Data Analytics

Semester 1:

Introduction to Applied Statistics  (5 Credits)

This module introduces the student to some fundamental statistical concepts, to probability and sampling mechanisms as well as basic methods in descriptive and inferential statistics and regression.

Database Design and Implementation  (5 Credits)

This module will introduce the student to the principles and practice of designing and implementing database systems. The student will gain competence in designing relational databases using Entity Relationship Modelling. They will implement relational databases using SQL data definition language. They will query the relational database using SQL data manipulation language. The students will be introduced to the concepts and use of NoSQL databases.

Data Ethics  (5 Credits)

This module provides students with a solid grounding in applied data ethics by considering the ethical and legal challenges surrounding contemporary data acquisition, governance and analytic practices.

Semester 2:

Data Analysis 1  (5 Credits)

This module will introduce the student to statistical techniques in data analysis, with a particular focus on linear models. Statistical software (such as Python or R) will be used in the application of techniques studied.

Business Intelligence Visualisation  (5 Credits)

The fundamental area of Business Intelligence (BI) is the skill to effectively communicate analysis, supporting a firm’s decision makers. The aim of this module is how BI visualises and analyses a firm's data. It builds on the skills learnt in a previous module from the creation of insights from structured and unstructured data. Visualization will facilitate the understanding of data and publish required metrics and key performance indicators (KPIs) relevant to a business. The approach of this module will enable visualisation for accessing, analysing, managing and interacting with data.

Data Security  (5 Credits)

This module provides the essentials of data security. Topics covered include relevant security threats and vulnerabilities and the services available to address these threats. Cryptographic foundations that underpin many security mechanisms are covered.  Network security is covered in the context of ensuring secure access to data in motion. Security-enabled frameworks like Distributed Ledger Technology (also known as blockchain) and privacy preserving techniques are highlighted.

Semester 3:

Data Analysis 2  (5 Credits)

This module will build on statistical modelling techniques introduced in Data Analysis 1 and introduce the student to some machine learning techniques for data analysis. Statistical software such as Python or R will be used in the application of the techniques studied.

Advanced NoSQL Databases  (5 Credits)

This module will facilitate the student to understand the application of NoSQL databases in organisations and gain knowledge and practical experience that enables them to analyse, design and construct complex NoSQL database solutions to handle large volumes of either structured, semi structured or unstructured data. The student will gain competence about NoSQL databases and be able to exercise judgments in using different types in a centralised or distributed manner to solve problems. Specifically, the student will be introduced to the concepts of CAP, Sharding and Replication and be able to independently appraise their relevance and application in regard to NoSQL database development.

Semester 4

Building Data Science Models  (10 Credits)

This module introduces the student to the practice of data science, where techniques and algorithms from mathematics and statistics, supplemented with advanced data infrastructures and processes, are used to learn from data. In this context learning includes understanding and prediction, and the data might not have been collected for the purpose of such analysis.  The student will be introduced to the machine learning pipeline, which provides a principled, mathematically rigorous way to build data science models. 

With these models, and the solution algorithms covered in the module, the student can extract understanding and knowledge from the data. The practical part of the module will present a suite of machine learning exercises that the student will complete by building data science models, making predictions, validating those predictions, and interpreting the results.

Data Analytics Project (10 Credits)

The purpose of this module is to enable the student to apply the knowledge, skills and competencies gained through the academic phase of the course through putting them into practice in a relevant workplace environment. This will allow the students to consolidate, deepen and contextualise their learning and thereby enhance their employability. If a student does not work in a data-rich environment then every effort will be made to find a suitable work placement for them. Where an appropriate work placement is not possible then the student will conduct a project based on data from an industry source under the guidance of an academic supervisor.

The minimum entry requirements for

• Computing or mathematics graduates will be a Level 7 qualification plus one year’s post qualification experience
• Non-computing and non-mathematics graduates will be a Level 8 qualification.

Some applicants who do not immediately meet these requirements may wish to consider using SETU’s Recognition of Prior Learning mechanism in order to make an application that can be considered for entry to this course.

The primary aim of this programme is to produce highly skilled graduates with industry-ready skills in data analytics. The skills will be transferrable across a range of industries such as traditional manufacturing, biopharmaceutical, healthcare or financial services.

Should graduates wish to continue their education then they will be eligible for entry into MSc programmes in cognate disciplines.