Data science and society

Course goals

At the end of this course, you will be able to:

1. understand the role of data science and its societal impact
2. recognise the knowledge discovery processes in applied data science
3. identify trends and developments in data science and machine learning
4. apply selected data science and machine learning technologies to solve real-world problems
5. apply statistical and machine learning tools for data analysis
6. apply containerisation techniques in a practical data science project
7. apply simple data base queries in a practical data science project.
8. understand principles and frameworks for AI and privacy-preserving data science
9. understand and apply all phases of CRISP-DM in a practical data science project

• part A: final digital exam at the end of the course, which will provide an overall assessment on all concepts discussed in the course.
• part B: practical team project that provides hands-on experience in the practical implementation of the concepts taught in the course in the context of developing a performance dashboard.
• part C: optional bonus for extraordinary participation/performance

The exam has a guessing correction, as is UU standard for digital exams https://remindo-support.sites.uu.nl/wp-content/uploads/sites/79/2017/11/Verantwoording-raadkans-en-score-in-Remindo-Toets_eng-GB.pdf

For passing the exam the result has to be positive after the guessing correction. The final grade = [A]*0.60 + [B]*0.40 + [C]. Passing the course requires passing each of the two partial grades.

To qualify for a repair of the final result the mark needs to be at least a 4, or “AANV”.

Prerequisites
Even though this course is not a programming course, you are required to write various data analysis scripts.
Therefore, if you don't have any script programming experience yet, it is advisable to familiarize yourself beforehand by taking an online introductory Python programming course. Likewise, basic understanding of Databases and query formulation in SQL are required.
The course will also make use of Docker for containerisation.

Content

This is the starting and obligatory course for the master MBIM. As such, its primary objective is to inspire and introduce you to the domain of applied data science.
Thus, the course will provide an overview on Data Science, its process, key techniques and selected algorithms.
Throughout this course, societal aspects are implicitly catered for, with the whole course building towards an integrated view. Ultimately, it aims to empower you to ensure a responsible use of data science techniques, be it as a manager, developer, a (data) scientist, or citizen.
Understanding the principles behind techniques such as clustering, classification, optimisation et cetera. is key to understanding concepts like privacy models, consent/shadow profiles, and ethical dilemmas, as well as how to address them responsibly.
Since the participants in this course typically have highly heterogeneous background knowledge, we will first work to equip all with a basic understanding of data science, and of the principles and

The course is constructed as follows:

1. introduction: data science & processes, analytics (data science and related concepts & terms, overview on analytic tasks, processes
2. organizing the data: DevOPs challenges, virtualization, containerisation, data sources & integration, operational vs. analytics data, getting the data: flat files, databases, APIs)
3. descriptive analytics: nature of data, its types, quality, integrity, data preprocessing, statistical modelling, visualization
4. predictice analytics: supervised vs. unsupervised machine learning, clustering, clustering, classification
5. predictice analytics: optimisation
6. further aspects: ethics, privacy, explainability

Course form
Lectures, tutorials, quizzes, Q&A sessions, assignments, team project, self-study.