MA334-7-SP-CO:
Data analysis and statistics with R

The details
2021/22
Mathematics, Statistics and Actuarial Science (School of)
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 17 January 2022
Friday 25 March 2022
15
28 October 2021

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

MSC G30512 Applied Data Science,
MSC G30524 Applied Data Science,
MSC G305JS Applied Data Science

Module description

The module will introduce concepts from data analysis and statistics and show how they can be applied effectively via the R language. It will cover a wide introduction to statistics and provide practical experience of real-world examples of how statistics is used to gain insights.

Throughout these examples, and many more, we will teach programming techniques that will enable students to apply statistical approaches to real-world applications.

This module assumes no previous exposure to statistics.

Module aims

The purpose of this module is to introduce:

Data analysis.
Statistics.
The use of R for data analysis and statistics.

Module learning outcomes

A. A systematic, extensive and comparative knowledge and understanding of the use of R for carrying out statistical analysis
B. A systematic, extensive and comparative knowledge and understanding of data analysis methods
C. A systematic, extensive and comparative knowledge and understanding of statistical methods

Module information

Basic ideas of probability and statistical distributions
Random variables, means, covariance and variance
Variance of a sample mean and confidence intervals for means, variances and differences between means
Conditional probability and independence
Probability distribution theory
Standard distributions and their use in modelling: including Bernoulli, Binomial, Poisson
Estimation and Maximum Likelihood estimators
Hypothesis tests concerning means and variances
Null and alternative hypotheses
Type I and type II errors
Test statistic and critical region
Probability value and level of significance
Using tables of the t, F and chi-squared distributions
Introduction to linear regression
The least square estimates of the intercept and the slope of a simple linear regression
Confidence intervals for the slope parameter and prediction intervals for response
Coefficient of determination and the sample correlation coefficient

Learning and teaching methods

This module has 35 contact hours that will be structured as follows: Lectures: 20 hours Classes: 10 hours Computer labs: 5 hours

Bibliography

This module does not appear to have a published bibliography for this year.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Final Project & Presentation    100% 

Exam format definitions

  • Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
  • In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
  • In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
  • In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary, for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.

Your department will provide further guidance before your exams.

Overall assessment

Coursework Exam
100% 0%

Reassessment

Coursework Exam
100% 0%
Module supervisor and teaching staff
Dr Dan Brawn, email: dbrawn@essex.ac.uk.
Dr Dan Brawn
dbrawn@essex.ac.uk

 

Availability
Yes
Yes
Yes

External examiner

Prof Fionn Murtagh
University of Huddersfield
Professor of Data Science
Dr Yinghui Wei
University of Plymouth
Resources
Available via Moodle
Of 1722 hours, 30 (1.7%) hours available to students:
1692 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Mathematics, Statistics and Actuarial Science (School of)

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