Course curriculum

  • 1

    Welcome to the course!

    • About this course: Overview, Learning Outcomes, Who Should Enroll...
    • Instructor bio - Pramod Gupta
    • Key pointers for this program
    • Joining the Alumni Community
  • 2

    Module 1

    • Module 1 - Introduction to Data Science
    • Segment - 01 - Introduction to the Course
    • Segment - 02 - Data Analysis and Data Mining
    • Segment - 03 - Software Choices for Data Analysis
    • Segment - 04 - What is R
    • Segment - 05 - Steps in the Analytics Exercise
    • Segment - 06 - Analytics Capabilities Framework
    • Segment - 07 - The Job of the Data Scientist
  • 3

    Module 2

    • Module 2 - R Studio and Getting Started with R
    • Segment - 08 - Getting Started with R and Installation
    • Segment - 09 - Things to Know About R and Its Packages
    • Segment - 10 - Getting a Feel for R Some Examples
    • Segment - 11 - Lists and Factors
    • Segment - 13 - DataFrame Basics
    • Segment - 12 - Matrices
    • Segment - 14 - Getting Data Into R and Managing Data
    • Segment - 15 - Loops and Flow Controls
    • Segment - 16 - Functions in R
    • Segment - 17 - What to do When You Need Help
    • Segment - 18 - R and R Studio in Action
    • Segment - 19 - Vectorized Operations
    • Segment - 20 - Matrices in R
  • 4

    Module 3

    • Module 3 - Data Manipulation Techniques - Part 1
    • Segment - 21 - What is Data
    • Segment - 22 - Types of Data Sets
    • Segment - 23 - Preparing Data and Data Integration
    • Segment - 24 - Handling Missing Data and Values
    • Segment - 25 - Handling Outliers
    • Segment - 26 - Data Transformation
    • Segment - 27 - Handling Duplicate Data
  • 5

    Module 4

    • Module 4 - Data Manipulation Techniques - Part 2
    • Segment - 28 - Getting to Know the Data
    • Segment - 29 - Relationships Between Variables
    • Segment - 30 - Data Aggregation
    • Segment - 31 - Sub-setting
    • Segment - 32 - Sorting and Ordering
    • Segment - 33 - Merging Matrices and DataFrames
  • 6

    Module 5

    • Module 5 - Advanced Data Manipulation
    • Segment - 34 - dplyr
    • Segment - 35 - Other Functions in dplyr
    • Segment - 36 - Still More Functions in dplyr
    • Segment - 37 - Apply Function
    • Segment - 38 - What is Data Visualization
  • 7

    Module 6

    • Module 6 - Visualization
    • Segment - 39 - Graphics in R
    • Segment - 40 - Graphing Basics in R
    • Segment - 41 - ggplot
    • Segment - 42 - ggplot2
  • 8

    Module 7

    • Module 7 - Probability and Estimation
    • Segment - 43 - Probability Distributions
    • Segment - 44 - Probability Distributions
    • Segment - 45 - Sampling Techniques
  • 9

    Module 8

    • Module 8 - Statistical Tests and Hypothesis Testing
    • Segment - 46 - Statistical Tests and Hypothesis Testing
    • Segment - 47 - Confidence Intervals
    • Segment - 48 - Determining the Appropriate Tests
    • Segment - 49 - ANOVA - The Analysis of Variance
    • Segment - 50 - Summary of Statistical Tests
  • 10

    Module 9

    • Module 9 - Regression
    • Segment - 51 - Linear Regression and Building Models from Data
    • Segment - 52 - Linear Regression Models
    • Segment - 53 - Multivariate Linear Regression Models
    • Segment - 54 - Data Preprocessing
  • 11

    Labs

    • Labs - Overview
  • 12

    Quizzes

    • Quiz - Overview
  • 13

    Additional Course Readings

    • Articles and downloads
    • Best books on this subject
  • 14

    Final Exam

    • Final Exam: Overview and Instructions
    • Final Exam: Launch here
  • 15

    Recommended Further Readings

    • Articles and Downloads
    • Best books on this subject