Academic Handbook Data and Artificial Intelligence
Intermediate Programming with Data Course Descriptor
Last modified on February 2nd, 2023 at 12:05 pm
| Course code | LDSCI4210 | Discipline | Computer & Data Science |
| UK credit | 15 | US credit | 4 |
| FHEQ level | 4 | Date approved | November 2022 |
| Core attributes | Analysing and Using Data (AD) | ||
| Pre-requisites | LDSCI4211 Programming with Data | ||
| Co-requisites | None | ||
Course Overview
This course advances the students’ skills in Python programming for data science, preparing them for more advanced courses in data science. It equips students with sufficient programming experience to start making practical contributions to data science projects in a real-world setting.
Learning Outcomes
On successful completion of the course, students will be able to:
Knowledge and Understanding
| K1a | Demonstrate knowledge and understanding of basic concepts in data science, including natural language processing, machine learning and data visualisations. |
| K2a | Demonstrate knowledge and understanding of basic software design principles in Python. |
| K3a | Demonstrate knowledge and understanding of basic programming practices, tools, and techniques for software development and data science projects. |
Subject Specific Skills
| S1a | Identify, formulate and solve simple problems in machine learning, natural language processing and data visualisation, addressing basic considerations surrounding data management, ethical data use, safety, equality, diversity, inclusion, and sustainability. |
| S2a | Apply basic concepts and techniques in machine learning, natural language processing and data visualisation to a dataset. |
| S3a | Apply basic software design concepts, including encapsulation and inheritance, in software development. |
Transferable and Employability Skills
| T3a | Effectively use of IT facilities and programming frameworks to support projects. |
| T3a | Display a developing technical proficiency in written English and an ability to communicate clearly and accurately in structured and coherent pieces of writing. |
Teaching and Learning
This course has a dedicated Virtual Learning Environment (VLE) page with a syllabus and range of additional resources (e.g. readings, question prompts, tasks, assignment briefs, discussion boards) to orientate and engage students in their studies.
The scheduled teaching and learning activities for this course are:
- Lectures/labs. 40 scheduled hours – typically including induction, consolidation or revision, and assessment activity hours:
- Version 1:All sessions in the same sized group, or
- Version 2: most of the sessions in larger groups; some of the sessions in smaller groups
Faculty hold regular ‘office hours’, which are opportunities for students to drop in or sign up to explore ideas, raise questions, or seek targeted guidance or feedback, individually or in small groups.
Students are to attend and participate in all the scheduled teaching and learning activities for this course and to manage their directed learning and independent study.
Indicative total learning hours for this course: 150
Assessment
Both formative and summative assessment are used as part of this course, with purely formative opportunities typically embedded within interactive teaching sessions, office hours, and/or the VLE.
Summative Assessments
| AE: | Assessment Activity | Weighting
(%) |
Duration | Length
(words) |
| 1 | Set Exercises | 40 | 24-32 hours | |
| 2 | Written Assignment | 40 | 2,000 | |
| 3 | Presentation | 20 | 15 min. |
Further information about the assessments can be found in the Course Syllabus.
Feedback
Students will receive formative and summative feedback in a variety of ways, written (e.g. marked up on assignments, through email or the VLE) or oral (e.g. as part of interactive teaching sessions or in office hours).
Indicative Reading
Note: Comprehensive and current reading lists are produced annually in the Course Syllabus or other documentation provided to students; the indicative reading list provided below is for a general guide and part of the approval/modification process only.
- Paul Deitel and Harvey Deitel. 2019. Intro to Python for Computer Science and Data Science: Learning to Program with AI, Big Data and The Cloud. Pearson.
Indicative Topics
Note: Comprehensive and current topics for courses are produced annually in the Course Syllabus or other documentation provided to students; the indicative topics provided below are used as a general guide and part of the approval/modification process only.
- Object-oriented programming in Python
- Machine learning applications in Python
| Title: LDSCI4210 Intermediate Programming with Data
Approved by: Dr Alison Statham Location: academic-handbook/programme-specifications-and-handbooks/undergraduate-programmes |
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| Version number | Date approved | Date published | Owner | Proposed next review date | Modification (as per AQF4) & category number |
| 1.0 | November 2022 | January 2023 | Dr Alexandros Koliousis | November 2027 | |