Courses / Module

STATISTICAL METHODS FOR DATA SCIENCE 1

Module code: DS663
Credits: 5
Semester: 1
Department: MATHEMATICS AND STATISTICS
International:

	Overview
	This course covers probability and statistical techniques for data analytics and data science. Topics include: Exploratory data analysis and visualisation. Probability basics, independence, Bayes theorem. Probability models for data, including Binomial, Poisson, Exponential and Normal. Confidence intervals and hypothesis testing: one and two samples, paired samples, proportions. Case studies in R.

Overview

This course covers probability and statistical techniques for data analytics and data science. Topics include: Exploratory data analysis and visualisation. Probability basics, independence, Bayes theorem. Probability models for data, including Binomial, Poisson, Exponential and Normal. Confidence intervals and hypothesis testing: one and two samples, paired samples, proportions. Case studies in R.

	Learning Outcomes
	On successful completion of the module, students should be able to: Analyse data using R. Write statistical reports. Model data using probability distributions. Choose and construct appropriate data visualisations.

Teaching & Learning methods

Assessment

	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%.

	Pre-Requisites
	Some calculus

Timetable

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Courses / Module

STATISTICAL METHODS FOR DATA SCIENCE 1

Semester 1

Semester 2