Study Guide

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 sessions when 3 hours for theory and practical exercises.

Exam schedules

The exam will be arranged in week 49.

International connections

The course includes approximately 12 theory sessions and guided exercises sessions where students work with practical tasks.
Additionally, exercises for home work that will be partly demonstrated in during contact sessions.

Student workload

Contact hours
- 12 times 3h theory and practice: 12 x 3h = 36 hours
- Exam: 2 hours
Home work: approximately 90 hours
Total: approximately: 130 hours

Content scheduling

Week 36 - 48
- Introduction to data management
- Introduction to Jupyter Notebook
- Data storage formats
- Databases
- Git basics
- Data management with Python libraries
- Basics of Linear algebra
- Recap
Week 49:
- Exam

Further information

ITS.

Evaluation scale

H-5

Assessment methods and criteria

The course is graded on a scale of 0-5.
*
You can achieve a maximum of 100 points from practical exercises in class room and home work exercises.
Around half of the exercises are done during the contact hours.
*
Additionally, there is an exam, you need to pass.

Assignments affect to grading 60% and exam 40%.

Assessment criteria, fail (0)

General: Less than 50 points in exercises and exam not passed (less than 40% points).

Assessment criteria, satisfactory (1-2)

50 - 69 points in exercises and 45% - 64% points in the exam.

Assessment criteria, good (3-4)

70 - 84 points in exercises and 60% - 80% points in the exam.

Assessment criteria, excellent (5)

85 - 100 points in exercises and at least 80% points in the exam.