ECON2306-Data-Management-2021-22

Course materials for Spring 2021 HEC Liège Course, “Data Management”

Outline of the class

The following table presents the structure of the class through the program & materials of the Lectures et the Practices classes. The Lecture column lists the content associated with theoretical presentations.
The practice column details the content associated with practical sessions. The Homework column details the homework, that are either project’s milestones or problem sets.

Week	Lecture	Practice	Problem sets, project milestones
10-Feb	Introduction html pdf	Using git html pdf	Installing python using the guide ; getting ready with git
17-Feb	Introduction to Python slides	Python practice notebook
24-Feb	The importance of visualization principles (1h) slides	Pandas 1: introduction (2h) slides	PS1: simple pandas + graphs (due:17/03)
03 & 10 -Mar	Webscraping theory notebook	Webscraping practice notebook	Start thinking about a project idea
17-Mar	Satistical learning html pdf	Kickstarting of course project	ML1: Having decided on the project idea
24-Mar	Supervised learning slides pdf	Practice ML notebook	PS2 on ML (due: 28/04)
31-Mar	Unsupervised learning html pdf	‘Open house’ on webscraping of project
28-Apr	Natural Language Processing slides	NLP notebook	ML2: Webscraping done; starting with visualization
05-May	‘Open house’ on webscraping	Presentation of project ideas & scraping methodology
12-May	Visualization using dash notebook	‘Open house’ on dash visualization
19-May	ML3: Student’s presentation of the applications

• Lists of potential subjects : Maps, Creating a Github website, Replication package, Logging, Pandas and SQL, Scraping twitter, Selenium, Scrapy, Computer vision

A notebook with some guidelines for using selenium can be found here

Course project

I would like you to chose a standard [research or policy or business] question that you can answer using data (eg. where are located the death from covid? Where are the cheapest beers in Liège? At what time people go to work? When and where should you go sailing?)

You will develop a project abiding by the good practices taught in class. The project should incorporate:

• Include an original data collection step (webscraping)
• The main output should be a dash page that you develop on Herokuapp, and should include;
  • Some text, with brief explanations regarding : the question asked, the choice and the nature of the data (you do not have to include the data steps [collection & cleaning] here), the exhibits (should have reading notes) and what you conclude from them.
  • 4 exhibits: 2 tables and 2 Figures (using different commands)
  • The possibility to modify the visualization using button for at least one exhibit

Submission format: Invite @malkaguillot and @michel to collaborate on your GitHub repository by the due date (see here).

- This means, we expect well version controlled work.
- a github folder with at least 5 commits (bonus if you have several development branches!)
- Tag your final submission using the following `git command git tag -a 1.0 -m "submitted version"`.
- You must have a `README.md` in the main directory with:
  - instructions on how we can build the assignment & what it does;
  - a link to the deploymed application.

Projects should be realized by group of 1 or 2 (one group of 3 if there is an odd number of remaining students).

Project milestones We will discuss about your proposed project several times so that we can evaluate whether it is do-able within the time frame. The project will be organized around the following milestones, materialized by a presentation in class:

• ML1: Project idea & scraping methodology (5 minutes)
• ML2: Visualization plan (5 minutes)
• ML3: Final presentation (5 minutes) At ML3, you should also hand the final course product (by inviting us to your gitlab folder).

Problem sets

The Problem sets are simple exercises designed to help you to “get their hands in the data”. They will be available on Lola and should be handed in there. These problem sets will take the form of Jupyter notebooks, that will have to be converted in pdf before being handed in.

Evaluation

Detail	Grading decomposition
Problem sets (individual)	15% (5% each)
Course project	85%
Project management (reproducibility, github, readme)	15%
Relevance of the project Does the project respond to an interesting/important question?	10%
Quality of the visualization Choice of the graphical representations & colors	20%
Technical dimension Is the project using advanced tools/techniques?	15%
Quality of the oral presentation Final presentation Project idea & scraping methodology Visualization plan	25% 15% 5% 5%
Bonus (class participation)	5%