NDAK15018U Large-Scale Data Analysis (LSDA)

Volume 2017/2018

MSc Programme in Computer Science


Almost every scientific and industrial field is nowadays faced with massive amounts of data and the field of large-scale data science has become one of the key drivers for data-intensive research and innovation. Taking advantage of this data-rich situation often requires specialized approaches, tools, and skills. What society needs are qualified experts capable of designing, developing, and applying data analysis techniques in the context of big data scenarios. That is, “Data Analytists” 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 is 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 data mining
  • Online and large-scale machine learning
  • Programming paradigms for large-scale data analysis
  • Mining of streaming data
  • Data analysis on (massively-)parallel platforms

Learning Outcome

At course completion, the successful student will have:

Knowledge of

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


Skills in

  • applying efficient algorithms for analyzing large-scale data sets;
  • 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.

See Absalon when the course is set up.

Among others, the freely available textbook “Mining of Massive Datasets” by Jure Leskovec, Anand Rajaraman, and Jeff Ullman published by Cambridge University Press will be used.



Participants should have passed the course "Machine Learning" or similar. Knowledge of basic calculus and statistics is required. Participants should also have knowledge of basic programming and programming languages (in particular Python) or should be willing to spend some extra study time to get familiar with the required programming skills.
Lecture and exercise classes
  • Category
  • Hours
  • Exam
  • 20
  • Lectures
  • 28
  • Practical exercises
  • 70
  • Preparation
  • 18
  • Theory exercises
  • 70
  • Total
  • 206
7,5 ECTS
Type of assessment
Continuous assessment
4-6 weekly take-home exercises.
The final grade will be the average over all assignments except the one with the lowest score.
All aids allowed
Marking scale
7-point grading scale
Censorship form
No external censorship
Several internal examiners.

20 minutes oral exam without preparation in full course syllabus.

If student is not qualified then qualification can be achieved by hand-in and approval of equivalent assignments. The assignments must be submitted no later than two weeks before the re-exam date.

Criteria for exam assesment

See Learning Outcome.