Data Analytics - Roy Jafari

Welcome to Online Data Analytics Course

Using Python: Pandas/Matplotlib

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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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