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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.

Practical Info:

• Class location: Building 306, Auditorium 032
• Timing: Classes start Tuesdays at 9:00
• Equipment: You will need a laptop work on and headphones.
• Communication: Is via Teams, you can find a link to the Teams space on DTU Learn.

Assignments

You should use DTU Learn to submit your assignments.

• Get Assignment 1 here.
• Get Assignment 2 here.

For details on deadlines, etc, check out the course overview.

Lectures

• Before week 1: Info. Take a look at this page before you do anything. This class most likely works a little bit differently from other classes you've taken. The notebook explains pretty much everything - the rest will be explained during the lectures.

• Before week 1: Python BootCamp. Python is the key tool we use in this class. If you don't feel 100% ready this notebook offers a quick refresher course. You will learn about installing python, about jupyter notebooks. By the end of this thing, you'll know enough to get going with the course.

  • Reading I: This pandas Tutorial. Just work through the examples in your own Jupyter Notebook.

• Week 1: Introduction. This week is all about getting started. It's a light load, since we want everyone to get a good start, especially if you're not a Python Ninja, just yet. Thus, there's room for prep, making sure you're all on top of Python, etc.

  • Reading: We'll be looking into crime patterns. You may optionally take a look at this article from Science Magazine to get a bit deeper sense of the topic.
  • Note: To run a notebook you first need to download it.

• Before week 2: Info on Assignments and Final Project. Here's a quick informational video on how we run the assignments and the final project.

• Week 2: Let the data science begin. Ok. So now that everyone's up to speed with Python, we'll start with a little intro on data visualization while continuing the analysis of the data that we downloaded last week. You'll learn that just calculating simple distributions (and conditional distributions) can teach you A LOT about a dataset.

  • Reading: No reading this week. Just fun with coding.

• Week 3: Plotting single variable data. This week we go deeper with the dataviz lectures. We'll also start reading independently and learn about the many different ways you can visualize just a single variable. Finally, we'll have some fun plotting data on a map.

  • Reading: Data Analysis with Open Source Tools Chapter 2. To find the text, go to the Teams space and look in Files/Week2.

• Week 4: Heatmaps and data errors. GeoSpatial data is a very important category, so this week we dig deeper with options for visualizing that data-type. Including strategies for making little movies. We also have a small exercise to talk about errors in the data, which draws on some of the work we've done in previous weeks. I hope you enjoy todays relatively light load.

  • Reading I. Read through the following tutorial How to: Folium for maps, heatmaps & time data. Get it here
  • Reading II (Optional) There are also some nice tricks in Spatial Visualizations and Analysis in Python with Folium. Read it here if you'd like, otherwise it should be safe to skip:.

• Week 5: More plotting, linear regression.. This lecture features an exploration of data with two variables, something which we have read about (see below). Then we do logarithmic plots and have lots of fun with linear regression, and its associated math.

  • Reading: DAOST Chapter 3. It's on Teams in the Files tab.

• Week 6: Interactive plotting. The main point of this lecture is to get started with interactive plotting. We also do another lecture on visualization with the same theme + read a little bit on narrative data visualization.

  • Reading: Narrative Visualization: Telling Stories with Data, section 1-3. This paper is by Edward Segel and Jeffrey

• Week 7: Setting up a website for dataviz. For the final project you'll need a website to display your visualizations. Recently, I've started to let you work on creating the website as part of the course.

  • Reading: There's actually a fair amount of documentation to wrap your brains around, but it's spread out across the instructions inside the lecture itself. So my recommendation is simply to get started on the website creation.

• Week 8: Getting ready for project mode. Today we're going to read a bit more about narrative data viz & then get ready for independent work by creating a micro-project base on the work in the course up to now.

  • Reading: Narrative Visualization: Telling Stories with Data, the rest of it. This paper is by Edward Segel and Jeffrey