This module aims to introduce students to the essential concepts, tools, and applications of data science across industries, fostering an understanding of its interdisciplinary nature and societal impact.

It provides students with the skills to develop data-driven solutions for practical business needs, offering an overview of the data science pipeline and key considerations for successful implementation.

It will also equip students with operational mathematics, statistics, and programming to enable effective problem-solving and support the acquisition of core data science knowledge.

1. Research and critically evaluate the stages of a data science pipeline and assess their application in addressing complex business requirements.
2. Apply core mathematical, statistical, and programming techniques to design, implement and critically evaluate appropriate data manipulation procedures.
3. Use predictive modelling to design and develop robust data science solutions for challenging and changeable real-world scenarios.
4. Demonstrate autonomy, originality and effective collaboration in planning, executing, and evaluating data science solutions within professional and academic contexts.
5. Interpret and communicate analytical results for diverse stakeholders using narratives to structure information, reporting methods and contextualisation.

Overview of data science process and lifecycle; Implementation of data strategy for business - key elements and considerations; Understanding data sources and types; Mathematics basics- number theory, variables, expressions and functions; Programming basics – data representations, conditional statements, flow controls, built-in and self-defined functions, data structures; Data pre-processing – cleaning, wrangling, transformation; Descriptive statistics – populations, samples, measures of central tendency & variability, distributions; Inferential statistics - confidence intervals, p-value, testing; Data analysis, correlation and visualisation; Predictive modelling with linear regression.

Coursework 1: 50% weighting, 50% pass mark.
Learning Outcomes: 1, 2 & 4
Word Length: 2750
Outline Details: Case study focussing on understanding, pre-processing and organising data around organisational strategic needs.

Coursework 2: 50% weighting, 50% pass mark.
Learning Outcomes: 3, 4 & 5
Word Length: 2750
Outline Details: Case study focussing on data exploration, modelling and communication of insights to aid business decisions making