This minor provides a structured introduction to the field of Data Science. Students will learn scientific approaches to extract knowledge from structured and unstructured data. This minor complements the corresponding bachelor programs in Computer Science and Artificial Intelligence in the direction of data collection and data-driven solution methods. Thereby, this minor prepares students also for the Master Programme in Information Sciences.
Courses (I) introduce the main topics in Data Science, (II) provide some necessary knowledge of information retrieval, and (III) give societal perspectives in a course on Data Science and Privacy. The minor is concluded with the course Data Wrangling, in which students learn how to scrape data. The minor consists of 30 ECTS in total.
Required courses (12 ECs)
Introduction to Data Science
This introductory course will provide an overview of the field of Data Science, the different specialties within data science and the ethical issues that arise around data collection and use. The student will understand the daily activities of a data scientist, and get hands-on experience with a first data science project. The lectures will be given by many different people: scholars of different specialties, and people who work in data science teams of big and small companies.
Data wrangling is the process of gathering data in its raw form and molding it into a form that is suitable for its end-use. This course is about how to gather the data that is available and produce an output that is ready to be used. There are a number of common steps in the data wrangling process that will be discussed: acquiring, cleaning, shaping and structuring the data, as well as feature engineering and visualization.
Constrained choice (6ECs)
Strategic Management of Technology and Innovation
This course focuses on the strategic management of technology and innovation. Innovation refers to the development and implementation of new products, services, processes, and business models and many of those innovations are enabled by technological developments. Innovation is crucial for business organizations to stay competitive in ever-changing markets. In this course, students learn to understand and apply basic theories behind the processes of technology-based innovation within organizations and their environments, the development of innovation strategies, and the organizational implementation of innovation strategies. Theoretical understanding is applied in a simulation game and real-life cases focusing on managerial dilemmas in the management of innovation.
Data Structures and Algorithms
The course is concerned with data structures and the design and analysis of algorithms. We study several subjects from the book by Cormen et al: linear data structures such as stacks, queues, linked lists, tree-like data structures such as binary trees, binary search trees, balanced binary search trees, heaps, graph-like data structures, and hash tables. Further, we study several sorting algorithms, some graph algorithms, string matching, and the programming paradigms divide-and-conquer, dynamic programming, and greedy algorithms. We consider the worst-case time complexity and in some cases the correctness of algorithms.
Constrained choice (12ECs)
Data Analytics and Privacy
In the field of data analytics and data science, the opportunities seem endless. Automatic enforcement of norms, predictive medicine, and AI-operated digital assistants are but a few examples of what has become possible. Outcomes of data analytics can even precede what's on a man's mind: the cab arrives at the moment you did not even know yet you needed it, the packages are already posted before you ordered them, or the criminal behavior is predicted before it takes place. The focus of this course is not on the possibilities, but about the limits, we as a society want to put on those possibilities. Standards in fundamental rights are well known, yet not carved in stone. Through the study of literature, law and case law students will develop a critical view on the role of law in regulating data analytics, the limits it imposes, its' shortcomings and an understanding of the way these new technologies affect power relations between citizens and entities which deploy data analytics.
This course covers the core aspects of information retrieval and search engines, including indexing, Boolean retrieval, the different types of queries, query execution, the vector space model, web crawling, networks, link analysis, PageRank, classification, and clustering.
The course Logistics Analysis is an exciting course that will challenge you in various ways. By taking Logistics as a point of departure, we bring together several perspectives and analyze business problems faced by logistics companies. Taking a logistics perspective will stimulate you to think about organizations in a different way, bringing together knowledge from different fields and realizing that this creates challenges and conflicts that managers need to deal with. You will learn to systematically describe logistical systems, and identify problems that emerge in these systems. Moreover, this course offers you a number of tools that allow you to analyze logistical systems, optimize them, (re)design them and assess the consequences of suggested improvements. Important topics such as production management, inventory management, and maintenance management are addressed, which are essential, hands-on tools any logistics professional should be able to work with.
For specific information about each course please visit our Study Guide.
1 semester (30 EC)
Informatica, Wiskunde en Bedrijf