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Archived Curricula Guide 2017–2019
Curricula Guide is archieved. Please refer to current Curricula Guides
Advanced Studies in Big Data Analytics, 85 ECTS

Learning outcomes

The advanced studies in Computational Big Data Analytics make the student familiar with statistical and computational methods suitable for big data analytics, so that the student can choose suitable data analysis methods for each analysis task at hand, apply these methods to analyze the data, and use efficient computational and statistical methods to manage and analyze big data.

The available advanced studies include teaching computational and statistical analysis methods for several kinds of data and several analysis tasks, methods that are necessary for integrating data from different data sources during data preprocessing and/or analysis, and methods for visualizing the data / analysis results.

Content

Compulsory courses and courses in Methods of Computational and Statistical Data-Analytics.

Evaluation

Numeric 1-5.

Belongs to following modules

Faculty of Natural Sciences
Master's Degree Programme in Computational Big Data Analytics
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Advanced Studies in Big Data Analytics

Compulsory Advanced Courses in Big Data Analytics 40 ECTS
MTTTS11 Master's Seminar and Thesis, 40 ECTS
Advanced Courses in Methods of Computational Data Analytics 20– ECTS
20 ECTS
TIETS01 Algorithms, 5 ECTS
TIETS07 Neurocomputing, 5 ECTS
TIETS11 Data Mining, 5 ECTS
TIETS31 Knowledge Discovery, 5–10 ECTS
TIETS39 Machine Learning Algorithms, 5 ECTS
TIETS42 Databases and Information Retrieval Integration, 5 ECTS
TIETS43 Recommender Systems, 5 ECTS
TIETS44 Big Data Entity Resolution, 5 ECTS
TIETS33 Advanced Course in Computer Science, 1–10 ECTS
Advanced Courses in Methods of Statistical Data Analytics 25– ECTS
25 ECTS
Compulsory Courses 5–15 ECTS
5–15 ECTS
One or more of the following three courses.
Optional Courses 10–20 ECTS
10–20 ECTS
Sufficient amount of credits from the courses below.
Faculty of Natural Sciences
Degree Programmes