Applied Data Science and Machine Learning

M1 - Applied Data Science and Machine Learning

This module provides a condensed introduction to the “Data Science Pipeline”, introducing students to methods, techniques, and workflows in applied data analytics and machine learning, including data acquisition, preparation, analysis, visualization, and communication.

Content by week for this module

Click on the to do for the week to see what you should do to keep up with the module

W 35: Introduction & landing
Weekly to do
- Introduction to R & Python (Datacamp, both necessary if no prior experience)
  - Intro to Python and/or R
  - [Intro to R
- Statistics Refresher (Datacamp, recommended if no prior statistics classes, choose either R or Python)
  - Python
    Statistical Thinking 1
    [Statistical Thinking 2
    Intro to linear modelling
  - R:
    Introduction to data in R
    Foundation of probability;
    Correlation & regression
- Course Material (Watch videos, study/run notebooks, solve provided exercises, optimally study suggested further material)
  - Warmup
  - basics: Theory for all, afterwards feel free to choose R or Python track
W 36: Data Manipulation, Exploratory Data Analysis (EDA), Data Visualization
Weekly to do
- Continue with the Course material (Datacamp, both necessary if no prior experience)
- Datacamp Courses of your Choice (For instance Pandas or Tidyverse practice)
- 1st Workshop
- 1st Peergrade Assignment
W 37: Unsupervised Machine Learning (UML)
Weekly to do
- Continue with the Course material (Datacamp, recommended but not mandatorye)
  - Python: UML
  - R: UML
- Course Material (Watch videos, study/run notebooks, solve provided exercises, optimally study suggested further material)
  - Unsupervised ML
- 2nd Workshop
- 2nd Peergrade Assignment
W 38: Supervised Machine Learning (SML)
W 39: Project work + exam