EEEN40660

Learning Outcomes:

On successful completion of this module the student will:
- know how to engage in statistical thinking and rigorous scientific inquiry
- understand the basic mathematics underlying modern statistical analysis
- have proficiency in conducting data analyses using Python, the dominant and fastest-growing programming language used in the engineering and data science industries
- be able to carry out a range of statistical analyses including ANOVA, single- and multi-variable regression, logistic regression, repeated-measures and nonparametric tests (including permutation tests), linear mixed-effect models, and basic Bayesian statistics.
- understand basic principles of model selection and the relationship between statistical and mechanistic modeling
- understand how to design valid, effective, and statistically powered experiments for problems ranging from basic scientific investigations of biological processes to process validation in manufacturing

Indicative Module Content:

Motivating, designing and executing scientific studies; Principles of statistical inference; Tests for differences between 2 groups; Confidence intervals; Tests for differences among >2 groups; Multiple comparisons; Repeated-measures designs; Power analysis; Correlation; Simple regression; Logistic regression; Nonlinear regression; multiple regression; Tests for rates and proportions; Additional topics: Bayesian statistics; mechanistic models

Student Effort Hours:

Approaches to Teaching and Learning:

Lectures: 2-4 videos per week with content aligned with lab work from week to week, to be viewed asynchronously, available in Brightspace.
One in-person tutorial session per week for discussions, group work, and journal club work pertaining to the lecture content.
Active/task-based learning: Every week there will be Python programming/analysis demos and tasks to complete, which are to be worked on partially autonomously and partially within a weekly 2-hour lab. It will be possible to join by Zoom if isolating due to Covid.
A 15-minute quiz (one hour in week 12) will be given in brightspace in each lab session, with the password called out on the zoom call. Therefore, attendance to each lab is necessary.
2 Data analysis assignments will further engage and formally assess task-based learning.

Requirements, Exclusions and Recommendations

Learning Requirements:

Students should have previously taken basic probability theory (e.g. from STAT20060)

Module Requisites and Incompatibles

Not applicable to this module.
 

Assessment Strategy

 

Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Brief feedback will be provided to each student on Brightspace for their data analysis assignment submissions. Summary feedback will be provided via video content or broadcast messages on common pitfalls and tips related to the weekly task submissions. Submission of reports and feedback will all be conducted online through brightspace.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.