Module Details

Module Code: SCIE
Module Title: Case Studies in Data Science
Title: Case Studies in Data Science
Module Level:: 8
Credits:: 5
Module Coordinator: Nigel Whyte
Module Author:: Agnes Maciocha
Domains:  
Module Description: 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.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
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
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Data Science: Introduction
AI, Business Analytics, Data Analytics, Data Science, Machine Learning - concepts and definitions
Statistics, Statistical Modelling and Machine Learning
Statistics vs. Statistical Modelling vs. Machine Learning
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
Seatle House Prices Case Study: Descriptive vs Predictive Analytics
Exploratory Data Analysis, Visualisation,and Predictive Modelling (Regression Analysis)
Car engines and the polution level: Case Study
Introducing Basic Inferential Statistics Concept: Confidence Intervals, Logarithm Transformation, Significance Test, The Power of the test
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;
Customer Segmentation Case Study
Exploratory Data Analysis, Data Visualisation, k-means clustering, Determining the Optimal number of Clusters: Elbow, Silhuette,and Gap methods
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
Wine market analysis - Case study
Dimiensionality Reduction: the rationale and application, The concept of Principal Component Analysis, Visualising PCA
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
Marketing Data Case Study
Experimental Design, T-test, ANOVA, F-test, Hypothesis Testing, Post-Hoc testing
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment100.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Case Studies % of Total Mark 100
Timing Week 12 Learning Outcomes 1,2
Non-marked No
Assessment Description
Students will analyse a case study to provide solution to a stated problem by applying chosen statistical learning methods.
No Project
No Practical
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.
Reassessment Description
Decided by module academic in conjunction with programme board. Repeat of coursework and/or written examination or other repeat mechanism as appropriate dependent on students performance and module engagement.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Laboratory Contact No Description 12 Weeks per Stage 3.00 36
Independent Learning Non Contact No Description 15 Weeks per Stage 5.93 89
Total Weekly Contact Hours 3.00
 
Module Resources
Recommended Book Resources
  • J. D. Long,Paul Teetor. (2019), R Cookbook, O'Reilly Media, p.600, [ISBN: 1492040681].
  • Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 1461471370].
  • Christopher N. Chapman,Elea McDonnell Feit. (2015), R for Marketing Research and Analytics, Springer, p.454, [ISBN: 3319144359].
  • Deborah Nolan,Duncan Temple Lang. (2015), Data Science in R, Chapman and Hall/CRC, p.539, [ISBN: 1482234815].
  • Garrett Grolemund,Hadley Wickham. (2016), R for Data Science, [ISBN: 1491910399].
  • Rami Krispin. (2019), Hands-On Time Series Analysis with R, [ISBN: 1788629159].
This module does not have any article/paper resources
Other Resources
Discussion Note: