Module Details

Module Code: COMP C9047
Full Title: Data Analytics for Immersive Environments
Valid From:: Semester 2 - 2021/22 ( January 2022 )
Language of Instruction:English
Duration: 1 Semester
Credits:: 5
Module Owner::
  • Martin Mchugh
  • Niall McGuinness
Departments: Visual and Human-Centred Computing
Module Description: The aim of this module is to provide students with the knowledge to store, retrieve, analyse and visualise data to help inform the development of immersive technology solutions.
 
Module Learning Outcome
On successful completion of this module the learner will be able to:
# Module Learning Outcome Description
MLO1 Design and Implement effective data models.
MLO2 Choose SQL functionality for storing, manipulating and retrieving data in databases.
MLO3 Analyse immersive technology data sets and effectively communicate the results.
MLO4 Evaluate the appropriate data analysis techniques using regression methods and statistical inference.
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 DkIT 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
 
Module Indicative Content
Relational Databases and Storage
Collecting Data. Data Storage. Data Modelling. Normalisation. Indexes. Query processing and optimisation. Database Performance Evaluation.
SQL for Data Retrieval
Outputting Data Streams. Complex Joins/Multi-Joins. Analytic Functions. Nested and Repeated Data. Writing Efficient Queries.
Data Cleaning
Handling Missing Values. Scaling and Normalization. Parsing Dates. Character Encodings. Inconsistent Data Entry.
Visualisation and Descriptive Statistics
Univariate, bivariate and multivariate data visualisation techniques such as Bar Charts, Boxplots, Heatmaps, Scatterplots, Histograms. Density plots. Measures of centrality and spread.
Regression & Correlation
Pearson's and Spearman Correlation, the Coefficient of Determination, Linear Regression Analysis.
Statistical Inference
Inferential Statistics, Confidence interval estimates & construction of appropriate hypothesis tests.
Module Assessment
Assessment Breakdown%
Course Work75.00%
Project25.00%
Module Special Regulation
 

Assessments

Full-time

Course Work
Assessment Type Continuous Assessment % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing S1 Week 3 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Student will design, implement and test a database schema design.
Assessment Type Continuous Assessment % of Total Mark 50
Marks Out Of 100 Pass Mark 40
Timing S1 Week 6 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Student will visualise the data from assessment 1, applying data analysis and visualisation techniques.
Project
Assessment Type Group Project % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing S1 Week 12 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Students will work as a team to visualise and analyse their main collaborative project.
No Practical
No Final Examination

Part-time

Course Work
Assessment Type Continuous Assessment % of Total Mark 25
Marks Out Of 0 Pass Mark 0
Timing S1 Week 3 Learning Outcome 1,2
Duration in minutes 0
Assessment Description
Student will design, implement and test a database schema design.
Assessment Type Continuous Assessment % of Total Mark 50
Marks Out Of 0 Pass Mark 0
Timing S1 Week 6 Learning Outcome 3,4
Duration in minutes 0
Assessment Description
Student will visualise the data from assessment 1, applying data analysis and visualisation techniques.
Project
Assessment Type Group Project % of Total Mark 25
Marks Out Of 100 Pass Mark 40
Timing S1 Week 12 Learning Outcome 1,2,3,4
Duration in minutes 0
Assessment Description
Students will work as a team to visualise and analyse their main collaborative project.
No Practical
No Final Examination
Reassessment Requirement
No repeat examination
Reassessment of this module will be offered solely on the basis of coursework and a repeat examination will not be offered.

DKIT reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Practical Contact Lecture/Lab Every Week 2.00 2
Directed Reading Non Contact Concepts relating to data storing analysis and visualisation Every Week 2.00 2
Independent Study Non Contact Concepts relating to data storing analysis and visualisation Every Week 5.00 5
Total Weekly Learner Workload 9.00
Total Weekly Contact Hours 2.00
Workload: Part-time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Practical Contact Lecture/Lab Every Week 2.00 2
Directed Reading Non Contact Concepts relating to data storing analysis and visualisation Every Week 2.00 2
Independent Study Non Contact Concepts relating to data storing analysis and visualisation Every Week 5.00 5
Total Weekly Learner Workload 9.00
Total Weekly Contact Hours 2.00
 
Module Resources
Recommended Book Resources
  • James, G., Witten, D., Hastie, T., Tibshirani, R.. An Introduction to Statistical Learning: with Applications in R, Springer-Verlag New York Inc., [ISBN: 9781461471370].
This module does not have any article/paper resources
Other Resources