INFO420 Statistical Techniques for Data Science
| Second Semester |
This paper is compulsory for the Master of Business Data Science.
An introduction to statistical modelling and multivariate analysis that includes generalised linear models and procedures for analysing patterns in multiple quantitative measurements. The paper combines background theory with practice in applying the methods to real datasets.
Topics include
- linear regression
- factorial analysis of variance
- models for binomial and count data
- principal component analysis
- clustering
- replication and pseudo-replication
- randomisation and blocking
- penalised methods for regression, e.g. lasso, ridge regression
- splines and generalised additive models
- linear and quadratic discriminant analysis
Coordinator
Lectures
Wednesday 11:00 AM – 12:50 PM in Archway 2
Three additional 2-hour lectures: Wednesday 2:00 PM – 3:50PM in Rm 240, Science 3. September *6, 13, 20* only
Resources
The textbook "An Introduction to Statistical Learning - with Applications in R" by James et al. is freely available here. There are also links to slides and lectures there which you might find useful.
Assessment
The internal assessment counts for 33% of the final mark with the final exam accounting for the remaining 67% of the final mark. Plussage applies so that your internal assessment counts only if it helps your mark.
There are 11 written assignments throughout the semester worth 67% of the internal assessment mark and one mid-term exam worth 33% of the internal assessment mark.
The final exam is a 3-hour written exam.
Midsemester Test
Wednesday 6th September, 7.30pm in Archway 1. The test is one (1) hour.
Final mark
Your final mark F in the paper will be calculated according to this formula:
F = max(E, (6E + T + 2A)/9)
where:
- E is the Exam mark
- A is the Assignments mark
- T is the Tests mark
and all quantities are expressed as percentages.
Students must abide by the University’s Academic Integrity Policy
Academic endeavours at the University of Otago are built upon an essential commitment to academic integrity.
The two most common forms of academic misconduct are plagiarism and unauthorised collaboration.
Academic misconduct: Plagiarism
Plagiarism is defined as:
- Copying or paraphrasing another person’s work and presenting it as your own.
- Being party to someone else’s plagiarism by letting them copy your work or helping them to copy the work of someone else without acknowledgement.
- Using your own work in another situation, such as for the assessment of a different paper or program, without indicating the source.
- Plagiarism can be unintentional or intentional. Even if it is unintentional, it is still considered to be plagiarism.
All students have a responsibility to be aware of acceptable academic practice in relation to the use of material prepared by others and are expected to take all steps reasonably necessary to ensure no breach of acceptable academic practice occurs. You should also be aware that plagiarism is easy to detect and the University has policies in place to deal with it.
Academic misconduct: Unauthorised Collaboration
Unauthorised Collaboration occurs when you work with, or share work with, others on an assessment which is designed as a task for individuals and in which individual answers are required. This form does not include assessment tasks where students are required or permitted to present their results as collaborative work. Nor does it preclude collaborative effort in research or study for assignments, tests or examinations; but unless it is explicitly stated otherwise, each student’s answers should be in their own words. If you are not sure if collaboration is allowed, check with your lecturer.
