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DRPS : Course Catalogue : School of Mathematics : Mathematics

Undergraduate Course: Introduction to Data Science (MATH08077)

Course Outline
SchoolSchool of Mathematics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 8 (Year 1 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Informatics 2 - Foundations of Data Science (INFR08030)
Other requirements None
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) 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 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Feedback Not entered
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. employ all stages of a modern data science pipeline, including import, visualize, model, and communicate.
  2. critique data-based claims and evaluate data-based decisions.
  3. interpret results correctly, effectively, and in context without relying on statistical jargon.
  4. use the statistical computing language R to perform fully reproducible data analyses.
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
Additional Information
Graduate Attributes and Skills Not entered
Course organiserDr Amy Wilson
Tel: (0131 6)50 5087
Course secretaryMrs Frances Reid
Tel: (0131 6)50 4883
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