Opinto-opas

Tavoitteet

Opintojakson suoritettuaan opiskelija osaa:
- selittää tavallisimpia tietorakenteita
- hyödyntää keskeisiä tietorakenteita sekä niiden käyttöön liittyviä algoritmeja
- arvioida algoritmien suoritustehokkuutta.

Sisältö

- listat, pinot, jonot, puut, verkot ja hajautustaulut
- algoritmien analysointi
- algoritmien suunnittelu
- järjestämistekniikat
- hakualgoritmit

Oppimateriaalit

Lecture and practice session material in ItsLearning, e.g. PowerPoint presentations, Jupyter notebook / Python files, etc.
All material is provided in English

Opetusmenetelmät

Attending to lectures: teacher will provide both theoretical background and practical examples on a dedicated topic
Attending practice sessions: the theory is put into action in practice - we will see solutions to previous exercises + practice new topics
Individual work: Completing the assignments individually after face-to-face sessions

Tenttien ajankohdat ja uusintamahdollisuudet

No exam, retake not possible after the publication of the final assessment/course grade

Pedagogiset toimintatavat ja kestävä kehitys

The course has 14 two-hour contact sessions where teacher present theory and examples, and 12 two-hour practice sessions where students work with practical tasks.

Electronic materials are used in the course.

Toteutuksen valinnaiset suoritustavat

Ask teacher, if there is an ongoing suitable online course (FiTech / Coursera or equivalent).
CampusOnline courses ARE NOT accepted!

Opiskelijan ajankäyttö ja kuormitus

Lectures 2h / week x 14 = 28h
Practice sessions 2h x 12 = 24h
Individual work outside school: reading, studying, preparing the weekly programming practice tasks: 80h

Total: approx. 80h

Sisällön jaksotus

Understanding data structures and algorithmic efficiency / complexity are essential in many ways. During this course, the student will gain both theoretical and practical understanding on these topics. Students will learn to use pseudocode / flowcharts to describe algorithms and analyze their complexity (time and space, both experimental and theoretical). We will gain a comprehensive insight to elementary data structures and their algorithms.

Practical understanding is gained through coding exercises. We will use Python as our main coding language.

January– April 2025

January: Algorithmic Thinking and Analysis
• pseudocode, flowcharts
• efficiency and algorithmic complexity

February: Basic Data Structures
• Arrays, Linked List, Stack, Queue

March: Advanced Data Structures
• Hash table, Trees, Graphs

April: Sorting Algorithms, Search Algorithms
• Bubble, Selection, Insertion, Merge and Quick Sort

Viestintäkanava ja lisätietoja

ItsLearning
Email

Arviointiasteikko

H-5

Arviointimenetelmät ja arvioinnin perusteet

The grade will be based on the following criteria:
- practical work (at the end of the course)
- homework activity (continuous, weekly assignments)
- attendance to lectures and practice sessions

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.

Hylätty (0)

Student fails to meet most of the general objectives of the course in a satisfactory level.

Arviointikriteerit, tyydyttävä (1-2)

• has an elementary understanding on the performance of algorithms and in simple cases is able to apply some methods of analysis covered during the course.
• is familiar with some major algorithms and data structures covered in the course.
• has a basic understanding on how to apply the algorithmic design parameters covered in the course.
• has an elementary understanding on data representation.
• demonstrates some understanding on how to decompose programming problems in a purposeful way.
• can use most elementary data structures appropriately

Arviointikriteerit, hyvä (3-4)

• can analyze the performance of simple algorithms and is able to apply some of the methods of analysis covered during the course.
• is familiar with most of the major algorithms and data structures covered in the course.
• has an understanding of the algorithmic design parameters covered in the course.
• has a good understanding on data representation.
• demonstrates ability to decompose programming problems in a somewhat purposeful way.
• can choose and use elementary data structures appropriately in most cases.

Arviointikriteerit, kiitettävä (5)

• can analyze the performance of algorithms and is able to apply all the methods of analysis covered during the course.
• is familiar with all the major algorithms and data structures covered in the course.
• is able to apply the algorithmic design parameters covered in the course.
• has a deep understanding on data representation.
• demonstrates ability to decompose programming problems in a purposeful way.
• can choose and use elementary data structures appropriately.

Esitietovaatimukset

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