Introduction to data science
| Module title | Introduction to data science |
|---|---|
| Module code | GEO1419 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Jo Browse (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 11 |
| Number students taking module (anticipated) | 120 |
|---|
Module description
This module will give you practical insights into how scientists test hypotheses using data. We start with simple methods to describe data and move on to more advanced ways of comparing data and describing data trends. Once you finish the module you will be competent in managing data and handling data, describing data, and running basic statistical tests using the statistical programming language R.
This module uses a combination of lectures, group discussions, supervised practical classes and online (ELE) teaching resourcesto provide you with the support necessary for achieving the module learning objectives. Weekly lectures provide a theoretical overview of the techniques covered in each week’s practical class, while group discussions will evaluate and critique the use of these techniques in published scientific studies. Each practical class is led by lecturing staff, and support staff. The emphasis is placed upon learning how to apply statistical techniques to answer research questions in geography, environmental science and marine science using numerical data of various forms. As such, data from a range of environmental applications are provided in the practical classes for analysis. The module is assessed by 6 weekly quizzes , completed online, Practical classes focus on developing essential programming where R is used for data manipulation and analysis, allowing you to learn key transferable skills in data science..
Module aims - intentions of the module
This module will introduce you to the use of data-centred quantitative analysis techniques in research. The module will establish the purpose and scope of statistical analysis methods, focusing on analytical tests and their execution.
Through lectures, assisted practical classes, and group discussion you will be encouraged to evaluate and critique statistical methods as one of a suite of analytical techniques available to researchers. Assisted practical classes complement the lecture series and will provide you with key transferable skills in data handling which will increase your future employability. These skills are relevant for a range of different careers from environmental management and assessment through to energy policy
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Describe and critique a range of approaches to managing data
- 2. Use basic commands to manage and manipulate data in R
- 3. Calculate and understand the use of basic descriptive statistics including the mean, median, mode, standard deviation and coefficient of variation
- 4. Discuss the limitations associated with different descriptive statistics in your own and others work
- 5. Apply appropriate techniques to determine whether data are normally distributed and explain the role of gaussian distributions in statistical approaches
- 6. Explain the difference between parametric and non-parametric tests.
- 7. Choose the correct statistical test for different data distributions
- 8. Use the statistical programming language R , to apply appropriate statistical test in order to answer research questions.
- 9. Understand statistical significance and interpret p-values
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 10. Describe essential facts and theory across data management and analysis in geography and the natural sciences
- 11. Identify and implement, with some guidance, appropriate methodologies and theories to answer research questions in geography and the natural sciences
- 12. With guidance, deploy established techniques of data science including analysis and management within geography and the natural sciences
- 13. Describe and begin to evaluate approaches to the development of research questions in geography and the natural sciences with reference to primary literature, reviews and research articles
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 14. Develop, with guidance, a hypothesis which can be answered via quantitative methods
- 15. Collect and interpret appropriate data and undertake straightforward research tasks with guidance
- 16. Evaluate own strengths and weaknesses in relation to professional and practical skills identified by others
- 17. Reflect on learning experiences and summarise personal achievements
Syllabus plan
There will be several key themes covered in this module as follows:
- Lecture: Introduction to module and overview of subject
- Practical: working with data in R
- Lecture: Descriptive statistics
- Practical: Calculate and display descriptive statistics in R
- Lecture: Theoretical frequency distributions
- Practical: Identifying frequency distributions in R
- Lecture: Parametric inferential statistics
- Practical: Parametric difference testing in R
- Lecture: non-parametric techniques in data science
- Practical: Nonâ??parametric hypothesis testing in R
- Lecture: statistical modelling
- Practical: correlation and regression in R
- Lecture: Transforming data and alternative distributions
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 30 | 120 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 13 | Lectures (7) Group discussion (6) |
| Scheduled Learning and Teaching | 5 | PGR lead help sessions (weeks 3-5,7-8) |
| Scheduled Learning and Teaching | 12 | IT Practicals |
| Guided Independent Study | 120 | Additional research, reading and preparation for module assessments and group discussions |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Short answer questions during lectures and practical sessions | Ongoing throughout the module | 1-17 | Oral |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 0 | 0 | 100 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Test 1 (opens week 2, closes week 3) | 10 | 2-hours | 1-4, 10-17 | Model answers |
| Test 2 (opens week 3,closes week 4) | 10 | 2-hours | 1-5, 9-17 | Model answers |
| Test 3 (opens week 4, closes week 5) | 20 | 4-hours | 1-17 | Model answers |
| Test 4 (opens week 5, closes week 6) | 20 | 4-hours | 1-17 | Model answers |
| Test 5 (opens week 7, closes week 8) | 20 | 4-hours | 1-17 | Model answers |
| Test 6 (opens week 8. closes week 9) | 20 | 4-hours | 1-17 | Model answers |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Weekly tests | Exam | 1-17 | August Re-assessment Period |
Re-assessment notes
RE-ASSESSMENT NOTES
Deferral – if you miss an assessment quiz for certificated reasons judged acceptable by the Mitigation Committee, you will be deferred in the assessment and asked to complete an exam (equivalent in size to the missed quiz[s]). The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment. Extensions during term may not be granted due to the release of model answers after the closure of quizes.
Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be required to complete a exam The mark given for a re-assessment taken as a result of referral will count for 100% of the final mark and will be capped at 40%.
Indicative learning resources - Basic reading
- Grolemund, Garrett, Hands-on programming with R, First edition. Sebastopol, Calif. : O'Reilly, 2014
- Matloff, Norman S.,The art of R programming : tour of statistical software design, San Francisco : No Starch Press, 2011.
- Rogerson, Peter,. A., Statistical methods for geography, London : SAGE, 2001.
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 4 |
| Available as distance learning? | No |
| Origin date | 03/02/2021 |
| Last revision date | 21/02/2023 |
