UC2ADS10 Algorithms and Data Structures

UC2ADS10 Algorithms and Data Structures

  • Course description
    • NQF Level
      Bachelor's degree (Level 6 1. Cycle)
    • Area of Study
      Computing
    • Program of Study
      Applied Data Science
    • ECTS
      10
    • Campus
      Kristiansand, OnlinePLUS - Bergen, OnlinePLUS - Oslo, Online
    • Course Leader
      Sahar Yassine
Introduction

Language of Instruction and assessment: English
May be offered on Campus and Online.
May be offered as a separate course.

Included in the following bachelor's degrees:

  • Applied Data Science
Course Aim(s)

This course aims to introduce a number of algorithms that can be used to solve a variety of computational problems, including data sorting and searching, and approaches to evaluating algorithmic complexity and efficiency. It will also introduce a variety of data structures to which these algorithms can be applied, including arrays, stacks, queues, trees and graphs.

Course Learning Outcomes
Knowledge

The student has knowledge of

K1 fundamental data types and algorithms used in computing.
K2 a selection of advanced algorithms used in computing, across a variety of subject domains.
Skills

The student gain skills in

S1 ability to select and implement appropriate data structures and algorithms within practical environments to solve problems.
S2 evaluate strengths, weaknesses and efficiency of algorithms.
S3 critically reflect upon academic development and lessons learned through experience in implementing data structures and algorithms.
General Competence

The student can demonstrate

G1 selecting and applying a variety of data types and algorithms available to computer professionals.
G2 present and explain key data structures and algorithms in an professional manor.
G3 ability to develop insights into, and critically evaluate, current development in the field.
Course Topics
  • Algorithms and Algorithmic Techniques
  • Fundamentals of Data Types
  • Trees and Graphs
  • Advanced Algorithms
Teaching Methods
  1. Teaching will be based on a hybrid-flexible approach. Instructor-led face-to-face learning is combined with online learning in a flexible course structure that gives students the option of attending sessions in the classroom, participating online, or doing both.
  2. All activities require active student participation in their own learning.
  3. Learning delivery methods and available resources will be selected to ensure constructive alignment with course content, learning outcomes and assessment criteria.
  4. Students will be taught using a mixture of guidance, self-study, and lecture material. Topics will be introduced in a series of weekly lectures. The guidance sessions will be directed practical exercises and reading in which students can explore topics with support from a teacher. This material will also require students to self-manage their time to ensure tasks are completed and the theory is fully understood. This will allow the students to fully engage with lectures and with their peers.
Resources and Equipment
  1. Learning resources are available in the LMS and include, but is not limited to:
    • literature and online reading material (essential and recommended)
    • streams, recordings and other digital resources, where applicable
    • video conferencing and communication platforms, if applicable
    • tools, software and libraries, where applicable
  2. Students must have access to an internet connection, and suitable hardware.
    • Accessing live streams and virtual laboratories requires a minimum broadband connection of 2Mbps (4Mbps recommended).
  3. Students working on their own laptop/computer are required to acquire appropriate communications software, e.g., webcam, microphone, headphones.
Prerequisite Knowledge

UC1PR210 Programming and Databases, or equivalent course(s).

Reading List

The reading list for this course and any additional electronic resources will be provided in the LMS.

Study Workload

250 nominal hours.
Study workload applies to both Campus and Online students.

ActivityDuration
Teacher-led activity
24
Teacher-supported work
48
Self-study
178
Work Requirements

There are no mandatory assignments in this course.

Assessment Strategy

This course has three (3) exams contributing towards the overall and final grade of the course.

All exams must be assessed as passed to receive the final Course Grade.

Form of assessmentGrading scaleGroupingDuration of assessment
Online Test
A-F
Online Test
A-F
Online Exam
A-F