Module Code: H9SDA Statistics for Data Analytics Statistics for Data Analytics LEVEL 9 7 Second Cycle
 Credits: 10
Module Coordinator: TONY DELANEY
Module Author: Margarete Silva
Departments: School of Computing
Specifications of the qualifications and experience required of staff This module requires a lecturer holding a Master’s degree or higher, in a discipline with a significant statistics component. e.g. Statistics, Mathematics, Economics.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Apply appropriate statistical inference techniques to the analysis of data across a variety of domains.
LO2 Interpret the outputs from statistical software packages and programming languages
LO3 Report and communicate statistical results in a comprehensive, ethical and professional manner
LO4 Apply appropriate forecasting techniques to time series.
LO5 Identify patterns in data and implement dimension reduction techniques.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements A cognate level 8 degree. Introductory statistics.

## Module Content & Assessment

Indicative Content
Review of Fundamental Statistical Concepts
Fundamentals of probability Sampling Estimation & confidence intervals Hypothesis testing & t-tests ANOVA techniques Hypothesis testing & ANOVA exercises
Review of Fundamental Statistical Concepts
Non-parametric tests Effect size in research & effect size metrics Statistical power and sample size Reporting test results Ethics in the use of data Correlation/partial correlation Basic linear regression models Exercises using non-parametric tools Examples of misuse of NHST Ethics in the use of data and statistical reporting
Topics in Multiple Linear Regression I
Model accuracy Qualitative variables Transformations Collinearity & collinearity diagnostics / VIF Diagnostics for leverage and influence Heteroscedasticity in regression models Correlation of error terms Non-linearity of data Use of statistical software & R to estimate regression models
Topics in Linear Regression II
Principles of regression model building Adjusted R2, AIC, BIC, Cp Best subset selection, forward selection, backward selection Modelling interactions Use of statistical software & R to estimate regression models
Logistic Regression
Principles behind the binary logistic regression model Odds & odds ratios The logit transformation Maximum likelihood estimation Estimating logistic regression coefficients Wald statistic – contribution of predictors Prediction using logistic regression Practical estimation of logistic regression models
Multinomial Logistic Regression & Linear Discriminant Analysis
Multinomial logistic regression Introduction to linear discriminant analysis Exercises in multinomial logistic regression and linear discriminant analysis
Dimension Reduction
Applications of PCA & exploratory factor analysis Suitability of data for PCA / factor analysis Kaiser’s criterion Interpretation of principal components Factor rotation Clustering methods Practical application of PCA in R / statistical software
Multivariate Analysis of Variance (MANOVA)
ANOVA vs MANOVA Applications of MANOVA SSCP matrices MANOVA test statistics Interpretation of MANOVA software output Practical application of MANOVA
Bayesian Statistics
Frequentists vs. Bayesians Bayes rule & applications Introduction to Bayesian networks Bayesian Statistics problems
Time Series I
Decomposition of Time Series Seasonality Stationarity Data Transformations Mean & Linear Trend models Random Walk Models Averaging & smoothing models Auto regressive models Applications of time series forecasting
Time Series II
Non-seasonal ARIMA models Orders of AR and MA terms Seasonal ARIMA models Model estimation ARCH Applications of time series forecasting
Revision
Revision
Assessment Breakdown%
Coursework35.00%
End of Module Assessment65.00%

## Full Time

Coursework
 Assessment Type: Continuous Assessment (0200) % of total: 35 Assessment Date: n/a Outcome addressed: 1,2,3,4,5 Non-Marked: No Assessment Description:Learners, individually or in a group, will be directed towards appropriate datasets and asked to produce a statistical report that incorporates the estimation of statistical models and reports findings in an appropriate manner. Estimation using some/all of multiple linear regression, logistic regression, time series analysis and dimension reduction techniques is likely to be required
 Assessment Type: Formative Assessment % of total: Non-Marked Assessment Date: n/a Outcome addressed: 1,2,3,4,5 Non-Marked: Yes Assessment Description:Formative assessment will be undertaken utilising exercises and short answer questions during certain tutorials. In class discussions will be undertaken on contemporary topics. Feedback will be provided individually or as a group in oral format.
End of Module Assessment
 Assessment Type: Terminal Exam % of total: 65 Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4,5 Non-Marked: No Assessment Description:The examination will be of two hours duration and may include a mix of: theoretical, applied and interpretation questions.
 No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

NCIRL reserves the right to alter the nature and timings of assessment

Module Target Workload Hours 0 Hours
Lecture No Description 24 Every Week 24.00
Tutorial No Description 24 Every Week 24.00
Independent Learning No Description 202 Every Week 202.00
Total Weekly Contact Hours 48.00
Lecture No Description 2 Every Week 2.00
Tutorial No Description 2 Every Week 2.00
Independent Learning No Description 17 Every Week 17.00
Total Weekly Contact Hours 4.00

## Module Resources

Recommended Book Resources
• Carlos Cortinhas,Ken Black. (2012), Statistics for Business and Economics, 1st European Edition. John Wiley & Sons, p.862, [ISBN: 1119993660].
• Jeremy J Foster,Emma Barkus,Christian Yavorsky. (2006), Understanding and Using Advanced Statistics, SAGE, p.178, [ISBN: 141290014X].
• Wolfgang Karl Härdle,Léopold Simar. (2012), Applied Multivariate Statistical Analysis, Springer Science & Business Media, p.516, [ISBN: 978-3-642-17229-8].
• Rob J. Hyndman,George Athanasopoulos. (2013), Forecasting, Otexts, p.292, [ISBN: 978-0987507105].
• Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 978-1461471370].
• Ben Lambert. (2018), A Student’s Guide to Bayesian Statistics, SAGE Publications Limited, p.520, [ISBN: 9781473916364].
• Field A.. (2018), Discovering statistics using SPSS statistics, 5th edition. SAGE, London.
Supplementary Book Resources
• Chris Brooks. (2019), Introductory Econometrics for Finance, Cambridge University Press, p.750, [ISBN: 978-1108436823].
• Christian Heumann,Michael Schomaker,Shalabh. (2017), Introduction to Statistics and Data Analysis, Springer, p.456, [ISBN: 978-3-319-46162-5].
• Julie Pallant. SPSS Survival Manual, [ISBN: 9780335261543].
• Ruey S. Tsay. (2012), An Introduction to Analysis of Financial Data with R, John Wiley & Sons, p.416, [ISBN: 9780470890813].
This module does not have any article/paper resources
This module does not have any other resources