Course Objectives

Pre-course preparations

• Installing the latest R version or ≥ 3.5 (https://cloud.r-project.org)
• Intalling the latest RStudio Desktop or ≥1.1 (https://www.rstudio.com/products/rstudio/download/)
• Installing latest Git or ≥ 2.7
  • Also install Git Bash for windows

R packages to isntall

During this course we will be using few packages that do not come with the base R. Therefore, I have put a chunk of code in the requirements.txt that will install all the necessary packages for you. All you have to do is open the R-base and copy and paste the content of the file.

Note: the file has a long list and installing all these packages might take sometime. Be patient.

Outline

• Introduction to Data Science – W1S1
  • Motivation to learn Data Science
  • Definition of Data Science
  • Types of Data
  • Ranks of Data
  • Tasks of Data Scientists
• CRISP-DM – W1S1
  • Business (Organizational) Understanding
  • Data Understanding
  • Data Preparation (Course Focus)
  • Modeling (Course Focus)
  • Evaluation (Course Focus)
  • Deployment
• Data Scientist ToolBox – W1S2
  • Git & GitHub practical Application
    • Initializing repos
    • Understanding the life cycle of files in git
    • Viewing History
    • Undoing things in git
    • Creating Branches
    • Merging Branches & Resolving conflicts
    • Adding Remote Repos
    • Pulling and Pushing files to remotes
    • Tracking Branches
  • R Programming Foundations W1S3
    • R Basics
      • Basic Math
      • Variables
      • Data Types
      • Vectors
      • Calling Functions
      • Function Documentation
      • Missing Data
      • Pipes
    • R beyond basics
      • Other data Types (Data.Frames, Lists, Matrices, Arrays)
      • Writing Functions
      • Control Statements & Loops
      • Reading Data into R
        • CSV, Excel
        • R Binary data
      • Graphing in R
        • Base Graphs
        • ggplot2
  • Rstudio IDE workspace.
    • Viewing data
    • Creating Projects
    • Modifying your global options
  • Creating Documents with R (Optional)
• Introduction to Statistics
  • Measure of Centrality
  • Measure of Variability
  • Probability Distributions
  • Confidence Intervals
• Data Preprocessing
  • Missing Data
  • Outliers
  • Inconsistent Data
  • Incorrect Data
• Supervised Models
  • Linear Regression
  • Logistic Regression
  • Elastic Nets
  • Decision Trees
  • Xgboost classifiers
  • Random Forest (brief)
• Unsupervised Models
  • KNN models
  • Hierarchical Clustering
  • Kmean (Homework)
• Model Evaluation
  • Handeling unbalanced classes
  • K-Fold Cross-Validation
  • Confusion Matrix