ES1B6-15 Data Analysis II
Introductory description
This module consolidates the first-year data analysis module in order to solve a wide variety of data-driven real-life problems. Students will develop a solid understanding of statistical methods and inferencing that will allow them to effectively analyse data and conduct hypothesis testing for decision making. Students will learn about random variables, probability distributions, one- and two-sample tests, statistical inferencing, hypothesis testing, linear regression, correlation, and analysis of variance (ANOVA). Students will further enhance their knowledge of statistical principles by using an appropriate statistical programming language to implement and analyse data.
Module aims
To equip students with sufficient knowledge and problem-solving skills needed to understand probability distributions, perform statistical inferencing using hypothesis testing, and carry out analysis of variance using the R statistical programming language.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
- Basic Probability Concepts (probability; complements; experiments; outcomes; sample space; independent events; mutually exclusive events; laws of probability – multiplication and addition rule).
- Discrete & Continuous Random Variables.
- Discrete & Continuous Probability Distributions (Binomial & Normal distributions).
- One- and Two- Sample tests.
- Statistical Inferencing and Hypothesis Testing.
- Regression and Correlation.
- Analysis of variance (ANOVA).
- Using the R statistical programming software for data analysis.
Learning outcomes
By the end of the module, students should be able to:
- Demonstrate a good understanding of statistical probability distributions to critically evaluate data and draw appropriate conclusions.
- Conduct hypothesis testing and understand how statistical tests can be used to quantify the amount of evidence in favour of a scientific hypothesis.
- Identify relationships between data variables using correlation and make predictions using regression analysis.
- Use the R statistical programming software to analyse quantitative/qualitative data.
Indicative reading list
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S. L. Weinberg, D. Harel, S. K. Abramowitz, Statistics using R : an integrative approach, Cambridge University Press (2021), ISBN: 9781108719148.
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E. G. M. Hui, Learn R for Applied Statistics, Apress (2019), ISBN: 9781484241998.
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R. J. Barlow, Statistics, A Guide to the Use of Statistical Methods in the Physical Sciences, Wiley (1989), ISBN: 9781118723234
View reading list on Talis Aspire
Subject specific skills
Able to manage data effectively and undertake data analysis;
Communicating mathematically;
Quantitative reasoning;
Logical thinking;
Manipulation of precise and intricate ideas.
Transferable skills
Analytical skills;
Problem-solving;
Flexibility;
Persistence;
A thorough approach to work.
Study time
| Type | Required |
|---|---|
| Lectures | 10 sessions of 1 hour (7%) |
| Seminars | 15 sessions of 1 hour (10%) |
| Tutorials | 5 sessions of 1 hour (3%) |
| Work-based learning | 73 sessions of 1 hour (49%) |
| Online learning (independent) | 10 sessions of 1 hour (7%) |
| Other activity | 2 hours (1%) |
| Private study | 10 hours (7%) |
| Assessment | 25 hours (17%) |
| Total | 150 hours |
Private study description
Inclusive of:
- Online tutor-recorded videos.
- Online Quiz for revision.
- Online forum for discussing queries with course peers and tutor.
Recapping of prior learning is expected where necessary. Reading around the topics covered will provide the depth of understanding required to complete the course to a good standard. This may be both prior to and/or after the teaching and learning sessions. Support from teaching staff is available but students will be expected to increasingly develop their independent learning skills.
Other activity description
- Support Session (Online).
Costs
No further costs have been identified for this module.
You must pass all assessment components to pass the module.
Assessment group A
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
Assessment component |
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| Class Test | 40% | 10 hours | No |
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This assessment will be based on the topics covered in Block 1. |
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Reassessment component is the same |
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Assessment component |
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| Data Analysis Assignment | 60% | 15 hours | Yes (extension) |
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This assessment will be based on the topics covered in Block 1 and Block 2. |
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Reassessment component is the same |
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Feedback on assessment
Feedback will be given as appropriate to the assessment type:
- Written cohort-level summative feedback on class test.
- Individual feedback provided for the data analysis assignment task.
There is currently no information about the courses for which this module is core or optional.