Overview
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Learning activities
Learning outcomes
Describe and implement fundamental statistics for data analysis.
Describe and implement a range of data analysis methods and predictive modelling techniques that are appropriate to individual datasets.
Describe the basic ideas and techniques underlying modern data science applications.
Implement basic statistics, data pre-processing, visualization, and analysis techniques using the R programming language.
Assessments
Additional information
· Introduction to data science · Data pre-processing · Data exploration and visualization · Introduction to basic statistics and machine learning techniques: regression, classification and clustering · Popular data science applications · R practices
A minimum of 10 hours per week of personal study for completing workshop activities, reading materials, assignments, private study and revision. Each student is required to complete assignments and an examination.