Data Analysis With Python For Beginners: Learn By Practice

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction to data analysis

Lecture 2: Installing required tools

Chapter 2: Introduction to Numpy

Lecture 1: Creating a Numpy Array

Lecture 2: Arithmetic operations betweeen Numpy array

Lecture 3: Indexing & Slicing a Numpy array

Lecture 4: Practice Example 1

Lecture 5: Practice example 1 solution

Lecture 6: Practice Example 2

Lecture 7: Practice example 2 solution

Lecture 8: Practice Example 3

Lecture 9: Practice example 3 solution

Lecture 10: Practice Example 4

Lecture 11: Practice example 4 solution

Chapter 3: Series & Data Frames

Lecture 1: Creating a Series

Lecture 2: Creating a Data Frame

Lecture 3: Reindexing a DataFrame and Series

Lecture 4: Practice Example 5

Lecture 5: Practice example 5 solution

Lecture 6: Practice Example 6

Lecture 7: Practice example 6 solution

Lecture 8: Practice example 7

Lecture 9: Practice example 7 solution

Lecture 10: Practice example 8

Lecture 11: Practice example 8 solution

Chapter 4: Arithmetic between Data Frame & Series

Lecture 1: Arithmetic methods with fill values

Lecture 2: Arithmetic between Data Frame and Series.

Lecture 3: Practice example 9

Lecture 4: Practice example 9 solution

Lecture 5: Practice example 10

Lecture 6: Practice example 10 solution

Chapter 5: Functions, Sorting & Ranking

Lecture 1: Function application and mapping

Lecture 2: Sorting and ranking

Lecture 3: Duplicate values

Lecture 4: Practice example 11

Lecture 5: Practice example 11 solution

Lecture 6: Practice example 12

Lecture 7: Practice example 12 solution

Chapter 6: Handling Data & Indexing

Lecture 1: Handling missing data

Lecture 2: Hierarchical indexing

Lecture 3: Practice example 13

Lecture 4: Practice example 13 solution

Lecture 5: Practice example 14

Lecture 6: Practice example 14 solution

Lecture 7: Practice example 15

Lecture 8: Practice example 15 solution

Chapter 7: Data Loading

Lecture 1: Introduction to data loading

Lecture 2: Reading data from multiple files

Lecture 3: Reading partial data from files

Lecture 4: Practice example 16

Lecture 5: Practice example 16 solution

Chapter 8: Merging & Reshaping

Lecture 1: Perform merge on datasets

Lecture 2: Merge with multiple keys

Lecture 3: Concat

Lecture 4: Combining values with overlap

Lecture 5: Reshaping

Lecture 6: Practice example 17

Lecture 7: Practice example 17 solution

Lecture 8: Practice example 18

Lecture 9: Practice example 18 solution

Lecture 10: Practice example 19

Lecture 11: Practice example 19 solution

Lecture 12: Practice example 20

Lecture 13: Practice example 20 solution

Lecture 14: Practice example 21

Lecture 15: Practice example 21 solution

Lecture 16: Practice Example 22

Lecture 17: Practice example 22 solution

Lecture 18: Practice Example 23

Lecture 19: Practice example 23 solution

Lecture 20: Practice Example 24

Lecture 21: Practice example 24 solution

Lecture 22: Practice Example 25

Lecture 23: Practice example 25 solution

Chapter 9: Data Visualization

Lecture 1: Introduction to matplotlib

Lecture 2: Plotting subplots

Lecture 3: Plotting multiple figures

Lecture 4: Adding text

Lecture 5: Plotting DataFrame & Series

Lecture 6: Practice Example 26

Lecture 7: Practice example 26 solution

Lecture 8: Practice Example 27

Lecture 9: Practice example 27 solution

Lecture 10: Practice Example 28

Lecture 11: Practice example 28 solution

Lecture 12: Practice example 29

Lecture 13: Practice example 30

Lecture 14: Practice example 30 solution

Chapter 10: Data Transformation

Lecture 1: Data Transformation part 1

Lecture 2: Data Transformation part 2

Lecture 3: Filtering outliers