| Unit name | Data Science and Applied Statistics |
|---|---|
| Unit code | SEMT20002 |
| Credit points | 20 |
| Level of study | I/5 |
| Teaching block(s) |
Teaching Block 1 (weeks 1 - 12) |
| Unit director | Dr. Golbabaee |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
EMAT10100 Engineering Mathematics 1, EMAT10704 Discrete Mathematics, and either: EMAT10006 Further Computer programming or SEMT10002 Computer Programming and Algorithms |
| Units you must take alongside this one (co-requisite units) |
EMAT20200 Engineering Mathematics 2 |
| Units you may not take alongside this one |
None |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
All of science and engineering is underpinned by the collection and analysis of data. In today’s world, data analysis has become even more important with the rise of “big data” and the development of new tools to deal with large data sets. This unit will introduce students to working with data and will explore many of the key concepts that underpin data science and statistics, including importing and exporting data, data visualisation, data analysis, and key statistical methodologies. Students will be exposed to different types of data and patterns and will gain experience in data analysis using suitable software tools and packages.
How does this unit fit into your programme of study
This unit builds on the mathematical and programming skills developed in first year and will equip students to analyse and interpret data in real-world contexts. The material covered in this unit will be critical in many projects encountered in the Mathematical and Data Modelling series of units. Additionally, this unit provides essential background knowledge of key concepts that will be developed further in later core and optional units on artificial intelligence and machine learning.
An overview of content
This unit has two parts: “Data Science” and “Applied Statistics”.
The first part on “Data Science” will cover data visualisation and pre-processing, and supervised and unsupervised machine learning, Students will be introduced to key concepts from machine learning such as classification and regression, clustering, and linear manifolds. They will also gain practical experience in data analysis using suitable software.
The second part on “Applied Statistics” will cover random variables and probability distributions, confidence intervals and hypothesis testing, generalised linear models and model selection. This part of the unit is designed to provide students with practical experience in using statistical methodologies to solve problems and will explore both real data and simulated realistic data. Each concept will be taught in a bottom-up way that allow students to understand the vital role of the field of probability and statistics in academic and industrial engineering research
How will students, personally, be different as a result of the unit
Students will better understand the challenges and opportunities presented by working with data and will gain a collection of mathematical, computational, and statistical tools for working with data. Most importantly, students will gain experience and practice with determining what tools are most appropriate in what situations, and will develop insights into the strengths and limitations of different approaches.
Learning Outcomes
At the end of this unit, a successful student will be able to
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets/self-directed exercises.
Tasks which help you learn and prepare you for summative tasks (formative):
All practical labs have some formative assessment in the form of embedded questions based on the lab exercises with model answers given a week later.
Tasks which count towards your unit mark (summative):
Individual coursework assessment covering all ILOs (100%)
When assessment does not go to plan:
Reassessment will be achieved through an individual coursework covering all ILOs that is equivalent to the normal summative assessment
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. SEMT20002).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
