Mathematics of Data Analytics

Course summary

Course code: MATH1146
Level: 6
Credits: 15
School: Architecture, Computing and Hums
Department: Mathematical Sciences

Specification

Pre and co requisites

Pass 120 credits at level 5

Aims

Data analytics is one of the most fast-moving and exciting areas of contemporary mathematics. Employers of mathematics graduates frequently require graduates to be able to manipulate and present data in a variety of ways to meet the needs of employers, use various software and have evidence of programming skills. This course seeks to address these requirements; providing students with these much wanted skills as well as giving them the confidence that, given a piece of mathematical software/new programming language, they will be able to teach themselves how to use it.

Learning outcomes

On successful completion of this course a student will be able to:

1 Critically evaluate the differences between a variety of mathematical methods and software for storing data and make appropriate choices for different applications.

2 Apply the regulations and ethical considerations surrounding data protection, data mining and warehousing.

3 Create macros to perform a variety of functions used by practitioners in mathematical data analysis.

4 Critically evaluate different data visualisation tools and techniques and present data in a variety of ways to aid senior management in decision making.

5 Explain the need for cleaning data and apply a variety of techniques for this purpose.

6 Be familiar with the advanced techniques for data manipulation in spreadsheets such as the use of Pivot Tables and Lookup functions.

Indicative content

Understanding how senior management use data to inform decision making.
Cleaning data; rationale, theory, practice in a variety of industries.
Excel shortcuts such as ‘trim’, ‘left’ etc leading on to Advanced Excel Techniques such as Pivot Tables and Lookup functions.
Databases for storing data using SQL and Access.
VBA for creating Macros.
Theory of data visualisation and software.
Introduction to data mining, data warehousing and its applications.
Data Protection and ethical issues.

Teaching and learning activity

Lectures (including flipped classroom approach where appropriate)
Lab sessions
Directed guided learning
Student investigation

Assessment

Summative assessment:
Data project coursework - 60%
LO - 1,3,5,6
Pass mark - 40%
Investigative coursework working with a data set and performing a number of functions such as Pivot Table, Macro, Database.

Data visualisation coursework - 40%
LO - 2,4
Pass mark - 40%
Create a deliverable on the dataset to enable management to visualise the key messages from the data set.
Discuss the need for data protection and the impact of this on those storing data and performing data mining.

Formative assessments - Weekly lab tasks.