Study Guide

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.

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

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

Slides on itslearning
Granville- Luca Del Tongo: Data Structures and Algorithms
https://dsa.codeplex.com/
Various internet sources, links & descriptions are provided in itslearning.

Teaching methods

Independent work, online activities (quizzes and problem sets).

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.