Welcome to Free Online Data Analytics Course

Using Python: Pandas/Matplotlib

Module 1

Introduction to Jupyter Notebook

Intro to Jupyter Notebook 

Length 10:41 min

Module 2

Python Basics

Python Basics - Data Variables 

Length 18:48 min

Python Basics - Loops and Conditionals 

Length 21:53 min

Challenge Questions 

Length 3:31 min

The solution to challenges 

Length 5:51 min

Module 3

Python Basics Ⅱ

Functions 

Length 11:58 

Lambda Functions 

Length 7:35

Solutions to the Challenges 

Length 4:46 

Module 4

Introduction to NumPy

NumPy Intro 

Length 16:29 

Randomization, Sorting and Boolean Masking 

Length 13:12 

Challenge Questions 

Length 5:03 

Solutions to the Challenges 

Length 16:30 

Module 5

Pandas Ⅰ

Intro to Pandas 

Length 15:39 

Pandas with an example dataset 

Length 20:24

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Challenge #1 

Length 3:02 

Challenge #2

Length 6:28

Challenge #3

Length 4:32

Challenge #4

Length 4:26 

Module 6

Pandas Ⅱ

Data Cleaning Example 

Length 14:44 

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Pandas Loop and Apply function 

Length 16:03 

Pandas Groupby 

Length 12:49 

Module 7

Matplotlib

Matplotlib Intro

Length 21:41

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Matplotlib Design the Visual 

Length 13:19  

Matplotlib Subplots 

Length 14:06 

Module 8

Melt/pivot functions

Get to know billboard dataset 

Length 04:46 

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Pandas Melt function 

Length 19:07 

Pandas Pivot function 

Length 13:11

Module 9

5 Dimensional Scatterplots

Get to know the GapMinder dataset 

Length 10:19 

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4D Scatter Plot

Length 17:06 

5D Scatter Plot 

Length 9:28

Module 10

Summarizing and comparing populations vis Boxplots and Histograms

Box-plot 

Length 11:42

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Histograms

Length 11:40

Module 11

Relating categorical attributes vis Bar charts

Get to Know Whickham dataset 

Length 09:35

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Multi-level columns and indexes 

Length 6:33 

Bar-char

Length 22:11 

Module 12

Case Study Ⅰ – Preprocessing Example

Get to know electricity consumption data 

Length 17:08

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Missing values

Length 11:40 

Outliers

Length 20:20 

Module 13

Case Study Ⅱ – Pattern Recognition via Visualization

Visualize to recognize patterns

Length 15:03 

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Integrate to Enrich Visual

Length 10:08 

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Module 14

Dimension Reduction

Redundancy Analysis

Length 21:41

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Principal Component Analysis

Length 17:18

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