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Course Name (English)
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INTRODUCTION TO STATISTICAL PROGRAMMING
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| Course Code |
STA334 |
| Course Description |
R is a powerful, versatile and free statistical programming language, which has become increasingly popular among industrial and academic data analysis. These introduction course assume no previous coding experiences in R or any other programming language.
This course will provide students with extensive skills of fast computing on how to organize computations to access, transform, explore, analyze data and produce results. The primary focus is on teaching the concepts and vocabulary of statistical computing.
The ultimate goal is that the students would be able to work in an office, lab or as a research assistant to do essential computations and that they would be able to legitimately put computing skills on their resume.
The method of teaching and learning included lecture, lab work and project which should be held in the statistics laboratory. The assessments consist of project, quiz and test. |
| Transferable Skills |
1) Demonstrate analytical skills
using technology.
2) Demonstrate practical and
contemporary knowledge of
relevant professional, ethical
and legal frameworks. |
| Teaching Methodologies |
Practical Classes, Presentation, Project-based Learning |
| CLO |
| CLO1 |
Display practical skills in constructing descriptive analysis using statistical programming. |
| CLO2 |
Perform an inferential analysis using statistical programming skill. |
| CLO3 |
Display the ability to report the findings related to various fields using statistical programming. |
| CLO4 |
Demonstrate entrepreneurial skills in completing tasks that use statistical programming. |
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| Pre-Requisite Courses |
No course recommendations |
| Reading List | | Recommended Text | - Kamarul Imran Musa, Wan Nor Arifin Wan 2021, Exploring Data Using R, 1 Ed., Penerbit USM Kota Bahru [ISBN: 9789674615321]
- Sandip Rakshit 2018, Statistics with R Programming, 2 Ed., McGraw-Hill Education India [ISBN: 9789353160951]
- John Braun, Duncan J. Murdoch 2016, A first course in statistical programming with R, 2 Ed., Cambridge University Press [ISBN: 9781316715802]
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| Reference Book Resources | - Winston Chang 2018, R Graphics Cookbook, 2 Ed., O'Reilly Media [ISBN: 9781491978603]
- James (JD) Long,Paul Teetor 2019, R Cookbook, 2 Ed., O'Reilly Media [ISBN: 9781492040682]
- Colin Gillespie,Robin Lovelace 2016, Efficient R Programming, O'Reilly Media [ISBN: 9781491950784]
- Garrett Grolemund,Hadley Wickham 2017, R for Data Science, 1 Ed., O’Reilly Media [ISBN: 9781491910399]
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| Article/Paper List | This Course does not have any article/paper resources |
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| Other References | This Course does not have any other resources |
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