# Course curriculum

• 1

### Module 01: Course Introduction

• Segment - 01 - Inroduction
• Segment - 02 - Python Data Science Environment
• Segment - 03 - For Mac Users
• Segment - 04 - Introduction to IPython
• Segment - 05 - IPython in Browser
• Segment - 06 - Python Data Science Packages To Be Used
• 2

### Module 02: Read in Data From Different Sources With Pandas

• Segment - 07 - What are Pandas?
• Segment - 08 - Read in Data from CSV
• Segment - 09 - Read in Excel Data
• Segment - 10 - Read in HTML Data
• 3

### Module 03: Data Cleaning & Munging

• Segment - 11 - Remove Missing Values
• Segment - 12 - Conditional Data Selection
• Segment - 13 - Data Grouping
• Segment - 14 - Data Subsetting
• Segment - 15- Ranking and Sorting
• Segment - 16 - Concatenate
• Segment - 17 - Merging and Joining Data Frames
• 4

### Module 04: Statistical Data Analysis: Basic

• Segment - 18 - What is Statistical Data Analysis?
• Segment - 19 - Some Pointers on Collecting Data for Statistical Studies
• Segment - 20 - Explore the Quantitative Data: Descriptive Statistics
• Segment - 21 - Grouping and Summarizing Data by Categories
• Segment - 22 - Visualize Descriptive Statistics - Boxplots
• Segment - 23 - Common Terms Relating to Descriptive Statistics
• Segment - 24 - Data Distribution- Normal Distribution
• Segment - 25 - Check for Normal Distribution
• Segment - 26 - Standard Normal Distribution and Z-scores
• Segment - 28 - Confidence Interval - Calculation
• Segment - 27 - Confidence Interval - Theory
• 5

### Module 05: Regression Modelling for Defining Relationship between Variables

• Segment - 29 - Explore the Relationship Between Two Quantitative Variables
• Segment - 30 - Correlation Analysis
• Segment - 31 - Linear Regression - Theory
• Segment - 32 - Linear Regression - Implementation in Python
• Segment - 33 - Conditions of Linear Regression
• Segment - 34 - Conditions of Linear Regression - Check in Python
• Segment - 35 - Polynomial Regression
• Segment - 36 - GLM: Generalized Linear Model
• Segment - 37 - Logistic Regression
• 6

### Module 06: Machine Learning for Data Science

• Segment - 38 - How is Machine Learning Different from Statistical Data Analysis?
• Segment - 39 - What is Machine Learning About ?
• 7

### Module 07: Machine Learning Based Regression Modelling

• Segment - 40 - Introduction
• Segment - 41 - Data Preparation for Supervised Learning
• Segment - 42 - Pointers on Evaluating the Accuracy of Classification and Regression Modelling
• Segment - 43 - RF - Regression
• Segment - 44 - Support Vector Regression
• Segment - 45 - Knn - Regression
• Segment - 46 - Gradient Boosting - Regression
• Segment - 47 - Theory Behind ANN and DNN
• Segment - 48 - Regression with MLP