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.
Applying and deepening selected subjects as well as further sharpening the critical attitude in a project with real-world data and a real-world challenge.|
Our goal is to advance students’ knowledge and skills on DS & AI with special attention to developing their reflection skills and critical attitude to effectively “see through the hype.”
B-CS students register via Osiris; others can contact firstname.lastname@example.org.
Minor students: please register in Osiris for the minor!