***DEGREE APPRENTICESHIP STUDENTS ONLY***

The primary role of a data analyst is to collect, organise and study data to provide new business insight. They are responsible for providing up-to-date, accurate and relevant data analysis for the organisation. They are typically involved with managing, cleansing, abstracting and aggregating data across the network infrastructure. They have a good understanding of data structures, software development procedures and the range of analytical tools used to undertake a wide range of standard and custom analytical studies, providing data solutions to a range of business issues. They document and report the results of data analysis activities making recommendations to improve business performance. They need a broad grounding in technology solutions to be effective in their role.

The aim of this module is to give you foundation skills in data analysis, including the fundamentals of data extraction and preparation and an introduction to the the use of a range of analysis techniques that can be used to derive useful information from data.

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

Introduction (2 weeks)

•          The steps involved in data analysis projects and tasks

•          The importance of the domain context for data analytics

•          The organisation's data architecture

•          Understanding and drawing conclusions from data

•          Defining customer requirements

•          Case studies (retail; healthcare; finance)

Revision: database (2 weeks)

•          Data structures

•          Database and database system design

•          Database system implementation and maintenance

•          Data definition and data manipulation language

•          Ad hoc queries

Data preparation (3 weeks)

•          Extracting, transforming and loading data

•          Validating and cleansing data

Analysing data to derive inferences and to identify and predict trends and patterns (5 weeks)

•          Common statistical techniques

•          Analysing small and large data sets

•          Structured vs unstructured data

•          Data mining

•          Forecasting

•          Using tools for data analysis e.g., SQL, Microsoft Excel, SPSS, SAS, R

•          Introduction to advanced techniques e.g. cognitive computing, machine learning

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be deferred in the assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you may be required to sit a referral. The mark given for a re-assessment taken as a result of referral will be capped at 40%.

ELE: vle.exeter.ac.uk

Reading list for this module: