Module Details
Module Code: |
DATA C9002 |
Full Title:
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Statistics
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Valid From:: |
Semester 1 - 2019/20 ( June 2019 ) |
Language of Instruction: | English |
Module Owner:: |
Siobhan Connolly
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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.
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Module Learning Outcome |
On successful completion of this module the learner will be able to: |
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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).
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No recommendations listed |
Module Indicative Content |
Descriptive Statistics
Contingency tables; Relative Risk; Odds Ratio; measures of central tendency & variation; Graphical representation of data
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Probability Theory and Probability distributions
Basic Laws of Probability, Probabilistic Problem Solving, Bayes Theorem, Binomial, Poisson, Normal and other distributions
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Statistical Parameter Estimation
Point estimation using method of moments and maximum likelihood estimators. Consistency, bias and mean squared error.
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Hypothesis Testing
Both Parametric and Non-parametric methods to do one sample, two sample, paired sample and more than two samples hypothesis testing
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Regression Analysis
Scatterplots, Correlation, Simple Linear Regression
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Generalised Linear Models
Multiple Linear Regression, Logistic Regression, Loglinear Regression
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Introduction to Bayesian Statistics
Prior probability, Posterior probability, Bayesian inference
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Module Assessment
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Assessment Breakdown | % |
Course Work | 25.00% |
Project | 25.00% |
Final Examination | 50.00% |
Module Special Regulation |
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AssessmentsFull Time On Campus
Part Time On Campus
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.
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Reassessment Description It is possible to be reassessed based on a coursework element and/or the repeat exam depending on the situation.
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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
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Supplementary Book Resources |
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Roger Berger, George Casella. (2001), Statistical Inference, 2nd.
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Bradley Efron, Trevor Hastie. (2016), Computer Age Statistical Inference: Algorithms, Evidence, and Data Science.
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Larry A. Wasserman. (2004), All of Statistics: A Concise Course in Statistical Inference.
| This module does not have any article/paper resources |
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Other Resources |
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Website, Online Statistics Education: An
Interactive Multimedia Course of Study,
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Website, Khan Academy,
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Website, Data Camp,
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