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Sune Lehmann edited this page Mar 23, 2021 · 47 revisions

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.

IMPORTANT COVID-19 INFORMATION

Assignments:

Exercises

  • 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.

  • Week 1: Introduction. This week is all about getting started: 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. You can also see the file here on github, but the videos won't display properly.

    • Reading: We'll be looking into crime patterns. Take a look at this article from Science Magazine to get a bit deeper sense of the topic.

  • 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 and Pandas, we'll start doing more analyses 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. But that's not it. We'll also get creative with plotting GPS data. (And as a little bonus, we'll also play with some traditional dataviz examples.) So LOTS to do today. No time for reading :) And if the notebook isn't rendering for some reason, you can always access it here

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

  • Week 3: Plotting single variable data. This week we start with dataviz lectures. We'll also start reading independently and learn about the many different ways you can visualize just a single variable. Or try your luck with NBviewer here

    • Reading: Data Analysis with Open Source Tools Chapter 2. To find the text, you will need to go to DTU Learn. It's under "Course content" → "Lecture 3 reading".

  • 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. If you want to try to view the exercises on NBviewer, check them out [here}(https://nbviewer.jupyter.org/github/suneman/socialdata2021/blob/main/lectures/Week4.ipynb)

  • Week 5: More plotting, linear regression. This lecture features more lecturing (and a cool bonus video). Then we get into exploring data with two variables, something which we'll read about (see blow). Then we do logarithmic plots and have lots of fun with linear regression and the associated math.

    • Reading: DAOST Chapter 3. To find the text, you will need to go to DTU Learn. It's under "Course content" → "Lecture 5 reading".

  • Week 6: Quick intro to machine learning. Today we catch everyone up on machine learning. That means a lot of material to get through, lots of videos, lots of reading, lots of exercises. But it will pay in terms of skills to analyze data and create amazing data visualizations. Here's the structure: We will learn about machine learning in general. Then we will play with sklearn, then we will learn the about KNN and solve a KNN exercise. Then we will rest. And if NBviewer works, you can find the exercises here.

    • Reading: Data Science from Scratch chapter 11 and 12 (get the files on DTU Learn under "Lecture 6 reading").

  • Week 7: More machine learning. Today we continue working with machine learning. The purpose of our exercises to day is to show you that amazing things can happen when we combine data sources. So we'll add weather data to our crime dataset for new insights. On the reading and video front, we'll prep for next week when we finish off by looking into explanatory data visualization. And if NBviewer works, you can also find the exercises here.

  • Week 8: Webpage & interactive visualizations with Bokeh. Today we explore tools for explanatory data visualization. We read more about narrative data visualization, create an interactive data visualization using Bokeh, and set up a web-page.

This class has been hand crafted for you by Sune Lehmann in Copenhagen.

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