Data Structures and AlgorithmsLaajuus (5 cr)
Code: 5051128
Credits
5 op
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
Qualifications
Introduction to Programming, or equivalent programming skills
Enrollment
04.12.2024 - 13.01.2025
Timing
13.01.2025 - 30.04.2025
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
0 - 45
Teachers
- Ali Khan
Scheduling groups
- Group 1 (Size: 40. Open UAS: 0.)
- Group 2 (Size: 40. Open UAS: 0.)
Groups
-
PTIVIS23HHealth Technology
Small groups
- Group 1
- Group 2
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.
International connections
The course has 12 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.
Completion alternatives
Not possible
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
Content scheduling
Week 2: Course introduction
Session from Weeks 2 - 15
- Algorithms and algorithmic thinking
- Data structures
- Search algorithms
- Sorting algorithms
Contact hours according to lukkari.turkuamk.fi.
Further information
ITS and Teams.
Evaluation scale
H-5
Assessment 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.
Assessment criteria, fail (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
Enrollment
01.06.2024 - 06.09.2024
Timing
02.09.2024 - 18.12.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
20 - 60
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Information and Communications Technology
Teachers
- Ali Khan
Teacher in charge
Noora Maritta Nieminen
Groups
-
PTIVIS23SEmbedded Software and IoT
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.
International connections
The course has 12 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.
Completion alternatives
Not possible
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
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.
Further information
ITS and Teams.
Evaluation scale
H-5
Assessment 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.
Assessment criteria, fail (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
Enrollment
02.12.2023 - 31.12.2023
Timing
01.01.2024 - 30.04.2024
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- Finnish
- English
Seats
20 - 50
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Information and Communications Technology
Teachers
- Noora Maritta Nieminen
Teacher in charge
Noora Maritta Nieminen
Groups
-
PTIVIS22PGame and Interactive Technologies
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
Lecture and practice session material in ItsLearning, e.g. PowerPoint presentations, Jupyter notebook / Python files, etc.
All material is provided in English
Teaching methods
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
Exam schedules
Midterm 1 will be held in February (close to winter break), topics are "Algorithms and complexity" and "Basic data structures"
Midterm 2 will be held at the end of the course in April, topics are "Advanced data structures" and "search algorithms"
You will have the chance to retake midterm 1 at the end of the course when you also take the midterm 2 exam.
There is one retake exam for the entire course at the beginning of May.
International connections
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. Practical understanding is gained through coding exercises. We will use Python as our main coding language.
Completion alternatives
Ask teacher, if there is an ongoing suitable online course (FiTech / Coursera or equivalent).
CampusOnline courses ARE NOT accepted!
Student workload
Lectures 2h / week (all three groups together)
Practice sessions 2h / week (three small groups)
Individual work outside school: reading, studying, preparing the weekly programming practice tasks
Content scheduling
January– April 2024
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
Further information
ItsLearning
Email
Evaluation scale
H-5
Assessment methods and criteria
The grade will be based on the following criteria:
- 2 midterm exams
- homework activity
- attendance to lectures and practice sessions
More detailed description provided at the first lecture and in ItsLearning
Assessment criteria, fail (0)
Student fails to meet most of the general objectives of the course in a satisfactory level.
Assessment criteria, satisfactory (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
Assessment criteria, good (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.
Assessment criteria, excellent (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.
Qualifications
Introduction to Programming, or equivalent programming skills
Enrollment
30.11.2022 - 18.01.2023
Timing
09.01.2023 - 30.04.2023
Number of ECTS credits allocated
5 op
Mode of delivery
Contact teaching
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Information and Communications Technology
Teachers
- Noora Maritta Nieminen
Groups
-
PTIVIS21PGame and Interactive Technologies
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
Evaluation scale
H-5
Qualifications
Introduction to Programming, or equivalent programming skills
Enrollment
02.12.2021 - 31.12.2021
Timing
01.01.2022 - 30.04.2022
Number of ECTS credits allocated
5 op
Virtual portion
3 op
Mode of delivery
40 % Contact teaching, 60 % Distance learning
Unit
Engineering and Business
Campus
Kupittaa Campus
Teaching languages
- English
Seats
25 - 60
Degree programmes
- Degree Programme in Information and Communication Technology
- Degree Programme in Information and Communications Technology
Teachers
- Tapani Ojanperä
Teacher in charge
Tapani Ojanperä
Groups
-
PTIVIS20PGame and Interactive Technologies
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
Granville- Luca Del Tongo: Data Structures and Algorithms
https://dsa.codeplex.com/
Various internet sources, links & descriptions are provided in itslearning environment.
Teaching methods
Independent work, online activities (quizzes and problem sets) in itslearning.
Exam schedules
One exam in the end of the course. The schedule of the exams will be in itslearning.
Two re-exams.
International connections
The course material is totally in English. Data Structures and Algorithms are very crucial to know in order to make successful programs. Besides some of the students are international ones. Material consists of PowerPoint slides, Word and PDF files. In addition, there are links to different web materials, e.g. lectures and presentations in YouTube and in some other places in Internet. The course consists of rounds of topics (learning material & quiz - homework - teacher review). There will be a specified time slot of each round. Some rounds have quizzes. Each quiz will consist of 4-6 quick questions, where the student needs to choose an answer out of 4 available ones, or answer yes or no. After every section there will be a homework assignment to solve some algorithmic problems related to the current subject.
Student workload
Material reading 60h
Homework (quizzes and exercises) 60h
Preparing exam 15h
Total 135h
Content scheduling
January– April 2022
January: Algorithmic Thinking and Analysis
February: Basic Data Structures
March: Advanced Data Structures
April: Sorting Algorithms, Search Algorithms, exam
• Arrays, Linked List, Stack, Queue
• Hash table, Trees, Graphs
• Bubble, Selection, Insertion, Merge and Quick Sort
methods
• Binary search, Pathfinding, Shuffling
Further information
Python (or C#) and MatLab are recommended and used, but other common languages are acceptable in homeworks.
All practical information on schedules, quizzes, problems, grading etc., as well as links to web materials are provided in itslearning.
Evaluation scale
H-5
Assessment methods and criteria
Quizzes 20 %
Quizzes (5) in itslearning diagnostic/formative instant scoring
Problem Sets 30 %
Different Problem Sets (7) in itslearning, diagnostic/formative self and teacher evaluation
Final exam 50 %
A written exam (90 minutes) on specified material
Summative teacher evaluation at the end of the course
Assessment criteria, fail (0)
Student fails to meet most of the general objectives of the course in a satisfactory level.
Assessment criteria, satisfactory (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
Assessment criteria, good (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.
Assessment criteria, excellent (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.
Qualifications
Introduction to Programming, or equivalent programming skills