After the course the student will be able to:
1. Indicate important components and tools in the data science ecosystem.
2. Describe and explain the elementary principles of data mining and their application in different contexts and domains.
3. Apply standard data preprocessing and data mining algorithms.
4. Analyze and evaluate elementary data mining experiments.
5. Draw conclusions on the potential and limitations of data, algorithms, and models, and their application in multidisciplinary contexts (e.g. teams, bridging between programmers and strategic management).
Data Mining for Business and Governance will be accessible for all students (no technical background required). During the course, students will complete mandatory assignments in which they will train their basic data mining skills in the domain of social media and behaviour. The experiments and assignments will be performed with open-source data mining software (jupyter, pandas, and scikit-learn). There will be one midterm exam to ensure that students keep on track with the course contents. The course is completed with a written exam.
Data Science methods are becoming the main tools for acquiring information both in business context and in scientific research. The course offers a thorough introduction in the use of data mining for analysis of various domains. Upon completion of the course, students will have acquired the skills necessary to apply data mining to extract information from large data sets and transform it into an understandable structure. In addition, students will be familiarized with advanced topics, including deep learning, time series and graph analyses. The perspective of the course is application-oriented and serves to provide students with the knowledge and experience that is in line with the current demand for skilled data scientists.
- Research papers, see Blackboard.