732A54 · 6 credits · Advanced level (A1N) · Statistik
Big Data Analytics
Active · next start VT 2027
72%
passed the latest ordinary exam
TENT · 18 Aug 2026 · 18 students
59%
passed the ordinary exams, all sittings over the last 5 years
TENT · 198 students
98%
Harder than 98% of courses at advanced level
Compared with 285 courses at the same level, ordinary exams over the last three academic years. 97 students.
Part of 1 programme
Exam
TENT Examination
- A
- B
- C
- D
- E
- F
Ordinary exams
59% passed · 198 students
- 72%18
- 67%27
- 32%22
- 45%20
- 30%10
- 50%24
- 85%13
- 77%30
- 64%11
- 61%23
Re-exams
55% passed · 33 students
- 40%15
- 29%7
- 91%11
- Pass the re-exam: 55% (33 students), compared with 59% (198 students) on the ordinary exam
- Get B or A: 10% (231 students)
Show small sittings (2)
Sittings with fewer than 5 students, often credit transfers or special sittings. They are not part of any figure on this page.
- 67%3
- 100%3
Other modules
Partial exams, hand-ins and labs. They are not part of the figures at the top.
Hand-in or lab
LAB1 Laboratory work
- P
Main rounds
100% passed · 126 students
- 100%25
- 100%20
- 100%26
- 100%16
- 100%18
- 100%21
Later rounds
100% passed · 9 students
- 100%9
- Pass a later round: 100% (9 students), compared with 100% (126 students) on the ordinary exam
Show small sittings (13)
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%2
- 100%2
- 100%2
- 100%2
- 100%2
- 100%2
- 100%1
- 100%2
- 100%2
- 100%2
- 100%3
- 100%2
About the course
Aim
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
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
- Examiner
- Huanyu Li
- Main field of study
- Statistik