ES1B5-15 Data Analysis I
Introductory description
This module prepares students to gain data analysis skills that will be useful for their course and workplace. Students will develop a solid understanding of statistical techniques to analyse data using a variety of data exploration techniques. Students will learn about data, population & samples, random & non-random sampling techniques, descriptive statistics, measures of central tendency, measures of spread, and appropriate visualization for data. Students will further enhance their knowledge of the statistical principles by using a variety of statistical tools/software to carry out data analysis. The R programming language will be taught to allow students familiarise themselves with different data types, data structures, and data manipulation, in order to carry out statistical modelling.
Module aims
To equip students with the knowledge and problem-solving skills needed to effectively perform statistical analysis, data visualisation and data manipulation using appropriate statistical software.
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.
- Introduction to Data (survey data; sources of data; data collection; types of data; data structures; quality, quantity, relevance and limitations of the data).
- Population; Sample and Random/Non-Random Sampling Techniques.
- Descriptive Statistics: Measures of central tendency (mean; median; mode) & Measures of spread (standard deviation; variance; quartiles; range; inter-quartile range).
- Outliers (cleaning data).
- Visualising data (Graphical display – Bar chart; Histogram; Line chart; Scatter plot; Box plots).
- Introduction to R statistical programming software.
- Working with large datasets using R and Microsoft Excel for data analysis (data entry; importing data; data manipulation).
Learning outcomes
By the end of the module, students should be able to:
- Understand how to describe and summarise present data using a range of statistical methods.
- Identify and critique the use of statistical sampling techniques in a variety of contexts.
- Use appropriate statistical softwares to analyse data and implement data visualisation to study trends and patterns.
- Interpret and communicate the results of data exploration.
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 | 5 sessions of 1 hour (3%) |
| Seminars | 10 sessions of 1 hour (7%) |
| Tutorials | 15 sessions of 1 hour (10%) |
| 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.
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.