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

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.