The module’s learning objectives are formulated using Bloom’s taxonomy. The learning objectives are mapped to Programme Intended Learning Outcomes.
- Understand the data analytics workflow (based on CRISP-DM (Wirth, & Hipp, 2000))
- Assess data quality and ability to scrape, cleanse, and ethically maintain data
- Assess and compare the suitability of different data modeling methods/algorithms for optimal performance and evaluate results objectively.
- Familiarity with infrastructures and distributed systems used to deal with them, such as Hadoop and MapReduce
- Analyze and apply the most advanced and relevant statistic and mathematical techniques for business purposes, specifically
- Fundamental algorithms and mathematical models for processing natural language
- The fundamentals of the neural network as applied to the analysis of images
- Mathematical methods of decision analysis and modeling ML/AI algorithms
- Apply new frameworks and advanced fundamental knowledge, reflect on how frameworks work and motivate choice, integrate different parts of ML/AI.
- Build an automated workflow to scrap, clean, and ethically maintain data and result’s privacy.
Understanding the concepts of explainability and interpretability in AI as well as how to “open up” the black-box models that are produced by current-day machine learning methods. Considering ethical concerns of application of DS & AI technology in practice to ensure proper critical attention to ethical principles like beneficence, non-maleficence, autonomy, justice, and explicability.|
Enrolment for this module is now closed. If you are not yet enrolled but wish to participate, contact the module co-ordinator Maurice van Keulen, email@example.com
B-CS students register via Osiris; others can contact firstname.lastname@example.org.
Minor students: please register in Osiris for the minor!