6 EC
Semester 1, period 1
5294FUDS6Y
| Owner | Master Information Studies |
| Coordinator | Fernando Pascoal Dos Santos |
| Part of | Master Information Studies, track Data Science, year 1 |
Data science is a dynamic and fast-growing interdisciplinary research field that, across science, industry, and government, is altering how people understand the world and make decisions. Not surprisingly, the demand for data science skills is on the rise. This course will cover key principles and tools of data science. In particular, the course will cover the process of acquiring and transforming data; the application of algorithms to learn from data (e.g., classification, regression, clustering); and the application of techniques to make decisions based on data, founded on introductory concepts of game theory. The course will also cover the social and ethical implications of data science, with a particular emphasis on algorithmic fairness and explainability. The course will expose students to theory (i.e., machine learning and statistical methods underlying data science) and practice (i.e., use of data science libraries and analysis of real-world datasets). During the course, students will work on a series of individual exercises and group assignments that will bind together all elements of the data science process. Python will be used for all programming assignments. The course will introduce and make use of Jupyter notebooks, Numpy, Matplotlib, Pandas and Scikit-learn.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer (link)
VanderPlas, J. (2016), Python Data Science Handbook: Essential tools for working with data. O'Reilly Media (link)
Each week the students are incentivized to work independently in the lab exercises and in the suggested readings (Self-study). In the plenary lectures (Hoorcollege), we will focus on discussing theoretical contents that bind together what students are supposed to learn during the week. Lectures will be used to discuss theoretical aspects of data science and to interact with invited guest speakers. Given that plenary lectures will occur after some labs (Werkcollege), it is advised that students read and do independent work before each lab, and use the lab to discuss, ask questions and confirm their solutions. In lab exercises, students will work with examples of data science applications implemented in Python. Students will get together in groups (4-5 people) to work on two assignments.
|
Activity |
Number of hours |
|
Hoorcollege |
28 |
|
Project |
60 |
|
Werkcollege |
14 |
|
Zelfstudie |
66 |
In TER part B of this programme no requirements regarding attendance are mentioned.
| Item and weight | Details |
|
Final grade |
Assessment will consist of
Changes (e.g, due to changes in COVID-19 restrictions) can apply and will be communicated through Canvas.
With group members: analyse the corpus of texts of UN General Debate statements from 1970 to 2020. Relate with external datasets (25% of overall grade)
With group members: analyse several ethical aspects of a case chosen by the group and write a paper about it. Use the Networked Systems Ethics guidelines to guide analysis (25% of overall grade)
The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl
(Tentative) schedule and readings *:
|
|
Week |
Hoorcollege 1 |
Hoorcollege 2 |
Werkcollege |
Assessment |
Weekly Readings |
|
Part I: Elements of Data Science |
1 |
Introduction, Course Overview Python basics and NumPy |
The Data Science life cycle Exploratory Data Analysis Pandas |
Exercises with Python and NumPy |
- |
Ch 1 and 2 of [2] [3] [4] |
|
2 |
Visual Analytics (by Marcel Worring) |
Data visualization with Matplotlib Elements of statistical learning |
Exercises with Pandas and Matplotlib; Analysis of the 2021 Happiness Report dataset (link) |
Quiz 1 (5%) Time TBD |
Ch 3 and 4 of [2] [5] [6] [7] |
|
|
3 |
Regression: Linear, Polynomial Logistic. Feature Engineering Model Validation |
Classification: Naïve Bayes Feature engineering: text features Short Intro to NLTK |
Regression and classification exercises; Intro to the group assignment: Analysis of UN Debates dataset (link) |
Quiz 2 (5%) Time TBD |
Ch 3, 4 and 8.1 of [1] Ch 4 of [2] |
|
|
4 |
Ensemble Methods: Decision Trees and Random Forests Support Vector Machines |
Principal Component Analysis Clustering, K-Means |
Support to group assignment |
Assignment I (25%) due 4/10 23:59 |
Ch 8.2, 9 and 10 of [1] Ch 4 of [2] |
|
|
Part II: Data Science in the Real World |
5 |
Ethics & Data Science I (by Arjan Vreeken) |
Ethics & Data Science II (by Arjan Vreeken) |
Compute fairness metrics in concrete datasets; |
Quiz 3 (5%) Time TBD |
[8] |
|
6 |
Fairness metrics Biased data and word embeddings Explanation techniques |
Consequential decision making; Data science in dynamic environments
|
Support to ethics group assignment |
Assignment II - Ethics (25%) due 18/10 23:59 |
[9] [10] [11] [12] |
|
|
7 |
Data Science in the field and synthetic data generation (by Max Baak) |
Wrap up Connections with next courses Discussion |
Discussion of assignments Data science with network data (NetworkX) |
- |
- |
|
|
|
8 |
Exam (35%) Check DataNose |
* check Canvas for updates.
Primary bibliography
[1] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning, Springer (link)
[2] VanderPlas, J. (2016), Python Data Science Handbook: Essential tools for working with data. O'Reilly Media (link)
Recommended readings
[3] Blei, D. M., & Smyth, P. (2017). Science and data science. Proceedings of the National Academy of Sciences, 114(33), 8689-8692. (link)
[4] Chapter 1: O'Neil, C., & Schutt, R. (2013). Doing data science: Straight talk from the frontline. O'Reilly Media, Inc. (link)
[5] D. Sacha et al.: Knowledge generation model for visual analytics. IEEE TVCG, 20 (12), pp. 1604 – 1613, December 2014.
[6] A. Endert et al.: The state of the art in integrating machine learning into visual analytics: Integrating machine learning into visual analytics. Computer Graphics Forum, 36 (4), March 2017.
[7] M. Worring et al.: Multimedia pivot tables for multimedia analytics on image collections. IEEE TMM, 18 (11), pp. 2217 – 2227, September 2016.
[8] Barocas, S., & Boyd, D. (2017). Engaging the ethics of data science in practice. Communications of the ACM, 60(11), 23-25.
[9] Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Poceedings of the National Academy of Sciences, 116(44), 22071-22080. (link)
[10] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016, August). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144) (link)
[11] Chouldechova, A., & Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(5), 82-89. (link)
[12] Liu, L. T., Dean, S., Rolf, E., Simchowitz, M., & Hardt, M. (2018, July). Delayed impact of fair machine learning. In International Conference on Machine Learning (pp. 3150-3158). PMLR. (link)
Recommended reading in case students need a Python refresher
VanderPlas, J. (2016), A Whirlwind Tour of Python. O'Reilly Media (link)
The schedule for this course is published on DataNose.