[ 536DASCBMDAK19 ] KV Basic Methods of Data Analysis

Workload Education level Study areas Responsible person Hours per week Coordinating university
3 ECTS B2 - Bachelor's programme 2. year (*)Artificial Intelligence Sepp Hochreiter 2 hpw Johannes Kepler University Linz
Detailed information
Original study plan Bachelor's programme Artificial Intelligence 2023W
Objectives Data analysis and visualization are essential to most fields in science and engineering. The goal of this course is to provide students with a basic tool chest of methods for pre-processing, analyzing, and visualizing scientific data.
  • Scatter plots and box plots
  • Basics of classification
  • Basics of regression
  • Clustering
  • Principal component analysis (+ visualization, including spectral maps)
  • Singular value decomposition (+ visualization)
  • Matrix factorization (+ visualization)
  • Independent component analysis
Criteria for evaluation The final grade is based on a combined assessment of homework and a final exam.
Methods Slide presentations complemented by online demos and examples presented on the blackboard; demos are based on the scientific computing platform R
Language English
Study material Electronic course material and example code are made available for download
Changing subject? No
Earlier variants They also cover the requirements of the curriculum (from - to)
875BIMLMDAK16: KV Basic Methods of Data Analysis (2016W-2019S)
675MLDAMDAK13: KV Basic Methods of Data Analysis (2013W-2016S)
On-site course
Maximum number of participants 35
Assignment procedure Direct assignment