NBS8614 : Applied Data Science
- Offered for Year: 2024/25
- Available for Study Abroad and Exchange students, subject to proof of pre-requisite knowledge.
- Module Leader(s): Dr Harry Pickard
- Owning School: Newcastle University Business School
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
The module provides students with an understanding of concepts and methods used in data science. Such techniques are rapidly becoming a part of the economist’s toolbox and used in the wider social sciences.
The module covers 3 key areas: the first is a concise, but nonetheless thorough, introduction to R – the statistical software package that will be used throughout the course. The second part covers the key concepts of data science, which serves to highlight how these new methods are distinct from the commonly used econometric techniques. We cover classification and resampling methods. The third part of the module covers the popular statistical learning algorithms and how these can increase our understanding of data. We will, broadly, cover tree based and ensemble methods. More advanced (further) methods covered will deep learning and unsupervised learning techniques, and then text analysis and web scraping.
For each topic we will also discuss how to implement these algorithms in R.
Outline Of Syllabus
1. Introduction to R and statistical learning
2. Basic concepts of data science
a. Classification
b. Resampling methods
3. Methods in data science
a. Linear model selection & regularization
b. Moving beyond linearity
c. Tree based methods
d. Causal forests
e. Support vector machines
4. Further methods in data science
a. Deep Learning
b. Unsupervised learning
5. Spatial analysis
6. Text analysis
a. Web scraping: Theory and applications (3h)
b. Topic Modeling: Theory and applications (3h)
2 hours per lecture, except Section 6 which is a total of 6 hours.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 64 | 1:00 | 64:00 | N/A |
| Scheduled Learning And Teaching Activities | Lecture | 14 | 2:00 | 28:00 | Present-in-person |
| Guided Independent Study | Directed research and reading | 50 | 1:00 | 50:00 | N/A |
| Scheduled Learning And Teaching Activities | Practical | 8 | 1:00 | 8:00 | Computer classes (PiP) |
| Guided Independent Study | Independent study | 50 | 1:00 | 50:00 | N/A |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures provide an exhaustive and in-depth introduction to the core course material, and introduction to required techniques. Computer classes will teach the application of these methods with real world data. Private study facilitates review and understanding of lecture material.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | 4000 words |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Written Examination | 2 | M | Formative assessment will take place halfway through the course in which students will receive a test of exam-style questions |
Assessment Rationale And Relationship
Students will have to write a short report where they demonstrate the ability to use machine learning algorithms to obtain insights from big data.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- NBS8614's Timetable