Module Details

Module Code: DATA C9002
Full Title: Statistics
Valid From:: Semester 1 - 2019/20 ( June 2019 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 10
Module Owner:: Siobhan Connolly
Departments: Unknown
Module Description: This module allows the learners to build on fundamental knowledge in statistics and probability and use these techniques as foundation to build further knowledge in the area. This module will encompass basic descriptive statistics, probability, probability distributions, statistical inference (parametric & non-parametric),and introduce generalised linear models. The module will also discuss Frequentist statistical approaches to point estimation and interval estimation and will introduce Bayesian statistics as another alternative approach.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Conduct exploratory data analysis by applying fundamental concepts and techniques.
MLO2 Assess fundamental probability laws and choose the appropriate probability distribution to model given problems.
MLO3 Conduct statistical inference to allow the learner to develop hypothesis to assess, examine appropriate models, interpret results and communicate findings
MLO4 Hypothesise and examine relationships between numerical variables through correlation, simple and multiple linear regression models
MLO5 Fit and interpret generalised linear models
MLO6 Distinguish between frequentist and Bayesian statistical approaches and be able to employ Bayes' Theorem to find posterior probabilities.
Pre-requisite learning
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 DkIT 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
 
Module Indicative Content
Descriptive Statistics
Contingency tables; Relative Risk; Odds Ratio; measures of central tendency & variation; Graphical representation of data
Probability Theory and Probability distributions
Basic Laws of Probability, Probabilistic Problem Solving, Bayes Theorem, Binomial, Poisson, Normal and other distributions
Statistical Parameter Estimation
Point estimation using method of moments and maximum likelihood estimators. Consistency, bias and mean squared error.
Hypothesis Testing
Both Parametric and Non-parametric methods to do one sample, two sample, paired sample and more than two samples hypothesis testing
Regression Analysis
Scatterplots, Correlation, Simple Linear Regression
Generalised Linear Models
Multiple Linear Regression, Logistic Regression, Loglinear Regression
Introduction to Bayesian Statistics
Prior probability, Posterior probability, Bayesian inference
Module Assessment
Assessment Breakdown%
Course Work25.00%
Project25.00%
Final Examination50.00%
Module Special Regulation
 

Assessments

Full Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing Every Second Week Learning Outcome 1,2,3,4,5
Duration in minutes 0
Assessment Description
Bi-Weekly short individual & group exercises or quizzes.
Project
Assessment Type Project % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5,6
Duration in minutes 0
Assessment Description
Data Project 1: Practical Implementation of some aspects of content of this module that involve analysing data. This will form part of a joint project with Research Process for Data Analytics and Programming for Data Analytics
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5,6
Duration in minutes 0
Assessment Description
End-of-Semester Final Examination

Part Time On Campus

Course Work
Assessment Type Continuous Assessment % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing Every Second Week Learning Outcome 1,2,3,4,5
Duration in minutes 0
Assessment Description
Bi-Weekly short individual & group exercises or quizzes.
Project
Assessment Type Project % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5,6
Duration in minutes 0
Assessment Description
Data Project 1: Practical Implementation of some aspects of content of this module that involve analysing data. This will form part of a joint project with Research Process for Data Analytics and Programming for Data Analytics
No Practical
Final Examination
Assessment Type Formal Exam % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing End-of-Semester Learning Outcome 1,2,3,4,5,6
Duration in minutes 0
Assessment Description
End-of-Semester Final Examination
Reassessment Requirement
A 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.
Reassessment Description
It is possible to be reassessed based on a coursework element and/or the repeat exam depending on the situation.

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

 

Module Workload

Workload: Full Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Three one hour lectures in the week Every Week 3.00 3
Tutorial Contact - Every Week 2.00 2
Directed Reading Non Contact - Every Week 3.00 3
Independent Study Non Contact - Every Week 8.00 8
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 5.00
Workload: Part Time On Campus
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Three one hour lectures in the week Every Week 3.00 3
Tutorial Contact - Every Week 2.00 2
Directed Reading Non Contact - Every Week 3.00 3
Independent Study Non Contact - Every Week 8.00 8
Total Weekly Learner Workload 16.00
Total Weekly Contact Hours 5.00
 
Module Resources
Supplementary Book Resources
  • Roger Berger, George Casella. (2001), Statistical Inference, 2nd.
  • Bradley Efron, Trevor Hastie. (2016), Computer Age Statistical Inference: Algorithms, Evidence, and Data Science.
  • Larry A. Wasserman. (2004), All of Statistics: A Concise Course in Statistical Inference.
This module does not have any article/paper resources
Other Resources