DATA SCIENCE SUMMER SCHOOL 2022 :: MACHINE LEARNING IN R

This is a DataKolektiv repository for our MACHINE LEARNING IN R Data Science Summer School 2022.
contact: hello@datakolektiv.com

The Summer School will be hosted in Startit Center Belgrade every Saturday in June 2022, 09:00 - 18:00 CET.
Asynchronous consultations and work will be carried on via Slack and GitHub 4. - 30. June 2022.

PROGRAM

WEEK 1.

• Saturday, 4. June, 09:00 - 18:00 CET, Startit center, Belgrade
  • 09:00 - 12:30. Introduction to R programming: data types, vectors, matrices, and vectorization, lists, the data.frame class, I/O operations, control flow, functions, functional programming.
  • 14:30 - 18:00. tidyverse approach to R programming: dplyr and tidyr for data management and processing.
    
• Asynchronous (Slack, GitHub), Monday, 6. June - Friday, 10. June
  • Data Visualization in ggplot2
  • Data Cleaning and Transformation (i.e. Data Wrangling) in stringr, dplyr and tidyr; Exploratory Data Analysis (EDA) in R.
• Learning Material:
  • R for Data Science
  • DataKolektiv's DATA SCIENCE SESSIONS: Session 01-08
  • Introduction to Programming with R
• Review Material (Math):
  • Khan Academy: Probability
  • Khan Academy: Random Variables
  • Khan Academy: Modelling Data Distributions
  • Khan Academy: Summarizing Quantitative Data

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WEEK 2.

• Saturday 11. June, 09:00 - 18:00 CET, Startit center, Belgrade
  • 09:00 - 12:30. Linear and Multiple Linear Regression [review]
  • 14:30 - 18:00. Binomial Logistic and Multinomial Logistic Regression for Classification Problems [review1], [review2]
• Asynchronous (Slack, GitHub), Monday, 13. June - Friday, 17. June
  • Case Study 1: Churn Prediction
  • Control for Overfit 1: Regularized Linear and Generalized Linear Models [review1], [review2], [review3]

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WEEK 3.

• Saturday, 18. June, 09:00 - 18:00 CET, Startit center, Belgrade
  • 09:00 - 12:30. Cross-Validation and Regularization in Classification Problems [review]; Model Selection (ROC analysis) [review]; note: Python code, we will have ours in R
  • 14:30 - 18:00. Decision Trees (CART) [review]
• Asynchronous (Slack, GitHub), Monday, 20. June - Friday, 24. June
  • Case Study 2: Price Prediction in the Real Estate Market
  • Control for Overfit 2: Cross-Validation and Regularization in Regression Models

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WEEK 4.

• Saturday, 25. June, 09:00 - 18:00 CET, Startit center, Belgrade
  • 09:00 - 12:30. Random Forests for Regression and Classification Problems [review1], [review2]
  • 14:30 - 18:00. Gradient Boosting: XGBoost Model for Regression and Classification Problems [review1]; note a bit heavy on math perhaps; [review2]
• Asynchronous (Slack, GitHub), Monday, 27. June - Thursday, 30. June
  • Case Study 3: Web Content Popularity Prediction.
  • Case Study 4: Complete Model Layout and Find-Tuning XGBoost for Regression and Classification Problems