Courses :: Module, TOPICS IN DATA ANALYTICS

	Overview
	Overview of the data science process and data analytics life cycle in industry. Data science methods using software such as R or SAS. Topics such as: data manipulation and cleaning, data visualisation, time series, text mining tools, missing data mechanisms and imputation techniques. Guest Lecturers to present examples of data science projects in industry. Data science group project.

Overview

Overview of the data science process and data analytics life cycle in industry. Data science methods using software such as R or SAS. Topics such as: data manipulation and cleaning, data visualisation, time series, text mining tools, missing data mechanisms and imputation techniques. Guest Lecturers to present examples of data science projects in industry. Data science group project.

	Learning Outcomes
	On successful completion of the module, students should be able to: Understand the life cycle of a data science project and the role of the data scientist within that. Collate and manipulate data in software such as SAS or R. Identify tools to handle the breadth of challenges that can arise within a data science project, such as missing data or time series data or complex data needing visualisation.

Learning Outcomes

	Autumn Supplementals/Resits
	Are supplemental/resit registrations permitted? Yes Students who score less than 40% on this module may repeat the final assessment (only) on a date determined by the Department. In this case, the module mark will be capped at 40%. University scheduled written examination (Autumn): 120 minutes, On-campus

Autumn Supplementals/Resits

	Pre-Requisites
	ST661, CS621C DS663 or DS665

Pre-Requisites

	Co-Requisites
	ST464/ST674/ST684

Co-Requisites

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Courses / Module

TOPICS IN DATA ANALYTICS

Semester 1

Semester 2