Module Details

Module Code: STAT9010
Title: Data Analytics and Statistics
Long Title: Data Analytics and Statistics
NFQ Level: Expert
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 1 programme(s)
Module Description: This module allows learners to explore the principal quantitative tools used to analyse data. It will provide exposure to downloading and organising real-world financial data using industry-standard databases and will also allow learners to conduct key data analytic operations such as regressions and time series analysis and the implementation of the methodologies using the latest of data visualisation tools. Participants will also have an opportunity to apply the statistical techniques to challenges relevant to the investment industry such as testing investment strategies including technical analysis, event studies, and measuring fund performance.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Assess the different types of data and the principles of data collection.
LO2 Analyse and interpret a set of univariate data using a range of statistical measures.
LO3 Perform and interpret the results of a range of hypothesis tests.
LO4 Conduct correlation and regression analysis on real world economic data.
LO5 Implement and assess the use of time series models as an analysis and forecasting tools for economic datasets.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Collection of data
Collection of data (types of data, use of industry-standard databases, role of big data).
Univariate Statistical Analysis
Univariate Statistical Analysis (measures of average and spread, data visualisation).
Descriptive and Inferential Statistics
Descriptive and Inferential Statistics (confidence intervals, normal distribution, ANOVA, hypothesis testing).
Correlation and Regression
Correlation and Regression (model assumptions, simple regression, multiple regression, stepwise regression, dummy variables, binary logistic regression, prediction).
Introduction to time series analysis
Introduction to time series analysis (basic time series analysis and forecasting).
Real-world applications
Real-world applications (technical analysis, event studies, investment strategies, fund performance).
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Other % of Total Mark 30
Timing Week 13 Learning Outcomes 1,2,3,4,5
Assessment Description
In-Class Assessment
Assessment Type Other % of Total Mark 70
Timing Every Second Week Learning Outcomes 2,3,4
Assessment Description
Assignments
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture Every Week 2.00 2
Independent Learning Non Contact Independent study Every Week 5.00 5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Lecture Every Week 2.00 2
Independent Learning Non Contact Independent study Every Week 5.00 5
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • Gujarati, D.N.. (2012), Econometrics by Example, Palgrave MacMillian, UK.
Supplementary Book Resources
  • Anderson, D.R., D.J. Sweeney, T.A. Williams, N.J. Freeman and E. Shoesmith.. (2017), Statistics for Business and Economics, 4th. Cengage Learning, UK.
  • Brooks, C.. (2019), Introductory Econometrics for Finance, 4th. Cambridge, UK.
  • Gujarati, D.N. and D.C. Porter.. (2009), Basic Econometrics, 5th. McGraw-Hill, USA.
  • Studenmund, A.H.. (2017), Using Econometrics: A Practical Guide, 7th. Pearson, England.
This module does not have any article/paper resources
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_BIFAD_9 Master of Business in Investment Fund Administration 1 Mandatory