Basic skills for Data Management (5 cr)

Evaluation scale

H-5

Content scheduling

Weeks 36 - 48:
Introduction to data management
Introduction to Jupyter Notebook
Data storage formats
Basics of linear algebra (vectors, matrices, linear equations)
Data processing and visualization with Python
Basics of virtualization and Linux shell commands
Working with databases with Python
Recap
Week 49: Exam

Objective

After completing the course the student can:
- Describe how data can be managed and processed
- Describe how data can be stored in various places and formats
- Manage and analyze data with suitable tools
- Utilize data management tools to process data
- Understand and describe how mathematics can be used for data management

Content

Introduction to data management
Data storage formats
Data storage
Introduction to data processing
Linear algebra
Data management tools

Materials

Material available via the learning environment (ITS).

Teaching methods

Weekly contact lessons:
- 2h Q&A and theory, where in the 1st hour students can ask about assignments and topics about theory that was unclear
- 2h practice where student work with lesson assignments and home work assignments are presented

Exam schedules

Exam in Week 49.
Retake exams in January and May 2026.

Pedagogic approaches and sustainable development

The course has Q&A and theory lessons and guided assignment lessons where students work with practical tasks.
Assignments are done both during lessons and as home work.
Lesson assignments should be completed during the lessons.
Example answers of lesson assignments are presented during practice sessions but not revealed in ITS.
Home work assignments need be completed within two weeks.
Example answers of home work assignments are revealed after the deadlines and then students complete peer reviews.

Sustainability is integrated in the implementation topics.

Completion alternatives

No.

Student workload

Contact hours
- Course introduction: 2h
- 11 times 1h Q&A: 11 x 1h = 11 hours (groups together)
- 11 times 1h theory: 11 x 1h = 11 hours (groups together)
- 11 times 2h practice: 11 x 2h = 22 hours (in own group)
- Exam: 2 hours
TOTAL: 48 hours

Home and independent work: approximately 85 hours

Evaluation methods and criteria

You can achieve points from lesson and home work assignments and exam.

Lesson assignments (effect 40%):
- Full points if student participates and completes them on time.
- Half points if student do not participate in practice lesson but completes them on time.

Home work assignments (effect 40%):
- Points based on peer and teacher's review if completed on time.
- Half points based on peer and/or teacher's review if completed delayed.

Exam (effect 20%): Points based on exam completed.
Note: Minimum 50% of exam points is required to pass the course!

The course is graded on a scale of 0-5.
Grading will be according to the total points collected by the student during the course as well as the exam.
1: 50% (minimum to pass the course)
2: 60-70%
3: 70-80%
4: 80-90%
5: 90- 100%

Failed (0)

Less than 50% points.

Assessment criteria, satisfactory (1-2)

50 - 69% points.

Assessment criteria, good (3-4)

70 - 89% points.

Assessment criteria, excellent (5)

90 - 100% points.

Further information

Open TUAS: max 5 students

Additional information is shared via ITS that is the main communication channel.

Use of AI in assignments: USE OF AI REPORTED.
AI can be used in the creation of outputs, but student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect to assessment.

Use of AI in exam: USE OF AI PROHIBITED.
The output must be created without the help of AI. The student should use only their own knowledge, understanding and skills. The use of AI is forbidden for a justified reason and will be interpreted as fraud.