Prerequisites for Module
None.
Corequisite Modules
None.
Precluded Modules
None.
Aims of Module
To provide students with necessary skills for developing complete data science products using a state-of-the-art high level programming language.
Learning Outcomes for Module
On completion of this module, students are expected to be able to:
| 1. |
Discuss the main concepts and tools for a data science project.
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| 2. |
Load, explore, model and visualise data using off-the-shelf tools and packages.
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| 3. |
Report data science results to a wider audience by tailoring them at different levels of detail.
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| 4. |
Design, implement and evaluate a data science product that addresses a given data problem.
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Indicative Module Content
1. Data science programming concepts
2. Data preparation methods
3. Data exploration, summarisation, transformation and visualisation techniques
4. Descriptive analytics (Cluster and link analysis)
5. Predictive analytics (Classification and regression analysis)
6. Advanced analytics (Text mining and social network analysis)
Indicative Student Workload
Contact Hours
| Full Time | Part Time | Laboratories
| 24 | 24 | Lectures
| 12 | 12 |
Directed Study
| | | Coursework Preparation
| 25 | 25 | Directed Reading
| 47 | 47 |
Private Study
| | | Private Study
| 42 | 42 |
| Mode of Delivery
This is a lecture based module, supplemented with practical sessions, where a data science programming language will be used to teach students how to develop a complete data science project from data preparation to advanced analytics. Online materials and online sessions will be used to support DL students.
Assessment Plan
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Learning Outcomes Assessed
| | Component 1 | 1,2,3,4
|
Component 1 - Data science project, 15% in class presentation, 85% Report including results and R scripts.
Indicative Bibliography
| 1. | JAMES, G., WITTEN, D., HASTIE, T.,& TIBSHIRANI, R. (2021) An introduction to statistical learning with applocations in R. New York: springer.
| | 2. | An Introduction to R, Version 4.1.2 (2021), https://cran.r-project.org/doc/manuals/R-intro.pdf
| | 3. | ZHAO, Y. (2012-2015). R and Data Mining: Examples and Case Studies, Elsevier. http://www2.rdatamining.com/uploads/5/7/1/3/57136767/rdatamining-book.pdf
| | 4. | KORDON, K. (2020) Applying Data Science: How to Create Value with Artificial Intelligence. Springer.
| | 5. | MAILUND, T. (2017) Beginning Data Science in R
Data Analysis, Visualization, and Modelling for the Data Scientist. APress.
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