Data Science 2020-2021
Course schedule
This course is combined with Data Science and Process Modelling from the I&E program (dr. Frank Takes). The I&E students have an additional process modelling part for 1 additional EC.
- The lectures are on Wednesdays, 11.15-12.15. These are live, online, and will be recorded.
- There are practical sessions on Mondays, 15.30-16.15 (online until March 15, maybe on-campus later)
Slides, video recordings, and other materials will be posted on Brightspace weekly. Assignments should be submitted through Brightspace as well.
| Week | Topic | Practical session | Literature | |
|---|---|---|---|---|
| 1 (Wed 3-3) | Introduction | No practical session | Paper: "What Educated Citizens Should Know About Statistics and Probability" (2003) | |
| 2 (Wed 10-3) | Visual Analytics (dr. Takes) | Assignment 1 | Paper: "iPhone's Digital Marketplace: Characterizing the Big Spenders" (2017) | |
| 3 (Wed 17-3) | Model learning 1 | Assignment 1 | Paper: “Machine learning: Trends, perspectives, and prospects” (2015) | |
| 4 (Wed 24-3) | Evaluation | Assignment 1 | ||
| 5 (Wed 31-3) | Network Analytics (dr. Takes) | Deadline Assignment 1: March 29 | Chapter 1 and 2 from "Networks, Crowds, and Markets: Reasoning about a Highly Connected World" (2010) | |
| 6 (Wed 7-4) | Data collection | Assignment 2 | ||
| 7 (Wed 14-4) | Pre-processing & Feature extraction 1 | Assignment 2 | Paper: “The Parable of Google Flu - Traps in Big Data Analysis” (2014) | |
| 8 (Mon 19-4) | Event data mining. Guest lecture by Bram Cappers, TU/e and AnalyzeData | Assignment 2 | ||
| 9 (Wed 28-4) | Pre-processing & Feature extraction 2 | Deadline Assignment 2: April 26 | ||
| (Wed 5-5) | No lecture | Instructions for final assignment | in lab session on Monday | |
| 10 (Wed 12-5) | Model learning 2 | Final assignment | Paper: “Crawling Facebook for Social Network Analysis Purposes” (2011) | |
| 11 (Wed 19-5) | Analysis and dicussion | Final assignment | Paper: “Exploring the Query Halo Effect in Site Search- Leading People to Longer Queries” (2017) | |
| 12 (Wed 26-5) | Recap on statistical concepts | Final assignment | ||
| 13 (Wed 2-6) | Big data & Responsible Data Science | Deadline final assignment: June 18 | ||
| Wed 16-6 | Exam | |||
| Wed 14-7 | Re-sit exam |
Course grading
The assessment of the course consists of a written exam (60% of course grade) and a practical part (40% of course grade). The practical part is subdivided in (1). Visual analytics assignment (10%); (2) Feature extraction assignment (assignment 2) (10%); (3) Final assignment (20%). The grade for the written exam should be 5.5 or higher in order to complete the course. The weighted average grade for the practical assignments should be 5.5 or higher in order to complete the course. If one of the tasks is not submitted the grade for that task is 0.
The assignments are completed in groups of 2 students.
A submission after the deadline is counted as re-sit. When submitted as re-sit, the maximum grade for an assignment is 6.