NBS8002 : Techniques for Data Analysis
NBS8002 : Techniques for Data Analysis
- Offered for Year: 2024/25
- Module Leader(s): Dr Bo Che
- 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 1 Credit Value: | 10 |
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
None
Co-Requisite
Modules you need to take at the same time
| Code | Title |
|---|---|
| NBS8003 | Financial Information Analysis |
| NBS8005 | Corporate Strategy and Valuation |
Co Requisite Comment
None
Aims
• To introduce a broad range of underlying concepts and methodologies that are used in the data analytics process.
• To enable students to employ various analytical techniques that support effective decision-making in today’s increasingly competitive market. Students will also be equipped to appraise a wide range of applications and techniques for collecting, visualising, organising and analysing financial data that are designed to help financial analysts and other information users to improve their decision-making.
• To enable students to transform, analyse and interpret data, delivering solutions for theevaluation of investment opportunities in the financial sector.
Outline Of Syllabus
1. Descriptive statistics, visualising, organising and presenting data using appropriate tables, diagrams and numerical measures.
2. Basic probability theory and its applications, manipulations of proabilities applying various rules.
3. Referential statistics, sampling, estimation, hypothesis testing.
4. Regression, econometric modelling, autocorrelation, heteroscedasticity, multicollinearity.
5. Event study, literature review, methodology review, research design.
6. Challenges faced by modern financial data analysis in the context of emerging ‘big data’ society.
NB: The programme assumes no prior experience of statistics.
Learning Outcomes
Intended Knowledge Outcomes
At the end of this module students should be:
1. Develop, appraise and discriminate between statistical approaches to practical problems so that they can transform financial and accounting information into effective investment decision-making.
2. Evaluate the limitations and assumptions of mathematical and statistical modelling in analysing data, demonstrating a critical awareness of the potential risks of data manipulation in processing, analysing and reporting financial data and the challenges faced by financial analysts in the context of the emerging big data society.
Intended Skill Outcomes
At the end of this module students should be:
1. Demonstrate the ability to select, synthesise and evaluate financial data making use of statistical packages, drawing on the relevant literature in order to justify their findings.
2. Communicate their results effectively, translating them into clear business insights that support investment decision-making.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 75:00 | 75:00 | N/A |
| Scheduled Learning And Teaching Activities | Lecture | 12 | 2:00 | 24:00 | pip lectures |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | N/A |
| Scheduled Learning And Teaching Activities | Small group teaching | 3 | 2:00 | 6:00 | pip seminars |
| Structured Guided Learning | Structured non-synchronous discussion | 4 | 0:30 | 2:00 | 2 per semester |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 4 | 1:00 | 4:00 | 1 for whole cohort intro, 1 for pre-assessment clinic, 2 for drop-in (1 per semester) |
| Guided Independent Study | Independent study | 1 | 69:00 | 69:00 | N/A |
| Total | 200:00 |
Teaching Rationale And Relationship
The syllabus requires students to master a wide range of statistical skills. Students are also expected to understand the concepts underlying the application of statistical tools and evaluate their limitations. Helping students achieve this competence is best achieved by the combination of lecture materials, seminars, online discussions on Canvas, and guided independent studies. The fully blended approach was taken as it offers the flexibility we need to prepare for the uncertain future.
Reading Lists
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | A | 100 | 4000 words - using statistical analysis of real data, examine impact of firm-specific information on a company’s stock returns. |
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 |
|---|---|---|---|
| Prob solv exercises | 2 | M | N/A |
Assessment Rationale And Relationship
There is one project, involving statistical analysis of financial data, including the presentation of data, development of a regression model for an event study, time series analysis and forecasting.
The objective of the report is to examine, using statistical analysis of real data, the impact of firm-specific information on a company’s stock returns. To complete the assignment, students are expected to undertake three types of activities: 1) Selecting a public company for analysis, collecting relevant data, and reviewing literature; 2) Performing appropriate analysis of the impact of multiple information events on the stock returns of a public company; 3) Writing up a report describing your research design and interpreting your research results.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- NBS8002's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- NBS8002's past Exam Papers
General Notes
N/A
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Disclaimer
The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
Modules may be amended on an annual basis to take account of changing staff expertise, developments in the discipline, the requirements of external bodies and partners, and student feedback. Module information for the 2025/26 entry will be published here in early-April 2025. Queries about information in the Module Catalogue should in the first instance be addressed to your School Office.