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