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Academic Handbook MSc Artificial Intelligence and Ethics

Theory and Applications of Data Analytics Course Descriptor

Course Code LDSCI7236 Discipline Computer Science
UK Credit 15 US Credit N/A
FHEQ Level 7 Date Approved June 2023
Core Attributes N/A
Pre-requisites None
Co-requisites None

Course Summary

This course provides an introduction to the fundamental concepts and techniques of data analytics. The course will cover programming and data analytic skills in high-level programming languages such as Python, as well as fundamental concepts of data structures and algorithms. Students will gain an understanding of the data science workflow including data collection from structured and unstructured sources building the foundations of relational and non-relational databases, multi-dimensional arrays, linear algebra transformations, hypothesis testing, regression analysis, machine learning and data visualisation. There is a particular focus on resource efficiency and sustainable development.

Course Aims

The aims of the course are to: 

  • Develop familiarity with an interactive computing and development environment.
  • Develop a basic understanding of arrays and vectorised computation.
  • Develop and manifest an elementary understanding of data structures and their functionality, and of methods of data transformation.
  • Be able to load and clean data sets, summarise and compute descriptive statistics, and plot and visualise data.

Learning Outcomes

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

Knowledge and Understanding

K1d Demonstrate a comprehensive understanding and knowledge of data science concepts and master their implementation in data analytic applications.
K2d Demonstrate critical awareness of feasible operations and transformation on data, and their relationships in current data processing pipelines.
K3d Demonstrate a degree of originality in plotting and visualising data in an effective manner.
K4d Critically review and identify key capabilities and limitations in data science practices, and propose directions for further innovation.

Subject Specific Skills

S1d Critically evaluate basic data science concepts in their application for solving complex data problems.
S2d Critically evaluate the requirements and limitations of data transformations techniques to the chosen dataset.
S3d Demonstrate the ability to identify and implement efficient data science techniques to the area of application and produce clear and concise and well documented code.
S4d Identify appropriate data science practices within a professional, legal and ethical framework for addressing data management and use, security, equality, diversity and inclusion (EDI) and sustainability issues.

Transferable and Professional Skills

T1d Demonstrate initiative in leading and participating in teams for delivering data science projects in a timely manner and according to specification.
T2d Consistently display an excellent level of technical proficiency in written English and command of scholarly terminology, so as to be able to deal with complex issues in a sophisticated and systematic way.
T3d Demonstrate initiative in working independently, effectively, and to deadlines.
T4d Communicate effectively to both technical and non-technical audiences through oral presentations, software demonstrations, and written reports.

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 you in your studies.

The scheduled teaching and learning activities for this course are:

Lectures/labs. Contact hours are typically a mix of weekly lectures and lab sessions:

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

Employability Skills

  • Skills in writing and analysing complex code.
  • Presentation skills in presenting code accordingly.
  • Skills in organisation of written and coding discourse
  • Skills in being able to read, understand and comprehend the code



Formative assessment will build on the material taught in the classroom notebooks. The material will be in the form of end of session exercises and in some cases questions and answers. Oral explanations are also part of summative assessment.


Students will be assessed during the course by means of set assignments. Assessment will be in two forms:

AE: Assessment Activity Weighting (%) Online submission Coding Notebook Submission
1 Coding Assignment 50% No Yes Code and 2500 word explanation
2 Coding Assignment 50% No Yes Code and 2500 word explanation

The examination will consist of two written coding assignments which the student will have to do to the set guidelines for coding. The written assignment will be assessed in accordance with the assessment aims set out in the Programme Specification.


Students will receive formal feedback in a variety of ways: written (including via email correspondence); oral (within one-to-one tutorials or on an ad hoc basis) and indirectly through discussion during group tutorials. Student’s will also attend the formal meeting, Collections in which they will receive constructive and developmental feedback on their  performance. 

Feedback is provided on written assignments (including essays, briefings and reports) and through generic internal examiners’ reports, both of which are posted on the University’s VLE.

Indicative Reading

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


  • Joel Grus (2019), Data Science from Scratch, 2nd ed., O’Reilly: Boston.
  • Wes McKinney (2017), Python for Data Analysis, 2nd ed., O’Reilly: Boston.
  • Hadrien Jean (2020), Essential Math for Data Science, O’Reilly: Boston.

Electronic Resources

Students can visit courses on Datacamp, Coursera and Udemy to watch videos on Python Programming.

Indicative Topics

  • IPython: An Interactive Computing and Development Environment
  • NumPy Basics: Arrays and Vectorised Computation 
  • Pandas
  • Data Transformation
  • Summarising and Computing Descriptive Statistics
  • Plotting and Visualisation
  • Advanced Pandas – Data Aggregation and Group Operations.
Title: LDSCI7236 Theory and Applications of Data Analytics Course Descriptor

Approved by: Academic Board

Location: Academic Handbook/Programme specifications and Handbooks/ Postgraduate Programme Specifications

Version number Date approved Date published Owner Proposed next review date Modification (As per AQF4) & category number
1.0 June 2023 June 2023 Alexandros Koliousis April 2028  
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