The ability to competently handle data sets and analytic tools is a key attribute of the professional data analyst. Underlying this ability is a solid grounding in statistics that allows data analysts to successfully interact with various methods of testing, interpretation and validation.

This module will give you an introduction to the relevant mathematical and statistical tools. You will also learn the techniques necessary for data analysis as well performing correctives thereon.

You will build upon your existing knowledge of statistical parameters and distributions to learn further methods of professional data analysis. Knowledge of univariate methods of descriptive statistics will be deepened over the course of the module. In addition, you will gain insight into inductive methods as well as multivariate methods.

You will also be able to name the components of an ETL process and explain the basics of using databases.  

An introduction to common statistics software packages will give you a view of the options available. The initial package used will be Microsoft Excel. You will then progress to the widely used statistics program released by Project R. Concurrently, you will get to grips with the basics of coding, e.g. in "R" and "Python" and you will have the chance to put that knowledge to use in small team-based projects.

• Teamwork assignment (50%)  

• Written examination (40%) 

• Course participation (10%) - multiple little quizzes and exercises in Moodle