CSC1033 : Foundations of Data Science
CSC1033 : Foundations of Data Science
- Offered for Year: 2025/26
- Module Leader(s): Dr John Colquhoun
- Lecturer: Dr Dan Nesbitt
- Teaching Assistant: Mrs Mahdieh Zaker, Mrs Chinomnso Ekwedike
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 10 |
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
N/A
Co-Requisite
Modules you need to take at the same time
| Code | Title |
|---|---|
| CSC1034 | Programming Portfolio 1 |
| CSC1035 | Programming Portfolio 2 |
Co Requisite Comment
N/A
Aims
This module will provide students with an understanding of information storage and retrieval. This relates to all forms of data, including text and multimedia (image, video and audio) stored on and consumed from the web, amongst other sources. The module covers fundamental techniques and strategies of information storage and retrieval used in a variety of online applications such as web- search engines and business storage and analytics.
Outline Of Syllabus
• Retrieval, browsing, user information needs, and other core concerns.
• Notions of structured, unstructured and semi-structured data.
• Data representation (XML, character sets, images, audio/video).
• Relational databases, SQL.
• A generic architecture for information retrieval.
• Spiders/crawlers, stopwords and keywords, indexing and stemming.
• Query expansion and its relationship with the Semantic Web.
• Metadata and semantics, faceted classifications, and other "linked data" issues.
• Information models, databases and data normalization for transactional systems (OLTP).
• Data de-normalization, data marts / data warehouses, star and snowflake schemas, as support for
analytical systems (OLAP) as support to Business Intelligence.
• Introduction to AI and Large Language Models (LLM).
• The challenges presented by "Big Data".
• NoSQL and Cloud Computing for distributed and scalable treatment of "Big Data".
• Exemplar applications, including publishing archives, web-based search engines.
• Data Ethics.
Learning Outcomes
Intended Knowledge Outcomes
At the end of this module students will be able to:
• Explain theories behind search and assess the impacts on search performance inherent in variations in their
construction.
• Elaborate a range of techniques for analysing, modelling, and retrieving data.
• Contrast different kinds of applications, and their integration, in satisfying specific user information
needs.
• Elaborate, contrast and evaluate information models that support efficient storage, retrieval and browsing,
in a variety of applications.
• Contrast the need for efficiency of data storage with the needs of batch access to large datasets.
• Apply appropriate, standard, metadata sets and semantics to ensure effective data storage and curation.
• Identify the important features for storage, retrieval and browsing of different forms of data.
Intended Skill Outcomes
At the end of this module students will be able to:
• Apply data storage and retrieval techniques to typical systems encountered in computing.
• Will be able to analyse and design the data needs of systems.
• Will be able to communicate the technical requirements and analysis.
Further practical skills related to this material are developed in the co-requisite modules Portfolio 1 and Portfolio 2.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 4 | 1:00 | 4:00 | Formative exercises (mock tests, quiz questions-non compulsory). |
| Guided Independent Study | Assessment preparation and completion | 69 | 1:00 | 69:00 | Lecture follow-up. |
| Guided Independent Study | Assessment preparation and completion | 12 | 1:00 | 12:00 | Revision for semester 2 exam. |
| Scheduled Learning And Teaching Activities | Lecture | 33 | 1:00 | 33:00 | Lectures (in person) or if this is stopped, we will instead release video. |
| Guided Independent Study | Assessment preparation and completion | 1 | 1:30 | 1:30 | Semester 2 examination. Will be online. |
| Guided Independent Study | Assessment preparation and completion | 12 | 1:00 | 12:00 | Semester 1 Assessed Coursework. There will be no return to the Semester 1 examination. |
| Scheduled Learning And Teaching Activities | Practical | 23 | 1:00 | 23:00 | Practicals (in person). |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 11 | 0:30 | 5:30 | Online Q&A session/drop-in with module staff. Also to be used as coursework clinic in Semester 1. |
| Guided Independent Study | Independent study | 1 | 40:00 | 40:00 | Background reading and independent study. |
| Total | 200:00 |
Teaching Rationale And Relationship
Techniques and theory are presented in lectures. Practical sessions provide experience of designing and building database applications and can be carried out online.
This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by practical classes, and on the related Programming Portfolio modules.
The new online drop-in/clinic sessions give students additional support and chances to talk to staff members. This will include students who are not present in Newcastle.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 90 | 2 | A | 50 | Exam. |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Case study | 1 | M | 50 | Assessed Coursework covering Semester 1 taught material. |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written exercise | 2 | M | Mock Test prior to Exam to consolidate student knowledge ahead of summative exam. |
Assessment Rationale And Relationship
The written examination in Semester 2 will assess the fundamental knowledge and understanding of Semester 2 taught material.
Semester 1 will be assessed with a piece of coursework allowing the students to apply the theory taught in lectures to a given scenario.
A mock test will take place in Semester 2 to enable the students to prepare for the examination.
The portfolio modules will also use elements from this module enabling further practice for the students.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC1033's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CSC1033's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2025 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
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