NDAK15018U Large-Scale Data Analysis (LSDA)

Volume 2019/2020
Education

MSc Programme in Computer Science

Content

Almost every scientific and industrial field is nowadays faced with massive amounts of data and analysing these data has become a key driver of research and innovation. Taking advantage of this data-rich situation often requires specialized approaches, tools, and skills. What society needs is qualified experts capable of designing, developing, and applying data analysis techniques in the context of big data scenarios. That is, “Data Analysts” are needed and the goal of this course is to educate such experts.

 

In comparison to other courses dealing with machine learning or data analysis, the focus of this course is on the peculiarities of processing large amounts of data - that is, on Big Data.

 

The course might also be relevant for students from, among others, the studies of Computer Science, Cognition and IT, Bioinformatics, Physics, Statistics, and other areas of quantitative studies.

 

The course covers a selection of the following (tentative topic) list:

 

  • Fundamentals of large-scale data analysis
  • Large-scale machine learning
  • Deep learning

  • Parallel and distributed data analysis

Learning Outcome

At course completion, the successful student will have:

 

Knowledge of

 

  • the general principles of large-scale data analysis;
  • the theoretical concepts underlying large-scale data analysis;
  • common pitfalls in large-scale data analysis.

 

Skills in

 

  • applying efficient algorithms for analyzing large datasets;
  • using programming paradigms for large-scale data analysis;
  • using software tools for large-scale data analysis;
  • identifying and handling common pitfalls in data analysis.

 

Competences in

 

  • recognizing and describing possible applications of large-scale data analysis ("Big Data");
  • comparing, appraising and selecting methods for specific data analysis tasks;
  • solving large real-world data mining problems.
Literature

See Absalon when the course is set up.

 

 

Participants should have passed the course "Machine Learning" or a similar one.

Knowledge of basic calculus and statistics is required. Participants should also have knowledge of basic algorithms and data structures, basic programming, and other basic computer science concepts or should be willing to spend some extra study time to get familiar with the required skills.

Academic qualifications equivalent to a BSc degree is recommended.
Lecture and exercise classes
  • Category
  • Hours
  • Exam
  • 20
  • Lectures
  • 28
  • Practical exercises
  • 70
  • Preparation
  • 18
  • Theory exercises
  • 70
  • Total
  • 206
Written
Continuous feedback during the course of the semester
Credit
7,5 ECTS
Type of assessment
Continuous assessment
4-6 take-home assignments. The final grade will be a weighted average over all assignments.
Aid
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.
Re-exam

The re-exam and the grade consist of two parts: The first part is handing in (potentially revised) solutions to at least 4 of the course assignments no later than 2 weeks before the oral re-exam (35%). The second part is the oral exam in full course syllabus -- 30 minutes including assessment, without preparation (65%).

Criteria for exam assesment

See Learning Outcome.