This course is available on the BSc in Accounting and Finance. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students. This course is available as an outside option to the students who are interested in data analytics and who have statistical background at least equivalent to ST107 or ST108. No prior knowledge in programming is required. However students who have no previous experience in R are required to take on an online pre-sessional R course from the Digital Skill Lab (https://moodle.lse.ac.uk/course/view.php?id=7022). This course is capped at 60 for the 2019/20 session.? This course cannot be taken with?ST310 Machine Learning.
This course is available on the BSc in Accounting and Finance. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students. This course is available as an outside option to the students who are interested in data analytics and who have statistical background at least equivalent to ST107 or ST108. No prior knowledge in programming is required. However students who have no previous experience in R are required to take on an online pre-sessional R course from the Digital Skill Lab (https://moodle.lse.ac.uk/course/view.php?id=7022). This course is capped at 60 for the 2019/20 session.? This course cannot be taken with?ST310 Machine Learning.
This course will be delivered through a combination of classes, lectures and Q&A sessions totalling a minimum of 30 hours in Michaelmas Term. This year, some of this teaching may be delivered through a combination of virtual classes and flipped-lectures delivered as short online videos. Students are encouraged to install R in their own laptops, and to use their own laptops in the workshops.
Students will be expected to produce 6 exercises in the MT. Studeents are expected to complete siix sets of exercises involving substantial data analysis using R.
Wickham, H, and Grolemund, G. (2017). R for Data Science. O'Reilly. Available online at?http://r4ds.had.co.nz James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013).?An Introduction to Statistical Learning with Applications in R. Springer. Available online at?http://www-bcf.usc.edu/~gareth/ISL Provost, F. and Fawcett, T. (2013).?Data Science for Business. O'Reilly.? Zuur, A.,?Ieno, E. and Meesters, E. (2009). A Beginner?s Guide to R. Springer. Available online from LSE?Library. Hastie, T.,?Tibshirani, R and Friedman, R. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer. Available online at?https://web.stanford.edu/~hastie/Papers/ESLII.pdf Silge, J. and Robinson, D. (2017). Text Mining with R: a tidy approach. O?Reilly. Available online at https://www.tidytextmining.com Wickham, H. (2016). ggplot2: Elegant Graphics for Data Analysis. Springer. Available online at http://moderngraphics11.pbworks.com/f/ggplot2-Book09hWickham.pdf
Coursework (30%) in the MT. Project (70%) in the LT. The project will be a group project with maximum 3 members per group. The detailed instruction will be handed out in Week 5 of Michaelmas term, and students need to submit a written report by Week 5 of Lent term. Students are required to hand in the solutions for 3 sets of exercises which account for the total 30% of the final grade.