Official course description:
Full info last published 15/11-21

### Algorithms and Data Structures

##### Course info
Language:
English
ECTS points:
7.5
Course code:
BSALDAS1KU
Participants max:
95
Offered to guest students:
no
Offered to exchange students:
no
Offered as a single subject:
no
##### Programme
Level:
Bachelor
Programme:
BSc in Data Science
##### Staff
Course manager
Associate Professor
Teacher
Full Professor
Semester
Forår 2022
Start
31 January 2022
End
31 August 2022
##### Abstract
This course provides the basic algorithmic tools indispensable for every software developer.
##### Description

Computers help us compute things: To sort alphabetically the entries in a telephone directory; to compute the next frame of a video game; to find the seats available on an airplane. However, there are faster and slower ways to compute things. To be an effective programmer, you must know not only how to make a computer compute things, but how to efficiently compute things.This course provides the basic algorithmic tools indispensible for every software developer.
Topics covered are among others complexity analysis, big-O, algorithmic problem solving techniques including divide-and-conquer, concrete algorithms and data structures for search trees, sorting, hashing, graphs, shortest paths.

##### Formal prerequisites

Basic ability to program in some imperative programming language (Java, Python, C/C++/C#, etc.), using conditions, loops, arrays, methods/procedures/functions, simple recursion and abstract data types (interfaces). This is normally obtained by following the first semester undergraduate course Introduction to Data Science and Programming (BSc DS) or Introductory Programming (BSc SWU or MSc SD.)

Knowledge of basic mathematical concepts like: sets, functions, graphs, and trees, arithmetic and geometric series. You are expected to know this from your high school education. We will make up for any lacks in that respect on the way. Information about the course of study. This course is mandatory for students who are enrolled on BSc in Software Development, BSc in Data Science and MSc in Software Design and part of the second semester. Moreover the student must always meet the admission requirements of the IT University.

##### Intended learning outcomes

After the course, the student should be able to:

• Discuss and clearly explain the mechanics of computations and data structures involving manipulation of references, nested loops and recursion, specified in natural language, in abstract pseudocode or in concrete programming language (Java/Python)
• Implement abstractly specified computations and data structures in an imperative programming language (Java/Python).
• Analyze time and space usage of algorithms/programs.
• Assess scalability of a given single-threaded software application, using asymptotic analysis.
• Choose among and make use of the most important algorithms and data structures in libraries, based on knowledge of their complexity.
• Design algorithms for ad hoc problems by using and combining known algorithms and data structures
• Describe the most important hardware and programming language factors influencing the speed at which a program runs and take them into account in assessment of speed of algorithms.
##### Learning activities

We will spend 6 hours a week on lectures and exercises.
You are expected to work systematically. The course gives plenty of opportunities to gain hands-on experience with solving problems, with implementing algorithms and with using them.

##### Mandatory activities

The course has 14 mandatory assignments, all of which must be completed and approved before the student can take the examination.

If the submission for a mandatory assignment is late or considered to be insufficient, it is not approved. However, make-up opportunities are available in these cases.

Detailed descriptions of the mandatory activities are available on the course website

The student will receive the grade NA (not approved) at the ordinary exam, if the mandatory activities are not approved and the student will use an exam attempt.

##### Course literature

The course literature is published in the course page in LearnIT.

##### Student Activity Budget
Estimated distribution of learning activities for the typical student
• Preparation for lectures and exercises: 10%
• Lectures: 20%
• Exercises: 20%
• Assignments: 30%
• Exam with preparation: 15%
• Other: 5%
##### Ordinary exam
Exam type:
A: Written exam on premises, External (7-point scale)
Exam variation:
A33: Written exam on premises on paper with restrictions
Exam duration:
4 hours
Aids allowed for the exam:
Written and printed books and notes

##### Time and date
Ordinary Exam - on premises Wed, 25 May 2022, 09:00 - 13:00
Ordinary Exam - on premises Wed, 25 May 2022, 09:00 - 13:00
Reexam - on premises Fri, 12 Aug 2022, 09:00 - 13:00