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Intro

Welcome to the wiki for the course Social data analysis and visualization (02806) offered by the Technical University of Denmark. This is the main page, where you can access the weekly exercises. If you take a look in the side-bar, you can read about the administrative details (including a very useful course overview), assignments, books, and more.

The class is taught flipped classroom style, where the the lecture and homework elements of a course are reversed. You'll be able to view short video lectures before (or during) the class session, so in-class time can be devoted to exercises, projects, or discussions. Check out the first lecture to learn more.

Assignments

• Assignment 1 is available here.
• Assignment 2 is available here.

Exercises

• Week 1: Introduction. This week is all about getting started: Installing python, learning about Jupyter notebooks, and making sure that you're a relatively skilled python programmer. You can also see the file here on github, but the videos won't display properly (note, this file is still being developed).
  • Reading: Data Science from Scratch Chapter 1-3
• Week 2: Foundations. We'll be doing a lot of data science in this class, so let's make sure that you master the basics needed. Stuff like visual data exploration, statistics, linear algebra, probability theory and so on. You can also see the file here on github - and since you're learning data-science, it would be even better if you figured out how to clone the repository to your own machine, so you can download the .ipynb file to your local machine and view it there (that's the recommended way of doing things).
  • Reading: Data Science from Scratch Chapter 4-7
• Week 3: Let the data science begin. This week we'll be getting into the principles of machine learning, while starting to get to know the dataset. The lecture will feature an overview of fundamentals of machine learning (videos), and we'll (finally) start working on getting to know the dataset we'll be crunching over the next few weeks.
  • Reading: Data Science from Scratch Chapter 9-12
• Week 4: Regression - Linear, Multiple, and Logistic. Regression models are powerful and highly useful. We go over the theory behind regression and use regression to capture a notorious criminal mastermind: The Red Baron. (We also play a little bit with KNN).
  • Reading: Data Science from Scratch Chapter 14-16
• Week 5: Decision trees and Clustering. We're getting to the end of the machine learning part of the class, and it feels like we've only gotten started. But don't worry - you've learned a lot during these past weeks, and you now have the skill set to learn the rest of the awesome methods/algorithms from the book on your own if you need to. Anyway - today we'll be working with some very useful methods: Decision trees and clustering, and next week we'll keep working with data but get started on visualization.
  • Reading: Data Science from Scratch Chapter 17,19
• Week 6: Data Visualization Part I. Ok, so we're changing direction today and focusing our attention on visualization. There are two important parts of today's lecture. In the first part, we address the theory of visualization with a video lecture that introduces the fundamental ideas. In the second part, we get started on learning D3.js. D3 requires you to learn a new programming language (javascript) so we'll take it slow. But the good news is that D3 is amazing, and if you're ever going to apply for a job working with data outside of DTU, knowing D3 will give you an strong advantage over all other applicants.
  • Reading: Interactive Data Visualization Chapter 1-5
• Week 7: Data Visualization Part II. It's all about more D3. This time we'll be visualizing data from SFPD and learning more about the anatomy of a visualization.
  • Reading: Interactive Data Visualization Chapter 6-9
• Week 8: Data Visualization Part III. Not much to say today. After reading chapter 10-12, you guys are officially pro-level visualizers - all you need now is practice, practice, practice, practice, and then much more practice. And I also recommend the lecture on visualization: today's video ties together many lessons from the two previous lectures ... and also shows some amazing examples of bad visualizations. Very much worth a look.
  • Reading: Interactive Data Visualization Chapter 10-12