Short Title:Data Analysis
Full Title:Data Analysis
Language of Instruction:English
Module Code:DATAH3008
 
Credits: 5
Field of Study:Computer Science
Module Delivered in 9 programme(s)
Reviewed By:FINBARR FEENEY
Module Author:Jelena Vasic
Module Description:An introduction to the field of data analysis, including its positioning in the wider world and the main methods and concepts of both its statistics and machine learning disciplinary facets
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Prepare data for analysis, including cleaning, transforming and imputing missing values, using standard methods and tools.
LO2 Apply numeric and visual methods of exploratory data analysis, including data summarisation and investigation of relationships.
LO3 Perform tests for the evaluation and comparison of data sets and their characteristics and apply methods for the evaluation of predictions.
LO4 Explain the difference and relationship between statistics and machine learning and how elements of analysis covered in the module support statistical inference and machine learning.
 

Module Content & Assessment

Course Work
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Assignment The student is required to prepare a data set and perform exploratory analysis on it by following prescribed steps and techniques and by means of a prescribed programming language. 1,2 20.00 Week 24
Assignment The student is required to work independently to answer a number of questions posed about a given data set. This will include choosing and then applying data preparation, analysis and visualisation methods and techniques and the writing of a short report. 1,2,3 30.00 Sem 2 End
End of Module Formal Examination
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Formal Exam n/a   50.00 End-of-Semester

IT Tallaght 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 Lecture 2.00 Every Week 2.00
Lab Lab 2.00 Every Week 2.00
Independent Learning Independent learning 4.00 Every Week 4.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lecture 2.00 Every Second Week 1.00
Lab Lab 2.00 Every Second Week 1.00
Independent Learning Independent learning 6.00 Every Week 6.00
Total Weekly Learner Workload 8.00
Total Weekly Contact Hours 2.00
 

Module Resources

Required Book Resources
  • GLENN J. MYATT, WAYNE P. JOHNSON 2014, MAKING SENSE OF DATA I A Practical Guide to Exploratory Data Analysis and Data Mining, 1 Ed., 1-6, Wiley Hoboken, New Jersey [ISBN: 9781118407417]
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
TA_KACTM_B Bachelor of Science (Honours) in Computing with Information Technology Management 5 Elective
TA_KACOS_B Bachelor of Science (Honours) in Computing with Software Development 5 Elective
TA_KACOD_B Bachelor of Science (Hons) in Computing with Data Analytics 5 Mandatory
TA_KACTM_D Bachelor of Science in Computing with Information Technology Management 5 Elective
TA_KACOS_D Bachelor of Science in Computing with Software Development 5 Elective
TA_KCOSD_D Bachelor of Science in Computing with Software Development - Year 3 (Add on) 5 Elective
TA_KITMG_D Bachelor of Science in IT Management 5 Elective
TA_BDAMKT_D BSc in Data Analytics with Digital Marketing 1 Mandatory
TA_KCOMP_HD Higher Diploma in Science in Computing 1 Elective