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Practical Data Pre-Processing & Visualization Training With Python
INTRODUCTION TO THE COURSE: The Key Concepts and Software Tools
Welcome to the Course (2:01)
Data & Script For the Course
Python Data Science Environment (10:57)
For Mac Users (4:05)
Introduction to IPython/Jupyter (19:13)
Read in Data From Different Sources With Pandas
What are Pandas? (12:06)
Read CSV (5:42)
Read Excel (5:31)
Data Clean
Remove NA Values (10:28)
Missing Values in a Real Dataset (6:04)
Data Imputation (9:07)
Imputing Qualitative Value (3:27)
Theory Behind k-NN Algorithm
Use k-NN for Data Imputation (6:23)
Basic Data Wrangling
Basic Principles (4:20)
Preliminary Data Explorations (8:17)
Basic Data Handling With Conditional Statements (5:24)
Drop Column/Row (4:42)
Change Column Name (3:35)
Change the Column Type (3:50)
Explore Date Related Data (4:02)
Simple Date Related Computations (3:46)
More Data Wrangling
Data Grouping (9:47)
Data Subsetting and Indexing (9:44)
More Data Subsetting (8:54)
Extract Information From Strings (4:40)
(Fuzzy) String Matching (2:39)
Ranking & Sorting (8:03)
Concatenate (8:16)
Merging and Joining (10:47)
Feature Selection and Transformation
Correlation Analysis (8:26)
Using Correlation to Decide Which Features to Retain (5:00)
Univariate Feature Selection (4:56)
Recursive Feature Elimination (RFE) (4:26)
Theory Behind PCA (2:37)
Implement PCA (3:53)
Data Standardisation (4:10)
Create a New Feature (6:16)
Theory Behind Data Visualisation
What is Data Visualisation? (9:33)
Some Theoretical Principles Behind Data Visualization (6:46)
Most Common Data Visualisations
Histograms- Visualize the Distribution of Continuous Numerical Variables (12:13)
Boxplot- Visualise Data Distribution (5:54)
Scatter Plot: Relationship Between Variables (11:57)
Barplot (22:25)
Pie Chart (5:29)
Line Chart (12:31)
More Line Charts (2:32)
Some More Plot Types (11:14)
And Some More (8:40)
Use k-NN for Data Imputation
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