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STAT8010 - Intro to R for Data Science

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Title:Intro to R for Data Science
Long Title:Intro to R for Data Science
Module Code:STAT8010
 
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2018/19 ( September 2018 )
Module Delivered in 2 programme(s)
Next Review Date: September 2023
Module Coordinator: David Goulding
Module Author: David Goulding
Module Description: In this module, students will learn how to clean, manipulate and visualise data using the statistical software package R. Students will create and analyse statistical models and simulations with R.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Evaluate the functionality of the R statistical programming language.
LO2 Perform data cleaning, manipulation and wrangling techniques to specified data problems.
LO3 Implement appropriate data visualisation techniques to examine real world datasets.
LO4 Investigate statistical modelling and simulation techniques.
LO5 Develop best practice in terms of reproducible documentation and version control.
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 CIT 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
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
Co-requisites
No Co Requisites listed
 

Module Content & Assessment

Indicative Content
Base R
Learn how to navigate RStudio or similar IDE; how to load/save a file, load a package, access help etc. Examine the base R objects - vectors, matrices, arrays, lists, factors and tables; their respective characteristics, naming conventions and structures. Understand subsetting, filtering and creation of these objects. Examine the implementation of control structures (loops and functions) in R. Investigate how R can be used for mathematical and statistical calculations.
Data Cleaning and Manipulation in R
Understand the tidyverse suite of packages and how they can be used for data wrangling and data manipulation. Learn how to use regular expressions and pattern recognition in R for data cleaning purposes.
Visualisation
Learn how basic plots are generated in R - histograms, X-Y plots. Understand the ggplot2 package for advanced plotting. Examine RShiny for the creation of web-based dashboards and interactive plots.
Statistical Testing
Understand how R can be used for sampling and simulation techniques such as bootstrapping, Monte Carlo method, simulating sample distributions, checking hypothesis testing. Investigate how R can be used in statistical modelling techniques (e.g. naive Bayes classifers).
Reproducible Documentation and Version Control
Learn how R and R Markdown can be used to produce documents for reproducible research and results. Implement version control through the integration of Git in R.
Assessment Breakdown%
Course Work100.00%
Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Multiple Choice Questions Assess proficiency in base R and tidyverse commands. 1 20.0 Week 4
Project Perform data wrangling, data manipulation and apply an appropriate visualisation technique to examine a real world data set. 2,3 30.0 Week 8
Project Design and implement an appropriate data modelling/simulation and visualisation solution to a specified data set. 1,2,3,4 50.0 Sem End
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.

The institute reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 1.0 Every Week 1.00
Lab Programming laboratory 3.0 Every Week 3.00
Independent & Directed Learning (Non-contact) Study, practice and completion of worksheets 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Module Content delivery 1.0 Every Week 1.00
Lab Programming laboratory 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Study, practice and completion of worksheets 4.0 Every Week 4.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • Garrett Grolemund and Hadley Wickham 2017, R for Data Science, O'Reilly Media http://r4ds.had.co.nz/ [ISBN: 9781491910399]
  • Kabacoff, Robert 2015, R in Action, 2nd Ed., Manning New York [ISBN: 1617291382]
  • Norman Matloff 2011, The Art of R Programming, No Starch Press San Francisco [ISBN: 9781593273842]
This module does not have any article/paper resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SDAAN_8 Higher Diploma in Science in Data Science & Analytics 1 Mandatory
CR_SDAAN_9 Master of Science in Data Science and Analytics 1 Mandatory

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie