Data Science 2020-2021

Teachers: Suzan Verberne and Frank Takes
Teaching assistants: Ruduan Plug, Cheyenne Heath, Rik Zandbelt, Juan Bascur Cifuentes

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.

WeekTopicPractical sessionLiterature
1 (Wed 3-3)IntroductionNo practical session Paper: "What Educated Citizens Should Know About Statistics and Probability" (2003)
2 (Wed 10-3)Visual Analytics (dr. Takes)Assignment 1Paper: "iPhone's Digital Marketplace: Characterizing the Big Spenders" (2017)
3 (Wed 17-3)Model learning 1Assignment 1Paper: “Machine learning: Trends, perspectives, and prospects” (2015)
4 (Wed 24-3)EvaluationAssignment 1
5 (Wed 31-3)Network Analytics (dr. Takes)Deadline Assignment 1: March 29Chapter 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 1Assignment 2Paper: “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 2Deadline Assignment 2: April 26
(Wed 5-5)No lectureInstructions for final assignmentin lab session on Monday
10 (Wed 12-5)Model learning 2Final assignment Paper: “Crawling Facebook for Social Network Analysis Purposes” (2011)
11 (Wed 19-5)Analysis and dicussionFinal assignmentPaper: “Exploring the Query Halo Effect in Site Search- Leading People to Longer Queries” (2017)
12 (Wed 26-5)Recap on statistical conceptsFinal assignment
13 (Wed 2-6)Big data & Responsible Data Science Deadline final assignment: June 18
Wed 16-6Exam
Wed 7-7Re-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.

Earlier editions of this course

  • Link to the course page for this course in 2019-2020
  • Link to the course page for this course in 2018-2019
  • Link to the course page for this course in 2017-2018