Data Structures and Algorithms (5 cr)

Evaluation scale

H-5

Content scheduling

Week 36: Course introduction

Session from Weeks 36 - 48
- Algorithms and algorithmic thinking
- Data structures
- Search algorithms
- Sorting algorithms

Contact hours according to lukkari.turkuamk.fi.

Objective

After completing the course the student can:
- explain the most common data structures
- apply the most common data structures and algorithms connected to the use of these structures
- evaluate the efficiency of algorithms.

Content

- lists, stacks, queues, trees, graphs and hash tables
- analysing and evaluating algorithms
- designing algorithms
- sorting methods
- search algorithms

Materials

Material available via the learning environment (ITS).

Teaching methods

Weekly contact 3 hours sessions for theory and practical exercises.
Additionally, if needed weekly 1h sessions for questions and support in exercises.

Exam schedules

No exam, and retake not possible after evaluation grade is published.

Pedagogic approaches and sustainable development

The course has 13 three-hour contact sessions where teacher present theory and examples and students work with practical tasks.
Additionally, students are able to receive extra guidance for exercises.

Electronic materials are used in the course. In addition, guidance is also organized online in order to reduce the carbon footprint caused by movement.

In teaching this course, active pedagogic methods like constructivist learning, flipped classroom, and project-based learning help students develop problem-solving, collaboration, and critical thinking skills. Continuous assessment and differentiated instruction support diverse learners.

Sustainable development is integrated by highlighting algorithm efficiency and its impact on energy consumption, promoting inclusive education, and encouraging innovation through real-world applications. Example activities include analyzing the environmental impact of algorithms or designing memory-efficient data structures for low-power devices.

This approach ensures students gain both technical expertise and awareness of their role in building sustainable digital solutions.

Completion alternatives

Self-paced learning

Student workload

Contact hours
- Course introduction: 3 hours
- 13 times 2h theory: 13 x 2h = 26 hours
- 13 times 1h demo 13 x 1h = 13 hours - Group 1
- 13 times 1h demo 13 x 1h = 13 hours - - Group 2
- FLIP Classroom 10 X 1h = 10h
Home work:
- Working with assignments: approximately 80 hours


Total: approximately 130 hours

Evaluation methods and criteria

The course is graded on a scale of 0-5.

You can achieve 80 points from practical exercises in class room and home work exercises.
Around half of the exercises are done during the contact hours.

Demonstrations of exercises during the contact session is mandatory without demonstration you will lose 50% of your marks.


Additionally, there is a group project of 20 points, passing group project is mandatory to pass the course.

Lastly, to pass the course the student need to get at least 40 marks in the exercises and at least 10 marks in the project.

Failed (0)

Less than 50% points in the exercises OR Student does not passed the group project.

Assessment criteria, satisfactory (1-2)

50 points -> 1
60 points -> 2

Assessment criteria, good (3-4)

70 points -> 3
80 points -> 4

Assessment criteria, excellent (5)

90 points -> 5

Qualifications

Introduction to Programming, or equivalent programming skills

Further information

ITS and Teams.

USE OF ARTIFICIAL INTELLIGENCE REPORTED
Allowed, can be used, must be reported. Artificial intelligence can be used in the creation of outputs, but the student must clearly report its use. Failure to disclose the use of AI will be interpreted as fraud. The use of AI may affect the assessment.