Overview and Aims
This unit provides learners with knowledge and skills for advanced data analytics. It introduces applied analytical models used in business to discover, interpret and communicate meaningful patterns from data, and to derive knowledge for competitive advantage. Learners are assumed to have prior programming knowledge.
Aims:
- Establish a theoretical foundation in data analytics methods and their role in business decision-making.
- Develop an understanding of the issues involved in preparing large datasets for analytical modelling.
- Enable learners to apply descriptive, predictive, and prescriptive analytical techniques using programming tools.
Indicative Content (Summary)
- Data analytics terminology: population, sample, categorical, ordinal, continuous, discrete data.
- Methods: descriptive, predictive, and prescriptive data analytics.
- Exploratory Data Analysis (EDA): variable identification, univariate/bivariate analysis, missing values.
- Data preparation: collection, processing, metadata handling, aggregation, discretisation, data reduction.
- Data visualisation: interactive visualisation, descriptive/inferential statistics, infographics.
- Descriptive techniques: measures of central tendency, probability distributions, statistical inferences.
- Predictive techniques: linear/logistic regression, forecasting (time series, causal methods).
- Prescriptive techniques: optimisation (linear programming, dynamic programming), decision analysis.
Outcomes
Learners will be able to:
- Understand the theoretical foundation of data analytics used in business decision-making.
- Understand issues in preparing a large data set for use in an applied analytical model.
- Be able to apply a range of descriptive, predictive and prescriptive analytic techniques to convert data into actionable insight.
Lessons:
