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Academic Handbook BSc (Hons) Digital and Technology Solutions

Intensive Foundations of Computer Science and Programming I Course Descriptor

Course Title Intensive Foundations of Computer Science and Programming I Faculty EDGE Innovation Unit (London)
Course code NCHNAP445 Course Leader Professor Scott Wildman (interim)
Credit points 15 Teaching Period This course will typically be delivered over a 6-week period.
FHEQ level 4 Date approved June 2020
Compulsory/
Optional 
Compulsory
Prerequisites None

Course Summary

This course introduces the fundamental ideas of computing and programming principles. The course discusses a systematic approach to word problems, including analytic reading, synthesis, goal setting, planning, plan execution, and testing. It presents several models of computing, beginning with functional program design. Learners will explore the Python programming language, its syntax, mathematical functionality and suitability for data analysis applications.

Course Aims

  • Train learners in the fundamentals of computing and programming principles.
  • Train learners in Python programming.
  • Give learners the tools to design and implement basic Python programmes. 

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

K1a Understand the underlying basic concepts and principles associated with programming languages.
K2a Understand the basic syntax and structure of a Python programme.
K3a Use Python file input/output functions to work with directories and files.

Subject Specific Skills

S1a Write, test and correct basic programs that others can read, understand and modify.
S2a Break large problems into an appropriate design for implementation.
S3a Select appropriate data types to represent information.

Transferable and Professional Skills

T1ai Test, evaluate and identify errors in coding.
T1aii Display a developing technical proficiency in written English and an ability to communicate clearly and accurately in structured and coherent pieces of writing.
T2a Appreciate the impact of data structure and algorithm choice on the running time and storage space needed to run a programme.
T3a Understand professional and ethical issues and guidelines.

Teaching and Learning

This is an e-learning course, taught throughout the year.

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

  • On-line learning
  • On-line discussion groups
  • On-line assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

  • Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
  • On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
  • Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

Assessment will be in two forms:

AE   Assessment Type Weighting Online submission Duration Length
1 Set programming exercises 50% Yes Requiring on average 15-25 hours to complete N/A
2 Practical skills assessment (workplace dataset) 50% Yes Requiring on average 15-25 hours to complete N/A

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Learners will also attend a formal meeting with their Academic Mentor (and for apprentices, including their Line Manager). These bi- or tri-partite reviews will monitor and evaluate the learner’s progress.  

Feedback is provided on all summatively assessed assignments and through generic internal examiners’ reports, both of which are posted on the VLE.

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books

  • Summerfield, M. (2009), Programming in Python 3: A Complete Introduction to the Python Language, Upper Saddle River, NJ: Addison-Wesley
  • Lutz, M. (2011), Programming Python, Beijing; Farnham: O’Reilly
  • Allen, B. (2015), Think Python: How to Think Like a Computer Scientist. Farnham: O’Reilly

Journals

Learners are encouraged to read material from relevant journals on Computer Systems and Programming as directed by their trainer.

Electronic Resources

Learners are encouraged to consult relevant websites on Computer Systems and Programming.

Indicative Topics

  • Basic syntax and semantics of Python
  • Variables and primitive data types
  • Sequential and binary search algorithms
  • Stacks and Queues

Version History

Title: NCHNAP445 Intensive Foundations of Computer Science and Programming I

Approved by: Academic Board

Location: Academic Handbook/BSc (Hons) Digital & Technology Solutions 

Version number Date approved Date published  Owner Proposed next review date Modification (As per AQF4) & category number
3.0 October 2022 January 2023 Scott Wildman June 2025 Category 1: Corrections/clarifications to documents which do not change approved content or learning outcomes

Category 3: Changes to Learning Outcomes

2.1 May 2022 May 2022 Scott Wildman September 2025 Category 1:
Corrections/clarifications to
documents which do not
change approved content.
2.0 January 2022 April 2022 Scott Wildman June 2025 Category 3: Changes to Learning Outcomes
1.0 June 2020 June 2020 Scott Wildman June 2025
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