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

Belongs to following modules

Advanced Studies in Big Data Analytics

Compulsory Advanced Courses in Big Data Analytics 50 ECTS

MTTTS11 Master's Seminar and Thesis, 40 ECTS

MTTTS12 Introduction to Bayesian Analysis 1, 5 ECTS

TIETS01 Algorithms, 5 ECTS

Advanced Courses in Methods of Computational Data Analytics 15– ECTS

15 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

TIETS33 Advanced Course in Computer Science, 1–10 ECTS

TIETS43 Recommender Systems, 10 ECTS

Advanced Courses in Methods of Statistical Data Analytics 20– ECTS

20 ECTS

MTTTS13 Introduction to Bayesian Analysis 2, 5 ECTS

MTTTS14 Statistical Modeling 1, 5 ECTS

MTTTS15 Statistical Modeling 2, 5 ECTS

MTTTS16 Learning from Multiple Sources, 5 ECTS

MTTTS17 Dimensionality Reduction and Visualization, 5 ECTS

MTTTS18 Time Series Analysis 1, 5 ECTS

MTTTS19 Advanced Regression Methods, 5 ECTS

MTTTS21 Statistical Inference 2, 5 ECTS

MTTS1 Other course (advanced),