| Module Title: | Case Studies in Data Science |
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| Language of Instruction: | English |
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| Teaching & Learning Strategies: |
The delivery of the material will be mainly in the laboratory setting. |
| Module Aim: |
The aim of the subject is to familiarise students with various applications of data science to create business value. The emphasis is to enable the student to apply the statistical learning and modelling techniques to develop an insight/solution to support business decisions.
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| Learning Outcomes |
| On successful completion of this module the learner should be able to: |
| LO1 |
Critically evaluate and apply a range of adequate statistical learning techniques to solve problem within a business context |
| LO2 |
Communicate and critically evaluate the outcomes of the application of data science methods to a chosen data set |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is recommended before enrolment in this module.
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| No recommendations listed |
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. |
| No incompatible modules listed |
Co-requisite Modules
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| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. |
| No requirements listed |
Module Content & Assessment
| Indicative Content |
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Data Science: Introduction
AI, Business Analytics, Data Analytics, Data Science, Machine Learning - concepts and definitions
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Statistics, Statistical Modelling and Machine Learning
Statistics vs. Statistical Modelling vs. Machine Learning
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Introduction to R & RStudio (IDE) environments
R vs Python, RStudio: scripts, workflow, packages: ggplot,plotly, tidyverse (dplyr,readr, purrr,forcats,stringr), plots tab: Graphs export, 3D graphs
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Seatle House Prices Case Study: Descriptive vs Predictive Analytics
Exploratory Data Analysis, Visualisation,and Predictive Modelling (Regression Analysis)
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Car engines and the polution level: Case Study
Introducing Basic Inferential Statistics Concept: Confidence Intervals, Logarithm Transformation, Significance Test, The Power of the test
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Twitter Data Case Study: Sentiment Analysis
The tidy text format, Sentiment Analysis with tidy data, data-type variables and their transformation with Lubridate,dplyr; Regular Expression, Comparing the odds ratios of words;
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Customer Segmentation Case Study
Exploratory Data Analysis, Data Visualisation, k-means clustering, Determining the Optimal number of Clusters: Elbow, Silhuette,and Gap methods
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Turists and their needs Case Study: Time Series Analysis
Identify the Time Series, Manipulating and Visualising Time Series; Calculate Time Series trends, Assessing Time Series Trends
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Wine market analysis - Case study
Dimiensionality Reduction: the rationale and application, The concept of Principal Component Analysis, Visualising PCA
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Student loan default Case Study
Logistic Regression, The concept of binary classification, application assumptions, the Logit model as part of the GLM family, Assessing Coefficients; caret package
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Marketing Data Case Study
Experimental Design, T-test, ANOVA, F-test, Hypothesis Testing, Post-Hoc testing
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| Assessment Breakdown | % |
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| Continuous Assessment | 100.00% |
| Continuous Assessment |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Case Studies |
Students will analyse a case study to provide solution to a stated problem by applying chosen statistical learning methods. |
1,2 |
100.00 |
Week 12 |
| No End of Module Formal Examination |
SETU Carlow Campus reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Frequency |
Average Weekly Learner Workload |
| Laboratory |
12 Weeks per Stage |
3.00 |
| Independent Learning |
15 Weeks per Stage |
5.93 |
| Total Hours |
125.00 |
Module Delivered In
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