# Untitled

**Source**: https://teaching.scss.tcd.ie/wp-json/wp/v2/module/370
**Parent**: https://teaching.scss.tcd.ie/module/stu44003-data-analytics/

{"id":370,"date":"2020-07-17T15:27:24","date\_gmt":"2020-07-17T15:27:24","guid":{"rendered":"https:\/\/teaching.scss.tcd.ie\/?post\_type=module&p=370"},"modified":"2025-09-05T13:32:22","modified\_gmt":"2025-09-05T12:32:22","slug":"stu44003-data-analytics","status":"publish","type":"module","link":"https:\/\/teaching.scss.tcd.ie\/module\/stu44003-data-analytics\/","title":{"rendered":"STU44003 – Data Analytics"},"content":{"rendered":"\n

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| **Module Code<\/strong><\/td> STU44003<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Name<\/strong> <\/td> Data Analytics<\/a><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **ECTS Weighting<\/strong>**[**[1]<\/strong><\/a><\/strong><\/td> 10 ECTS <\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Semester taught<\/strong><\/td> Semester 1 & 2<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Module Coordinator\/s <\/strong><\/td> Profs. Alessio Benavoli (semester I) and Athanasios Georgiadis (semester II)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nModule Learning Outcomes <\/h2>\n\n\n\n On successful completion of this module, students will be able to: <\/p>\n\n\n\n LO1. Identify, compare and select appropriate analysis and modelling techniques for a range of applications. <\/p>\n\n\n\n LO2. Deploy and document appropriate set of self-selected analysis techniques in response to the defined problem areas.<\/p>\n\n\n\n LO3. Demonstrate utilization of the appropriate statistical packages (in either R or Python) to perform and effectively present and interpret the analysis results.<\/p>\n\n\n\nModule Content<\/h2>\n\n\n\n  \n- Overview of the field<\/li>\n\n\n\n- Review of Probability Theory<\/li>\n\n\n\n- Introduction of Monte-Carlo Methods and Simulation<\/li>\n\n\n\n- Review of Hypothesis Testing<\/li>\n\n\n\n- Analysis of Categorical Data<\/li>\n\n\n\n- Concepts of the Information Theory, Entropy, Mutual Information, Conditional Entropy, and Information Gain<\/li>\n\n\n\n- Using CHAID in Classification Tree<\/li>\n\n\n\n- Using Gini Index in Classification Tree<\/li>\n\n\n\n- Detailed Discussion of Classification and Regression Tree<\/li>\n\n\n\n- Overfitting and the corresponding techniques to avoid overfitting (Cross Validation, Bagging, Boosting, Random Forest, …)<\/li>\n\n\n\n- Rule Fit Procedure, and Model Evaluation<\/li>\n\n\n\n- Handling Unbalance Dataset<\/li>\n\n\n\n- Concept of Similarity and Distance<\/li>\n\n\n\n- Distance Measures for Various Data Types<\/li>\n\n\n\n- Hierarchical Cluster Analysis<\/li>\n\n\n\n- Principal Component Analysis<\/li>\n\n\n\n- Concepts of Data Missingness and Its Mechanism<\/li>\n\n\n\n- Methods of Missing Data Imputation (MDI) <\/li>\n\n\n\n- Using package MICE in R for MDI<\/li>\n\n\n\n- Nonparametric methods<\/li>\n\n\n\n- Introduction to Bayesian Statistics<\/li>\n\n\n\n- Examples of applications of Bayesian Statistics (Gibbs Sampling, …) <\/li>\n<\/ul>\n\n\n\nTeaching and learning Methods <\/h2>\n\n\n\n Lectures and lab sessions. <\/p>\n\n\n\nAssessment Details <\/h2>\n\n\n\n                                              |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |                                             | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |                                             | **Assessment Component<\/strong><\/td> **Brief Description<\/strong> <\/td> **Learning Outcomes Addressed<\/strong><\/td> **% of total<\/strong><\/td> **Week set<\/strong><\/td> Week Due<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Coursework <\/td> semester I<\/td> All<\/td> 20%<\/td> <\/td> <\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Coursework<\/td> semester II<\/td> All<\/td> 20%<\/td> <\/td> <\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Examination<\/td> in-person (2 hours)<\/td> All<\/td> 60%<\/td> <\/td> Exam session in Semester 2<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nReassessment Details<\/h2>\n\n\n\n Examination (2 hours, 100%)<\/p>\n\n\n\n Contact Hours and Indicative Student Workload<\/p>\n\n\n\n  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | **Contact Hours (scheduled hours per student over full module), broken down by<\/strong>: <\/td> **54 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Lecture<\/td> 44 hours<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Laboratory<\/td> 10 hours<\/td><\/tr>|  |  |  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | <\/td> <\/td><\/tr>|  |  |  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | <\/td> <\/td><\/tr>|  |  |  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | --- | --- | | **Independent study (outside scheduled contact hours), broken down by:<\/strong><\/td> **40 hours<\/strong><\/td><\/tr>|  |  |  |  |  |  | | --- | --- | --- | --- | --- | --- | | Preparation for classes and review of material (including preparation for examination, if applicable<\/td> 30 hours<\/td><\/tr>|  |  |  |  | | --- | --- | --- | --- | | Completion of assessments (including examination, if applicable)<\/td> 10 hours<\/td><\/tr>|  |  | | --- | --- | | **Total Hours<\/strong><\/td> **94 hours<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nRecommended Reading List<\/h2>\n\n\n\n \n- Peter Bruce and Andrew Bruce, **Practical Statistics for Data Scientists<\/strong>, O’Reilly, 2017<\/li>\n\n\n\n- Xin\_She Yang, **Introduction to Algorithms for Data Mining and Machine Learning<\/strong>, Academic Press, 2019<\/li>\n\n\n\n- Alan Agresti, **An Introduction to Categorical Data Analysis<\/strong>, John Wiley and Sons, 2019<\/li>\n\n\n\n- Michael Greenarcre and Raul Primicerio, **Multivariate Analysis of Ecological Data<\/strong>, Fundacion BBVA, 2013<\/li>\n\n\n\n- Max Kuhn and Kjell Johnson, **Applied Predictive Modeling<\/strong>, Springer, 2013<\/li>\n\n\n\n- Trevor Hastie, Robert Tibshirani, and Jerome Friedman, **The Elements of Statistical Learning<\/strong>, Springer, 2021<\/li>\n\n\n\n- Pratap Dangeti, **Statistics for Machine Learning<\/strong>, Packt, 2017<\/li>\n\n\n\n- Gururajan Govindan, Shubhangi Hora, and Konstantin Palagachev, **The data Analysis Workshop<\/strong>, Packt, 2020<\/li>\n\n\n\n- Stef van Buuren, **Flexible Imputation of Missing Data<\/strong>, CRC Press, 2018<\/li>\n\n\n\n- William M. Bolstad, James M. Curran, **Introduction to Bayesian Statistics<\/strong>, Wiley, 2017 <\/li>\n<\/ul>\n\n\n\nModule Pre-requisites <\/h2>\n\n\n\n **Prerequisite modules:<\/strong> This is a year 4 module.<\/p>\n\n\n\n **Other\/alternative non-module prerequisites:<\/strong> NA<\/p>\n\n\n\nModule Co-requisites <\/h2>\n\n\n\n None<\/p>\n\n\n\nModule Website<\/h2>\n\n\n\n [Blackboard<\/a><\/p>\n","protected":false},"excerpt":{"rendered":" (Semester 1 & 2, 10 ECTS) The aim of the course is to introduce the students to a set of techniques including classification and regression trees, and ensemble methods.<\/p>\n","protected":false},"author":104,"menu\_order":0,"template":"","meta":[],"module\_category":[20,69,100,120,78],"class\_list":["post-370","module","type-module","status-publish","hentry","module\_category-fy","module\_category-ics-year-4-electives","module\_category-msiss-year-4-core","module\_category-stats-year-4","module\_category-visiting"],"\_links":{"self":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module\/370","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module"}],"about":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/types\/module"}],"author":[{"embeddable":true,"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/users\/104"}],"wp:attachment":[{"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/media?parent=370"}],"wp:term":[{"taxonomy":"module\_category","embeddable":true,"href":"https:\/\/teaching.scss.tcd.ie\/wp-json\/wp\/v2\/module\_category?post=370"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}](https://teaching.scss.tcd.ie/wp-json/wp/v2/module/\"https:\/\/tcd.blackboard.com\/webapps\/login\/\")************************** |** | | | | |** |** | | | | | | | | |** |** | | | | | | | | | | | | | | | | | | | |** |** |** |** |** | |** | |** | |**](https://teaching.scss.tcd.ie/wp-json/wp/v2/module/\"#_ftn1\")**** | |** | |** |