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Complete Data Wrangling & Data Visualisation With Python


INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools :
Welcome to the Course
Data & Script For the Course
Python Data Science Environment
For Mac Users
Introduction to IPython/Jupyter
ipython in Browser

Read in Data From Different Sources With Pandas :
What are Pandas?
Read CSV Data
Read Excel Data
Read in HTML Data

Data Cleaning :
Remove NA Values
Missing Values in a Real Dataset
Data Imputation
Imputing Qualitative Values
Theory Behind k-NN Algorithm
Use k-NN for Data Imputation

Basic Data Wrangling :
Basic Principles
Preliminary Data Explorations
Basic Data Handling With Conditional Statements
Drop Column/Row
Change Column Name
Change the Column Type
Explore Date Related Data
Simple Date Related Computations

More Data Wrangling :
Data Grouping
Data Subsetting and Indexing
More Data Subsetting
Extract Information From Strings
(Fuzzy) String Matching
Ranking & Sorting
Concatenate
Merging and Joining

Feature Selection and Transformation :
Correlation Analysis
Using Correlation to Decide Which Features to Retain
Univariate Feature Selection
Recursive Feature Elimination (RFE)
Theory Behind PCA
Implement PCA
Data Standardisation
Create a New Feature

Theory Behind Data Visualisation :
What is Data Visualisation?
Some Theoretical Principles Behind Data Visualisation

Most Common Data Visualizations :
Histograms-Visualize the Distribution of Continuous Numerical Variables
Boxplots-Visualize the Distribution of Continuous Numerical Variables
Scatter plot-Relationship Between Two Numerical Variables
Barplot
Pie Chart
Line Charts
More Line Charts
Some More Plot Types
And Some More

Miscallaneous Information :
Using Colabs as an Online Jupyter Notebook

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