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); analyze network data; and the application of techniques to make decisions based on data. 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, Scikit-learn and auxiliary libraries (e.g.,GeoPandas, NetworkX, Fairlearn, Seaborn).
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning, 2nd edition, Springer (link)
VanderPlas, J. (2016), Python Data Science Handbook: Essential tools for working with data. O'Reilly Media (link)
Python 3.x
Anaconda Individual Edition (link)
Numpy, Matplotlib, Pandas, Scikit-learn and auxiliary libraries (e.g., GeoPandas, NetworkX, Fairlearn, Seaborn)
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. Werkcollege will be used to practice concepts learned in the lectures. It is advised that students read and do independent work before each lab (Werkcollege), 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 people) to work on two assignments. We endeavor to have practical material available to you the week before it will be used (maximum, Friday before the respective classes). All hoorcollege and werkcollege will be in-person, at Science Park.
|
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 | |
|
0.35 (35%) Tentamen | |
|
0.5 (50%) Assignments | |
|
0.15 (15%) Online quizzes |
Assessment will consist of
Any changes will be communicated through Canvas.
With group members: analyse the corpus of texts of UN General Debate statements from 1970 to 2021. 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
Course structure and study materials *
|
Week |
Hoorcollege 1 |
Hoorcollege 2 |
Werkcollege |
Assessment |
Weekly Readings |
|
1 5/9 |
Introduction, Course Overview; Python basics NumPy |
The Data Science life cycle Exploratory Data Analysis Pandas |
Lab 1: Exercises with Python and NumPy |
Quiz 0 (0%) Sep 9 |
Ch 2 and 3 of [2] Suggested: [3] [4] [5] |
|
2 12/9 |
Visualization best practices Data visualization with Matplotlib Elements of Statistical Learning |
Visual Analytics (by Marcel Worring)
|
Lab 2: Exercises with Pandas and Matplotlib; Analysis of the 2022 Happiness Report dataset |
Quiz 1 (5%) Sep 16 |
Ch 4 and 5 of [2] Suggested: [6] [7] [8] |
|
3 19/9 |
Regression: Linear and Polynomial Regression Feature Engineering Model Validation Bias-variance tradeoff Regularization |
Classification: Logistic Regression, Naïve Bayes, k-NN Gradient Descent Generative/Discriminative, Parametric/non-parametric models |
Lab 3: Regression and classification; Intro to group assignment: Analysis of UN Debates dataset Network analysis |
Quiz 2 (5%) Sep 23 |
Ch 2, 3, 4, 5.1, 8.1 of [1] Ch 5 of [2] |
|
4 26/9 |
Decision Trees and Ensemble Methods Unsupervised Learning: Principal Component Analysis K-Means |
Network data: Principles of network science and graph analysis Unsupervised Learning: Community Detection |
Support to finish group assignment |
Assignment I (25%) suggested deadline 30/9 hard deadline 3/10 23:59 |
Ch 4.4, 8.2 and 12 of [1] Ch 5 of [2] Suggested:[9] [10] |
|
5 3/10 |
Ethics & Data Science I (by Arjan Vreeken) |
Ethics & Data Science II (by Arjan Vreeken) |
Lab 4: Compute fairness metrics in concrete datasets; Fairlearn |
Quiz 3 (5%) Oct 7 |
Suggested: [11] |
|
6 10/10 |
Fairness metrics Techniques to mitigate bias in data science Explanation techniques |
Differential Privacy Data Science in Dynamic Environments: Strategic Classification |
Support to finish group assignment |
Assignment II - Ethics (25%) suggested deadline 14/10 hard deadline 17/10 23:59 |
Suggested: [12] [13] [14] [15] |
|
7 17/10 |
Data Science in the field (by Max Baak) |
Frontiers of Data Science Connections with next courses Exam preparation |
Discussion of assignments Exercises exam preparation |
- |
Suggested: Ch 10 of [1] |
|
8 24/10 |
Exam (25/10) |
Exam (35%) |
|||
* Check Canvas for updates
Primary bibliography
[1] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning, 2nd Edt, Springer (link)
[2] VanderPlas, J. (2016), Python Data Science Handbook: Essential tools for working with data. O'Reilly Media (link)
Suggested 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] Leek, Jeffery T., and Roger D. Peng. What is the question?, Science 347.6228 (2015): 1314-1315 (link)
[6] Rougier, Nicolas P., Michael Droettboom, and Philip E. Bourne. "Ten simple rules for better figures." PLoS Computational Biology 10.9 (2014): e1003833 (link)
[7] 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) (link)
[8] M. Worring et al.: Multimedia pivot tables for multimedia analytics on image collections. IEEE TMM, 18 (11), pp. 2217 – 2227 (link)
[9] Vespignani, A. Twenty years of network science. Nature (2018): 528-529 (link)
[10] Newman,M. The structure and function of complex networks. SIAM Rev 45.2 (2003)167-256 (1 to 3, 8.2) (link)
[11] Barocas, S., & Boyd, D. (2017). Engaging the ethics of data science in practice. Communications of the ACM, 60(11), 23-25 (link)
[12] Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019). Definitions, methods, and applications in interpretable machine learning. Proceedings of the National Academy of Sciences, 116(44), 22071-22080. (link)
[13] Chouldechova, A., & Roth, A. (2020). A snapshot of the frontiers of fairness in machine learning. Communications of the ACM, 63(5), 82-89. (link)
[14] Dwork, Cynthia, and Aaron Roth. The algorithmic foundations of differential privacy. Foundations and Trends in Theoretical Computer Science 9.3–4 (2014): 211-407 (link)
[15] 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.