Dr Milena Tsvetkova COL8.03

This course is compulsory on the MSc in Applied Social Data Science. This course is available on the MSc in Applied Social Data Science, MSc in Data Science, MSc in Human Geography and Urban Studies (Research) and MSc in Social Research Methods. This course is available with permission as an outside option to students on other programmes where regulations permit.

Compulsory unit for MSc in Applied Social Data Science and MSc Data Science who will be given priority. Available with permission as an outside option to students on other programmes where regulations permit and places are available.

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, object­-orientation, scoping, and assignment. The course will rely on practical examples from computational social science and social data science.

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 project-­based 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. 

Guttag, John V. Introduction to Computation and Programming Using Python: With Application to Understanding Data. MIT Press, 2016.

Gries, Paul, Jennifer Campbell, and Jason M Montojo. Practical Programming: An Introduction to Computer Science Using Python 3. The Pragmatic Bookshelf, 2013.

Miller, Bradley N. and David L. Ranum. Problem Solving with Algorithms and Data Structures Using Python. Available online at http://interactivepython.org/runestone/static/pythonds/index.html.

Python, Intermediate and advanced documentation at https://www.python.org/doc/.

Venables, William N., David M. Smith, and the R Core Team. An Introduction to R. Available online at https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf.

Zuur, Alain, Elena N. Ieno, and Erik Meesters. A Beginner's Guide to R. Springer Science & Business Media, 2009.

 

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.

Department: Methodology

Total students 2018/19: 56

Average class size 2018/19: 18

Controlled access 2018/19: No

Value: Half Unit