Module Details
Module Code: |
SCIE |
Module Title:
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Case Studies in Data Science
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Title:
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Case Studies in Data Science
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Module Level:: |
8 |
Module Coordinator: |
Nigel Whyte
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Module Author:: |
Agnes Maciocha
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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.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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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.
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Additional Requisite Information
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No Co Requisites listed
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Indicative Content |
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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Module Content & Assessment
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Assessment Breakdown | % |
Continuous Assessment | 100.00% |
AssessmentsFull Time
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.
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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.
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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 |
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Contact |
No Description |
12 Weeks per Stage |
3.00 |
36 |
Independent Learning |
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Non Contact |
No Description |
15 Weeks per Stage |
5.93 |
89 |
Total Weekly Contact Hours |
3.00 |
Module Resources
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Recommended Book Resources |
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J. D. Long,Paul Teetor. (2019), R Cookbook, O'Reilly Media, p.600, [ISBN: 1492040681].
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Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 1461471370].
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Christopher N. Chapman,Elea McDonnell Feit. (2015), R for Marketing Research and Analytics, Springer, p.454, [ISBN: 3319144359].
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Deborah Nolan,Duncan Temple Lang. (2015), Data Science in R, Chapman and Hall/CRC, p.539, [ISBN: 1482234815].
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Garrett Grolemund,Hadley Wickham. (2016), R for Data Science, [ISBN: 1491910399].
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Rami Krispin. (2019), Hands-On Time Series Analysis with R, [ISBN: 1788629159].
| This module does not have any article/paper resources |
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Other Resources |
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datacamp. datacamp,
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Educational material R Studio,
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Towards Data Science,
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Educational material R Studio,
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UDEMY,
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