MY470 Half Unit
This information is for the 2017/18 session.
Prof Kenneth Benoit COL8.11
Dr Ben Lauderdale
This course is compulsory on the MSc in Data Science. This course is available with permission as an outside option to students on other programmes where regulations permit.
This course introduces students to the fundamentals of computer programming as students design, write, and debug computer programs using the programming language Python and R. The course will also cover the foundations of computer languages, algorithms, functions, variables, objectorientation, scoping, and assignment.
20 hours of lectures and 15 hours of classes in the MT.
Students will learn how to design algorithms to solve problems and how to translate these algorithms into working computer programs. Students acquire skills and experience as they learn Python and R, through programming assignments with an approach that integrates projectbased learning. This course is an introduction to the fundamental concepts of programming for students who lack a formal background in the field, but will include more advanced problem-solving skills in the later stages of the course. Topics include algorithm design and program development; data types; control structures; functions and parameter passing; recursion; data structures; searching and sorting; and an introduction to the principles of object oriented programming. The primary programming languages used in the course will be Python and R.
Students will be expected to produce 10 problem sets in the MT.
Type: Weekly, structured problem sets with a beginning component to be started in the staff-led lab sessions, to be completed by the student outside of class. Answers should be formatted and submitted for assessment.
Lutz, Mark Learning Python. 5th Edition. O'Reilly, 2013. Intermediate and advanced documentation at https://www.python.org/doc/.
Gries, Paul, Jennifer Campbell, and Jason M Montojo. Practical Programming: An Introduction to Computer Science Using Python 3. The Pragmatic Bookshelf, 2013.
Grolemund, Garrett and Hadley Wickham. R for Data Science. O'Reilly, 2016. http://r4ds.had.co.nz
Grolemund, Garrett. Hands-On Programming with R. O'Reilly, 2014.
Take home exam (50%) and in class assessment (50%) in the MT.
Student problem sets will be marked each week, and will provide 50% of the mark.
Total students 2016/17: Unavailable
Average class size 2016/17: Unavailable
Controlled access 2016/17: No
Value: Half Unit
Personal development skills
- Team working
- Problem solving
- Application of information skills
- Application of numeracy skills
- Specialist skills