Big Data Analytics, 6 credits

Analys av Big data, 6 hp


Main field of study


Course level

Second cycle

Course type

Programme course


Olaf Hartig

Course coordinator

Olaf Hartig

Director of studies or equivalent

Patrick Lambrix
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Weeks Timetable module Language Campus ECV
F7MSL Statistics and Machine Learning, Master´s Programme - First and main admission round 2 (Spring 2023) 202313-202322 1 English Linköping, Valla C
F7MSL Statistics and Machine Learning, Master´s Programme - Second admission round (open only for Swedish/EU students) 2 (Spring 2023) 202313-202322 1 English Linköping, Valla C

Main field of study


Course level

Second cycle

Advancement level


Course offered for

  • Master's Programme in Statistics and Machine Learning

Entry requirements

  • 180 ECTS credits passed including 90 ECTS credits in one of the following subjects:
    • statistics
    • mathematics
    • applied mathematics
    • computer science
    • engineering
  • Passed courses in:
    • calculus
    • linear algebra
    • statistics
    • programming
  • English corresponding to the level of English in Swedish upper secondary education (Engelska 6)
    Exemption from Swedish
  • At least 6 ECTS credits passed from semester 1 Master's Programme in Statistics and Machine Learning, or the equivalent

Intended learning outcomes

After completed the course, the student should on an advanced level be able to:
- collect and store Big Data in a distributed computer environment
- perform basic queries to a database operating on a distributed file system
- account for basic principles of parallel computations
- use MapReduce concept to parallelize common data processing algorithms
- account for how standard machine learning models should be modified in order to process Big Data
- use tools for machine learning for Big Data


Course content

The course introduces main concepts and tools for storing, processing and analyzing Big Data which are necessary for professional work and research in data analytics.

- Introduction to Big Data: concepts and tools
- Introduction to Python
- Basic principles of parallel computing
- Introduction to databases
- File systems and databases for Big Data 
- Querying for Big Data 
- Resource management in a cluster environment
- Parallelizing computations for Big Data 
- Basic Machine Learning algorithms
- Machine Learning for Big Data 

Teaching and working methods

The teaching comprises lectures and computer exercises. Lectures are devoted to presentations of theories, concepts and methods. Computer exercises provide practical experience of manipulation with Big Data. Homework and independent study are a necessary complement to the course. Language of instruction: English. 


Written reports on the computer assignments. Written examination. Detailed information about the examination can be found in the course’s study guide. 

If special circumstances prevail, and if it is possible with consideration of the nature of the compulsory component, the examiner may decide to replace the compulsory component with another equivalent component.

If the LiU coordinator for students with disabilities has granted a student the right to an adapted examination for a written examination in an examination hall, the student has the right to it.

If the coordinator has recommended for the student an adapted examination or alternative form of examination, the examiner may grant this if the examiner assesses that it is possible, based on consideration of the course objectives.

An examiner may also decide that an adapted examination or alternative form of examination if the examiner assessed that special circumstances prevail, and the examiner assesses that it is possible while maintaining the objectives of the course.

Students failing an exam covering either the entire course or part of the course twice are entitled to have a new examiner appointed for the reexamination.

Students who have passed an examination may not retake it in order to improve their grades.



Other information

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. 

The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

If special circumstances prevail, the vice-chancellor may in a special decision specify the preconditions for temporary deviations from this course syllabus, and delegate the right to take such decisions.


Institutionen för datavetenskap
Code Name Scope Grading scale
LAB1 Laboratory work 3 credits EC
TENT Examination 3 credits EC
There is no course literature available for this course in studieinfo.

This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page.

There are no files available for this course.