The module provides you with the skills and knowledge necessary to undertake analytical investigations of data, enabling you to understand the nature, utility, and quality of data. The focus is on formulating analysis questions and hypotheses that can be answered using available data. You will learn to draw statistically sound conclusions, utilising appropriate analytical techniques and methods. You will also gain knowledge about how data visualisation facilitates qualitative understanding of information, enabling informed decision-making. By the end of this module, you will have acquired the skills to effectively explore and analyse data, develop data quality guidelines, formulate meaningful analysis questions, and employ data visualisation techniques to support decision-making processes.

Pre-requisite modules: None.

Co-requisite modules: None.

This module is a part of MSc Digital and Technology Solutions ((Integrated Degree Apprenticeship) programme. It cannot be taken as an elective by students on other programmes.

The apprenticeship standard and other documentation relating to the Level 7 Digital and Technology Solutions (Data Analyst Specialist) Apprenticeship can be found here: https://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology-solutions-specialist-integrated-degree/

On completion of this module, you will be able to explore the quality and nature of your data, formulate meaningful analysis questions and hypothesis, effectively analyse available data to find patterns and trends, and employ data visualisation techniques to support your data analysis and decision-making processes. This includes Exploratory Data Analysis applied at early stage of analytical investigation, to summarise the main characteristics of your datasets without hypothesis testing task, for example for data cleaning. You will learn advanced analytics and statistical modelling techniques for making statistically sound decisions for different data scenarios. You will use various visualisation techniques in different stages of your analytical investigation, which help you to visualise and harness the power of data for new insights with high integrity and ethics, enabling informed decision-making.   

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

Discipline Specific Skills and Knowledge

Personal and Key Transferable / Employment Skills and Knowledge

Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:

• Do you know your data?                                                
• Statistical methods and algorithms
• Descriptive statistics and exploratory data analysis
• Overview of data Analytics and its life cycle
• Business intelligence and analytics
• What is Visualisation?
• Overview of visual Analytics and its life cycle
• Advanced statistical data analysis and modelling
• Programming/programmable APIs and associated libraries
• Visualisation classifications, Principles and methods
• Data analysis and visualisation in big data era
• Data and visual ethics with social aspects
• Putting all together

Basic reading:

• Soufian, M. (2014) Notes on STFC Big Data and Analytics Summer School 2014, Daresbury Laboratories, Warrington, UK
• Soufian, M. (2014) Notes on Hartree Visualisation Summer School 2014, Daresbury Laboratories, Warrington, UK
• EMC. (2015), Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, Wiley
• Ceder, N. (2018) The Quick Python Book. Third Edition, Manning Publications Co
• P. Tan, M. Steinbach, V. Kumar(2014) Introduction to Data Mining. Pearson.

Reading list for this module: