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Course sections

Section 1

Introduction to Data Analysis

Instructor and Course Introduction

M1 L1 What is Data Analysis

M1 L2 Introduction to Notebooks

M1 L3 Google Colab Overview

M1 L4 Course Material

Section 2

Introduction to Pandas

M2 L1 Inspecting DataFrame

M2 L2 Some Basic Methods

M2 L3 Sub Setting Columns

M2 L4 Summary Statistics

Section 3

Data Manipulation with Pandas

M3 L1 Selection with loc and iLoc

M3 L2 Slicing and Indexing

M3 L3 Reshaping Data

Section 4

Merging Dataframes with Pandas

M4 L1 Indexing and Reindexing

M4 L2 Concatenating Series

M4 L3 Appending Through Axis

M4 L4 Joining Techniques

M4 L5 Merging Dataframes

Section 5

Data Visualization

M5 L1 Getting started with Matplotlib

M5 L2 Matplotlib Subplots

M5 L3 Matplotlib Interface

M5 L4 Getting started with Seaborn

M5 L5 Seaborn Subplots

M5 L6 Line Plots

M5 L7 Bar Plots

M5 L8 Scatter Plots

M5 L9 Histograms

Section 6

Data Cleaning and Preparation

M6 L1 Handling Missing Data

M6 L2 Visualizing Missing Data

M6 L3 Removing Missing Data

M6 L4 Data Imputation

M6 L5 Removing Duplicate Values

M6 L6 Parsing Dates

M6 L7 Regular Expressions

M6 L8 Type Conversions

Section 7

Introduction to Probability

M7 L1 Sets and Events

M7 L2 Exclusive/Non-Mutually Exclusive Events

M7 L3 Independent/Dependent Events

M7 L4 Laws of Probability

M7 L5 Conditional Probability: Practice Questions 01

M7 L6 Conditional Probability: Practice Questions 02

M7 L7 Law of Total Probability

M7 L8 Bayes Theorem

Course Conclusion

Data Visualization

M5 L4 Getting started with Seaborn

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Data Visualization M5 L3 Matplotlib Interface

Data Visualization M5 L5 Seaborn Subplots