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The lecture notes are available below. (Note: These notes will be updated over the coming weeks)
Host: Marco Del Vecchio. ( https://marcodelvecchioblog.wordpress.com/ )
Office: IIPSI Building (Near Zeeman/MathStat building) third floor, desk number 41
Office Hours: Friday 10:00 – 11:00
Arrangements: The course will be held in A0.02 (PC room – Zeeman) during Term 1, and in R0.41 (PC room – Library) during Term 2. The course runs every Wednesday starting in Week 2 of Term 1 from 13:00pm to 15:00pm
Commitment: 12 lab lectures, 5 seminars, and 2 guest lectures.
- Lab lectures:
- Tidy data sets.
- A Sneak Peek into Machine Learning: Support Vector Machines
- The wonders of “foreach:” the joy and pain of parallel programming in R.
- Web Scraping in R.
- Reproducible Research: why it matters and how to do it.
Aims: This course offers a comprehensive and in-depth overview of the main aspects of the R language. This series of seminars and lab lectures are intended to provide the student with research-related tools, and examples of topics in computational statistics. All in all, this course will provide students with a strong background in R programming which will be beneficial when:
(a) taking academic modules that use R to illustrate practical applications
(b) embarking on a data analysis career.
Objectives: At the end of this course, students should have a broad understanding of the R language, and the ability to write concise code that can be used across many areas of data analysis. Specifically, students ought to be familiar with:
(a) R data types and the best way to perform operation on them
(b) functional programming and its role in data analysis and processing
(c) basic algorithm design.