UC1IPR10 Introduction to Programming

UC1IPR10 Introduction to Programming

  • Course description
    • NQF Level
      Bachelor's degree (Level 6 1. Cycle)
    • Area of Study
      Computing
    • Program of Study
      Digital Assurance and Security Management
    • ECTS
      10
    • Campus
      OnlinePLUS - Bergen, OnlinePLUS - Oslo, Online
    • Course Leader
      Johan Van Niekerk
Used in other programs
  • Digital Assurance and Security Management
Introduction

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

Included in the following bachelor's degrees:

  • Digital Assurance and Security Management
Course Aim(s)

This course aims to provide students with a programming foundation including both theoretical and practical elements. Students are provided knowledge to independently solve basic programming problems in a precise, specification-oriented, approach according to defined unit test standards. In addition, students are introduced to object orientation and selected third-party libraries.

Course Learning Outcomes
Knowledge

The student has knowledge of

K1 methods of problem decomposition.
K2 the use of software design techniques to describe a problem.
K3 the software development process and related methodologies.
K4 foundational programming concepts.
K5 foundational object-oriented concepts.
Skills

The student gain skills in

S1 the ability to abstract a problem.
S2 read, write, and interpret pseudo code and software designs.
S3 independently develop Python applications.
S4 write and read specification, as well as using specification as a step towards the fully implemented software application.
General Competence

The student can demonstrate

G1 a logical scientific approach to software development.
G2 basic programming concepts such as variables, program control, functions, input, output, and collections.
G3 object oriented concept such as classes, instances, and objects.
G4 ability to solve programming problems in a precise, specification-oriented, approach according to defined unit testing standards.
Course Topics
  • Variables and Data Types
  • Control Flow, Functions
  • Data Structures
  • File Handling
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, a webcam, and headphones (or a similar alternative).
Prerequisite Knowledge

Refer to the entry requirements.

Reading List

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

Study Workload

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

ActivityDuration
Teacher-led activity
24 Hour(s)
Teacher-supported work
48 Hour(s)
Self-study
178 Hour(s)
Work Requirements

There are no mandatory assignments in this course.

Assessment Strategy

This course has two (2) exams contributing towards the overall and final grade of the course.

  • F in Portfolio of Work will count towards the final Course Grade.
Form of assessmentGrading scaleGroupingDuration of assessment
Portfolio of Work
A-F
Individual
Online Exam
A-F
Individual