Tentalabbet

TDDE31 · 6 credits · Advanced level (A1F) · Informationsteknologi, Datateknik, Datavetenskap

Big Data Analytics

Active · next start VT 2027

75%

passed the latest ordinary exam

TEN1 · 2 Jun 2026 · 44 students

74%

passed the ordinary exams, all sittings over the last 5 years

TEN1 · 209 students

83%

Harder than 83% of courses at advanced level

Compared with 285 courses at the same level, ordinary exams over the last three academic years. 77 students.

Part of 10 programmes

Exam

TEN1 Written exam

  • 5
  • 4
  • 3
  • U

Ordinary exams

74% passed · 209 students

  • 75%44
  • 67%33
  • 72%43
  • 73%33
  • 80%56

Re-exams

52% passed · 67 students

  • 57%7
  • 82%11
  • 80%5
  • 33%6
  • 67%6
  • 40%10
  • 56%9
  • 38%8
  • 0%5
  • Pass the re-exam: 52% (67 students), compared with 74% (209 students) on the ordinary exam
  • Get 4 or 5: 35% (276 students)

Other modules

Partial exams, hand-ins and labs. They are not part of the figures at the top.

Hand-in or lab

LAB1 Labs

  • G

Main rounds

100% passed · 201 students

  • 100%46
  • 100%32
  • 100%40
  • 100%26
  • 100%57

Later rounds

100% passed · 6 students

  • 100%6
  • Pass a later round: 100% (6 students), compared with 100% (201 students) on the ordinary exam
Show small sittings (4)

Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.

  • 100%1
  • 100%1
  • 100%4
  • 100%4

About the course

Aim

After completed 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 the MapReduce concept to parallelize common data processing algorithms
  • be able to modify standard machine learning models in order to process Big Data
  • use tools for machine learning for Big Data

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
  • Basic principles of parallel computing
  • File systems and databases for Big Data
  • Querying for Big Data
  • Resource management in a cluster environment
  • Parallelizing computations for Big Data
  • Machine Learning for Big Data
Examiner
Huanyu Li
Scheduled hours
42 h
Main field of study
Informationsteknologi, Datateknik, Datavetenskap
Syllabus on Studieinfo ↗