebra Courses Time Series Analysis Courses Matplotlib Courses Scientific Computing Courses Matrix Decompositions Courses

Course Description

Overview

Learn the skills you need to create your NumPy analytical modules successfully and build professional analytical applications.

Syllabus

Introduction
  • Introduction
  • What you should know
1. Plotting with Matplotlib
  • Why should you use Matplotlib?
  • Matplotlib basics
  • Understanding figures
  • Matplotlib subplots functionality
  • Understanding legends
  • Challenge: Implementing a figure
  • Solution: Implementing a figure
2. Matplotlib Styling and Advanced Plots
  • Colors and styles
  • Advanced Matplotlib commands
  • Adding annotations
  • Creating pie charts and bar charts
  • Advanced plots
3. From Beginner to Advanced NumPy
  • Universal functions
  • Introducing strides
  • Structured arrays
  • Dates and time in NumPy
4. Linear Algebra in NumPy
  • Linear algebra capabilities in NumPy
  • Decomposition
  • Polynomial mathematics
  • Application: Linear regression
Conclusion
  • Next steps

Taught by

Terezija Semenski

Related Courses

Математика и Python для анализа данных
Moscow Institute of Physics and Technology via Coursera
Introduction to Python for Data Science
Microsoft via edX
Python for Data Science
University of California, San Diego via edX
Get Data Off the Ground with Python
George Washington University via Independent
用 Python 做商管程式設計(三)(Programming for Business Computing in Python (3))
National Taiwan University via Coursera