Undergraduate Course: Introduction to Data Science (MATH08077)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 8 (Year 1 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | This is an introductory level course on data science and statistical thinking. Students will learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, and do so in a reproducible and shareable manner. In doing so, they will gain experience in data collection, wrangling, and visualization, exploratory data analysis, predictive modelling, and effective communication of results while working on problems and case studies inspired by and based on real-world questions. The course will focus on the R statistical computing language. No statistical or computing background is necessary. |
| Course description |
This course is comprised of three learning units:
Unit 1 - Collecting and exploring data: This unit focuses on data visualization, wrangling, and collection.
Unit 2 - Modelling and prediction: This unit introduces simple and multiple linear regression models, with a focus on interpretations, visualizing interactions, model selection, prediction, and model validation.
Unit 3 - Making rigorous conclusions: In this part we introduce statistical inference for making data based conclusions from a simulation based perspective, focusing on bootstrapping and randomization.
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Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: 0 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 22,
Seminar/Tutorial Hours 22,
Supervised Practical/Workshop/Studio Hours 11,
Summative Assessment Hours 3,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
138 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- employ all stages of a modern data science pipeline, including import, visualize, model, and communicate.
- critique data-based claims and evaluate data-based decisions.
- interpret results correctly, effectively, and in context without relying on statistical jargon.
- use the statistical computing language R to perform fully reproducible data analyses.
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Reading List
There is no compulsory course text. The following books are useful complements to parts of the course for those who prefer learning from textbooks. Both books are freely available online.
- R for Data Science - Grolemund, Wickham O'Reilly, 1st edition, 2016
- OpenIntro: Introduction to Modern Statistics - ÇetinkayaRundel, Hardin. CreateSpace, Preliminary Edition, 2020
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | IDS |
Contacts
| Course organiser | Dr Amy Wilson
Tel: (0131 6)50 5087
Email: Amy.L.Wilson@ed.ac.uk |
Course secretary | Mrs Frances Reid
Tel: (0131 6)50 4883
Email: f.c.reid@ed.ac.uk |
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