Coursebook for Data Science Toolbox

Lectures

• 1.1 - Introduction to Data Science (26.59)
  • Slides
• 1.2 - Exploratory Data Analysis (26.45)
  • Slides
  • Reference R code (NB: See 1.3.1 for explanation)
• 2.1.1 Overview of Regression (35.15)
  • Slides
• 2.1.2 - Modern Regression (28.30)
  • Slides
  • Reference R code
• 2.2.1 Statistical Testing - Classical Testing (25.50)
  • Slides
• 2.2.2 - Statistical Testing - Empirical Testing (24.53)
  • Slides
• 2.2.3 - Model Selection (36.26)
  • Slides
  • Reference R code
• 3.1 Latent Structures and PCA (41:12)
  • Slides
  • Reference R code
• 3.2.1 Clustering Part 1 (32:34)
  • Slides
• 3.2.2 Clustering Part 2 (34:32)
  • Slides
  • Reference R code

Worksheets

• Worksheet 01.2 Exploratory Data Analysis
• Worksheet 02.1 Regression and Correlation
• Worksheet 02.2 Statistical Testing
• Worksheet 03.1 Latent Spaces and PCA
• Worksheet 03.2 Clustering
• Worksheet 04.1 Non-parametrics
• Worksheet 04.2 Outliers and Missing Data
• Worksheet 05.1 Introduction to Classification
• Worksheet 05.2 Ensemble Learning
• Worksheet 06.1 Trees, Forests, Decisions
• Worksheet 07.1 Topic Models
• Worksheet 08.1 Algorithms
• Worksheet 09.1 Neural Networks
• Worksheet 10.1 Parallel algorithms
• Worksheet 11.1 Parallel Infrastructure
• Worksheet 12.1 Ethics and Privacy in Data Science

Preparation

See Appendix 1: Preparation for details.

Workshops

• 1.3.1 - Workshop Lecture for RStudio (29.05)
  • Rmd for 1.3.1 Introduction to R Studio
• 1.3.2 - Workshop Lecture for Exploratory Data Analysis (18.13)
  • Rmd for 1.3.2 Exploratory Data Analysis R markdown
• 1.3.3 Workshop Lecture on Assessments, split into the following parts:
  • 1.3.3.1 - GitHub Workshop Intro (4:33)
  • 1.3.3.2 - GitHub Workshop Git Setup (8:10)
  • 1.3.3.3 - GitHub Workshop Git Repositories for Projects (21:32)
  • 1.3.3.4 - GitHub Workshop Assignment 0 walkthrough (19:48)
  • 1.3.3.5 - GitHub Workshop Example Assignment walkthrough (9:44)
• 2.3 - Workshop Lecture on Regression (28.58)
  • Rmd for 2.3 Model Selection for Regression R markdown
• 3.3 - Workshop Lecture (21:55)
  • Rmd for 3.3 PCA and Clustering markdown

Assessments

Semester 1:

• Example Assessment
• Assessment 0 (Formative, i.e. does not contribute to grade)
• Assessment 1
• Assessment 2

Semester 2:

• Assessment 3
• Assessment 4
• Assessment 5

See Appendix 2: Assessment for details, and Appendix 3: Replicability for notes on how to ensure that code works for everyone, by documenting versions of packages.