In the digital era, we are bombarded by humongous streams of information. Sometimes, these 'inputs' are not interesting enough to breach the defensive barriers that protect our attention―hence, we simply decide to pass over. Other times, what we are exposed to seems valuable but we do not see any neat, meaningful pattern in it (that's the bottom line as it comes to crunch massive datasets!!). According to the popular idiom "one picture is worth a thousand words", all what messengers and audiences need to 'click' is―often-times―just a simple, effective visualization.
This module is a journey into the art and science of creating memorable charts, which grab the attention of the audience and successfully convey insights and narratives. Pleasant journeys require good companions: 'infographic'―i.e., the field that investigates the representation of information, data, and knowledge―will offer the theoretical platform for the module; Python will make things happen (at least on our screen). Consistently with the teaching philosophy of the module leader, each individual lecture integrates 'theory' and 'practice'.
Mandatory materials/readings will be circulated via Moodle in advance (Monday). For this module you are not required to purchase any expensive book, whereas it is essential you carefully go through:
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lecture notes (to be uploaded onto Moodle/Github weekly);
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Python scripts/Jupyter notebooks (to be uploaded onto Moodle/Github weekly);
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journal articles (to be uploaded onto Moodle/Github weekly).
Discretionary readings/materials students may want to reference to are:
At the end of the module, students should be able to:
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generate and evaluate visual forms for appropriateness, context, and meaning;
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design and execute statical charts with a particular emphasis on massive datasets;
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design and execute complex visualizations involving timeliness and geospatial attributes;
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design and execute interactive visualizations;
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leverage the visualization capabilities of the Python libraries Matplotlib and Bokeh.
As per the module specification, students will be assessed on the basis of coursework submissions, which all are the outcome of group-level efforts (yes, you understand correctly, for this module there is no final examination and you are not supposed to deliver any assignment on your own). Specifically, there are two pieces of coursework, namely a 'mid-term project' (MTP), and a 'final course project' (FCP), which contribute to the final mark (FM) as follows:
FM = 0.25 X MTP + 0.75 X FCP
For the mid-term project, students are required to solve a complex visualization problem. This year, the project will focus on the topic of 'popularity' in markets (details will be available by week 3, when the project will be released). Submissions will be assessed on a 0 - 100% scale. The Groups who fail can resubmit a revised version of the project; if the revision is sufficient, students receive a 50% mark. The deadline for the project is November 11 (week 6). Selected groups will be invite to present the outcome of their work to fellow students in week 6.
With the final course project (due by week 10, see Table 1), groups make their hands 'dirty' as they help a real-world client to face some data visualization challenges. Details about the client and the challenge will be available in week 8. Final course projects will be evaluated on a rolling-based window and should be submitted by December 20.
Both types of assessment will be evaluated along four criteria: i) appropriate use of notions and frameworks discussed in class; ii) effectiveness of the proposed answer or solution; iii) originality/creativity of the proposed answer or solution; iv) organization an clarity of submitted materials. All criteria carry-out equal weight in terms of mark.
Problem sets will be launched weekly. Students may want to deal these problem sets and present their solution to the class. One student per session will be selected on the basis of the novelty and effectiveness of the proposed solution. One bonus point (+1 FM) will be assigned.
| Week | Topic |
|---|---|
| 1 | Introduction to the module |
| 2 | Elements of infographic |
| - taste, aesthetics, and perceptions | |
| - visual forms | |
| - colors | |
| - exemplars of visualization | |
| Exploratory statistical charts | |
| - frequencies | |
| - univariate distributions | |
| - bivariate distributions | |
| - 3D distributions | |
| Laboratory sessions (Matplotlib) | |
| 3 | Time-dependent data |
| - timelines | |
| - sequences of events | |
| - narrative | |
| Laboratory sessions (Matplotlib) | |
| 4 | Visualizing statical estimates and fits |
| - uncertainty in estimates | |
| - plotting causal effects estimated via regression | |
| - comparing causal effects across groups | |
| Laboratory sessions (Matplotlib) | |
| 5 | Geospatial maps, part I |
| Laboratory sessions (Fiona + Pyshp + Rasterio + Pyproj + Shapeley + Geopandas) | |
| 6 | Mid-term project in class-presentations |
| 7 | Geospatial maps, part II |
| Laboratory sessions (Fiona + Pyshp + Rasterio + Pyproj + Shapeley + Geopandas) | |
| Final course project release | |
| 8 | Interactive visualizations for the web, part I |
| Laboratory sessions (Bokeh) | |
| 9 | Interactive visualizations for the web, part I |
| Laboratory sessions (Bokeh) | |
| 10 | Group-level meetings |
The SMM692 ― Python Pre-Course module defines the knowledge students should possess in order to proficiently attend to SMM635 ― Data Visualization.
For this module you are supposed to run Python 3.7 on your machine. Now, how to get Python work on your machine? There are several ways to do that. A fast, smooth alternative is to install Anaconda, an open source distribution of Python that includes: i) 250+ popular data science packages; ii) the conda package, which makes quick and easy to install, run, and upgrade complex data science and machine learning environments.
Here is the workflow:
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Use your preferred browser to open the link pointing to the Anaconda repository;
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Select the installer the which suits your machine (32- or 64-bit) and operating system (Win, Mac OS, Linux). Mac users may want to download the graphical installer rather than the command-line installer (students may feel less comfortable with);
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Retrieve the installer (perhaps in your download folder);
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Run the installer;
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Log-out from your current session (it does not matter if you use Win, Mac OS or Linux);
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Log-in into a new session;
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Run 'Anaconda Navigator'―namely, a convenient place to launch the IPython shell or other user-interfaces to interact with IPython.
On top of Anaconda/Python, students should install the modules Matplotlib, Bokeh, Fiona, Pyshp, Rasterio, Shapely (I recommend to do that using Conda).
- Created: Tue 01 Oct 20:00:01 UTC 2019
- Last change: Wed Nov 20 17:03:45 UTC 2019