Data Analysis with Pandas and NumPy in Python [2024]

Course Curriculum

Chapter 1: Introduction

Lecture 1: Why Learn Python ?

Lecture 2: How to make the Best use of this Course

Lecture 3: Installation of Developer Environment -Anaconda & Jupyter

Lecture 4: Crash Course -1 : Python Basic Program

Lecture 5: Crash Course -2 : Python Basic Program

Chapter 2: Python Data Types

Lecture 1: Introduction to Python List

Lecture 2: Understanding Python List

Lecture 3: Introduction to Tuples

Lecture 4: Understanding Tuples

Lecture 5: Introduction to Dictionary

Lecture 6: Understanding Dictionary

Lecture 7: Python Sets

Chapter 3: Control Structure in Python

Lecture 1: If else Conditions – Decisions Making

Lecture 2: Python Loops- Intro

Lecture 3: Understanding For and While Loop

Lecture 4: Python Comprehensions-Intro

Lecture 5: Comprehension ; Loops in List,Dictionary

Lecture 6: Python Function-Intro

Lecture 7: Understanding Python Function

Lecture 8: Map,Reduce,Filter -Introduction

Lecture 9: Map,Reduce ,Filter -Using Lambda Functions

Chapter 4: Python NumPy

Lecture 1: Introduction to NumPy

Lecture 2: Basics of NumPy

Lecture 3: Structures and Contents of Arrays

Lecture 4: Slice and Dice

Lecture 5: NumPy Arrays-Operations

Chapter 5: Python PANDAS

Lecture 1: Introduction to the World of Pandas

Lecture 2: Pandas- Series and Dataframes

Lecture 3: Pandas- File upload and DATA Analysis

Lecture 4: Indexing Dataframe

Lecture 5: Merging Dataframes and Arithmetics

Lecture 6: Summarising , Group by ,Pivoting

Chapter 6: Data Extractions and Website Scrapping

Lecture 1: Intro to Data Extraction

Lecture 2: Reading Delimited files and RDBMS data

Lecture 3: Scrapping Websites

Chapter 7: Data Cleaning for Data Analysis / Business Insights

Lecture 1: Data Cleaning -Introduction

Lecture 2: Imputing Missing Values Techniques – Melbourne Real Estate Data-1

Lecture 3: Imputing Missing Value – Melbourne Real Estate Data-2

Chapter 8: Data Visualisations

Lecture 1: Introduction to the world of Visualisation

Lecture 2: Types of Plots

Lecture 3: Matplotlib Basics

Lecture 4: Matplotlib-Marketing Data Plots ( Boxplots,Histograms,Scatter)

Lecture 5: Searborn-Basics – Beautify the plots ( Boxplots,histograms,scatter )

Lecture 6: Seaborn-Correlation Matrix

Lecture 7: Aggregators Plots ( Bi-variate Analysis ) – Bar Charts , Boxplots , Histograms

Lecture 8: Timeseries Plots – Heatmap Plots

Chapter 9: Extrapolatory Data Analysis – Case Study (Car Price Data Sets )

Lecture 1: Understanding Data and uploading

Lecture 2: Data Manipulation and Analysis-1

Lecture 3: Data Manipulation and Analysis-2

Lecture 4: Data Cleaning

Lecture 5: PPT Presentation to Business Users for Data Insights

Chapter 10: Extrapolatory Data Analysis for Covid 19 ( Case Study )

Lecture 1: Introduction to Case Study for Covid 19

Chapter 11: Bonus Lectures on Machine Learning

Lecture 1: Understanding Machine Learning

Lecture 2: Introduction to Linear Regression

Lecture 3: CSV file Upload and Data Analysis

Lecture 4: Data Visualisations

Lecture 5: Model Building

Lecture 6: Assumptions of Linear Regression

Lecture 7: Model Fitness and Validation – 1

Lecture 8: Model Fitness and Validation – 2

Chapter 12: Investment Case Study for an Asset Management Firm using EXCEL

Lecture 1: Introduction to Investment Case Studies

Lecture 2: Understanding Case Studies and Downloads

Lecture 3: Assignment Solutions:Data Preparation-1

Lecture 4: Data Preparation – 2

Lecture 5: Data Preparation – 3

Lecture 6: Data Preparation – 4

Lecture 7: Data Preparation – 5

Lecture 8: Country Wise Analysis

Lecture 9: Investment Type Analysis

Lecture 10: Company Category Type Analysis

Lecture 11: PPT Presentation to Business Users for Data Insights