Data Science 2019-2020
Course schedule
Dates and times: see the table below
This course is partly combined with the course Data Science and Process Modelling from the I&E program (dr. Frank Takes).
- Lectures 2 and 4 are taught by dr. Takes;
- Lecture 7 is joined by the I&E students;
- Lecture 9 is a common guest lecture;
- The practical sessions in weeks 2, 3 and 4 are from 9.15-11.00 (Snellius room 306/308 and 307)
Slides and other materials will be posted on Blackboard weekly (under 'course documents'). Assignments should be submitted through Blackboard as well.
The video lectures of week 7 and onwards are available on Blackboard
| Week | Location | Topic | Practical session | Homework (literature/assignment) |
|---|---|---|---|---|
| 1 (Mon 3-2, 11.15) | HG106-109 | Introduction | No practical session (longer lecture) | Paper: “What Educated Citizens Should Know About Statistics and Probability” (2003) |
| 2 (Mon 10-2, 11.15) | 407-409 | Visual Analytics | Assignment 1 (9.15-11.00) | |
| 3 (Mon 17-2, 11.15) | HG106-109 | Model learning 1 | Assignment 1 (9.15-11.00) | Paper: “Machine learning: Trends, perspectives, and prospects” (2015) |
| 4 (Mon 24-2, 11.15) | 407-409 | Network Analytics | Assignment 1 (9.15-11.00) | Deadline visual analytics assignment: March 2 (Monday) |
| 5 (Wed 4-3, 14.15) | 312 | Evaluation | See Blackboard: week 5 (15.15-16.00) | |
| 6 (Wed 11-3, 14.15) | 312 | Data collection | See Blackboard: week 6 (15.15-16.00) | Paper: “The Parable of Google Flu - Traps in Big Data Analysis” (2014) |
| (Wed 25-3, 12.00) | Online | Test session with Kaltura | Q&A (TAs) | |
| 7 (Wed 1-4, 12.00) | Online | Pre-processing & Feature extraction 1 Video lecture: 1 2 3 4 5 | See Blackboard: week 7 (12.15-13.00) | Paper: “Crawling Facebook for Social Network Analysis Purposes” (2011) |
| 8 (Wed 8-4, 12.00) | Online | Pre-processing & Feature extraction 2 Video lecture: 1 2 3 4 | See Blackboard: week 8 (12.15-13.00) | Deadline Assignment 2: April 22 (Wednesday) |
| 9 (Wed 15-4, 11.15) | Online | Event data mining. Guest lecture by Bram Cappers, TU/e and AnalyzeData Video lecture: 1 | Deadline Assignment 2: April 22 (extended) | |
| 10 (Wed 29-4, 12.00) | Online | Model learning 2 Video lecture: 1 2 3 4 | Working on final assignment | Paper: “Exploring the Query Halo Effect in Site Search- Leading People to Longer Queries” (2017) |
| 11 (Wed 6-5, 12.00) | Online | Analysis and dicussion Video lecture: 1 2 3 | Working on final assignment | |
| 12 (Wed 13-5, 12.00) | Online | Recap on statistical concepts Video lecture: 1 2 3 | Working on final assignment | |
| 13 (Wed 20-5, 12.00) | Online | Big data & Responsible Data Science Video lecture: 1 2 3 | Q&A | Deadline final assignment: June 9 |
| (9-6) | B02 | Exam |
N.B. There is no class on March 18 and March 25.
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) (5%); (3) Final assignment (assignment 3-4) (25%). 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.
A submission after the deadline is counted as re-sit. When submitted as re-sit, the maximum grade for an assignment is 6.