Data Analytics
| Degree programme | Business Informatics – Digital Transformation |
| Subject area | Engineering & Technology |
| Type of degree | Master Part-time Summer Semester 2023 |
| Course unit title | Data Analytics |
| Course unit code | 087421020302 |
| Language of instruction | German, English |
| Type of course unit (compulsory, optional) | Compulsory |
| Teaching hours per week | 3 |
| Year of study | 2023 |
| Level of the course / module according to the curriculum | |
| Number of ECTS credits allocated | 6 |
| Name of lecturer(s) | Steffen FINCK, Kathrin PLANKENSTEINER |
- Applied Statistics
- Data Management
- Data analytics procedure models: CRISP-DM, ASUM
- Feature engineering
- Overview, functionality as well as use and orchestration of algorithms
- Text mining and information retrieval
- Prediction and anomaly detection for time series
- Ethical aspects of data analytics
- Data story telling: methods for explaining/interpreting data, models and results
- Outlook on advanced topics: Process mining, data-driven modeling, simulations.
Data analytics forms are the basis for knowledge generation from data and is therefore essential for data-driven innovations and business models. The course focuses on methods for processing time series (e.g. data from IoT systems) and methods for textual data. The aim of the course is to provide students with methodological knowledge of existing algorithms and to enable them to evaluate the results, taking into account the required use of resources. Additionally, ethical issues of data analytics are considered.
The students
- are familiar with procedural models and project management methods for data analytics projects and are able to apply them to plan, monitor and control such projects.
- are able to select and justify procedures for the sub-steps of the analysis process.
- are able to classify different problems, select appropriate algorithms and determine solutions with the help of software.
- know the basic mathematical concepts of the algorithms and can evaluate the results obtained and derive actions based on these.
- Lecture
- Exercises (several projects are group work)
- Presentations of results (incl. findings/action derivations/evaluations)
- Coaching
- Data Story (Individual, 20%)
- Exercises (group, 50%)
- Presentation and discussion (group, 30%)
For a positive grade, a minimum of 50% of the possible points must be achieved in each part of the examination.
- Berry, Michael W.; Kogan, Jacob (2010): Text Mining: Applications and Theory. John Wiley & Sons.
- Cleve, Jürgen; Lämmel, Uwe (2020): Data Mining. Berlin/München/Boston, GERMANY: Walter de Gruyter GmbH. Online im Internet: URL: http://ebookcentral.proquest.com/lib/vorarlberg/detail.action?docID=6370645 (Zugriff am: 15.06.2021).
- Fandango, Armando (2017): Python Data Analysis. Packt Publishing Ltd.
- James, Gareth u.a. (Hrsg.) (2013): An introduction to statistical learning: with applications in R. New York: Springer (= Springer texts in statistics).
- Miner, Gary u.a. (2012): Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications. Academic Press.
- Provost, Foster; Fawcett, Tom (2013): Data Science for Business. O’Reilly Media, Inc. Online im Internet: URL: https://www.oreilly.com/library/view/data-science-for/9781449374273/ (Zugriff am: 01.09.2020).
- Stock, Wolfgang G. (2007): Information retrieval: Informationen suchen und finden ; [Lehrbuch]. München: Oldenbourg (= Einführung in die Informationswissenschaft).
- Vanderplas, Jacob T. (2016): Python data science handbook: essential tools for working with data. First edition. Sebastopol, CA: O’Reilly Media, Inc.
On-site (input, work phases, and exercise discussions) and online (coaching)