COMP47350 Data Analytics (Conversion)

Academic Year 2021/2022

This is a core module on the MSc. Computer Science (Conversion) and Higher Diploma in Computer Science programmes. Data Analytics involves working with raw data towards a deeper understanding of the patterns and structures within the data to support making predictions and decision making. Data Analytics techniques enable the creation of new knowledge products and services.

This module aims to develop the fundamental skills for data analytics including:
(1) problem formulation,
(2) getting data,
(3) understanding and preparing data,
(4) model fitting and evaluation.


This module uses Python as a programming language and popular data analytics Python packages (e.g., pandas, matplotlib, scikit-learn).

Show/hide contentOpenClose All

Curricular information is subject to change

Learning Outcomes:

On successful completion of this module the learner will be able to:

1. Understand the principles and the purposes of data analytics.
2. Use Python to retrieve and analyse real-world datasets.
3. Apply the process of data understanding and address data quality issues.
4. Use appropriate machine learning techniques for a given data analytics problem.
5. Design evaluation experiments for selecting the best predictive model for a given analytics problem.

Student Effort Hours: 
Student Effort Type Hours
Lectures

24

Seminar (or Webinar)

2

Practical

24

Autonomous Student Learning

70

Total

120

Approaches to Teaching and Learning:
active/task-based learning;
lectures;
lab work;
enquiry & problem-based learning;
case-based learning; 
Requirements, Exclusions and Recommendations
Learning Recommendations:

Prior experience with programming in Python and working with the object-oriented programming paradigm.


Module Requisites and Incompatibles
Incompatibles:
COMP41680 - Data Science in Python, COMP47670 - Data Science in Python (MD)


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Assignment 1 Varies over the Trimester n/a Graded No

50

Assignment: Assignment 2 Varies over the Trimester n/a Graded No

50


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

1. "Fundamentals of Machine Learning for Predictive Data Analytics", J Kelleher, B MacNamee, A, D'Arcy, MIT Press, 2015
https://mitpress.mit.edu/books/fundamentals-machine-learning-predictive-data-analytics
http://machinelearningbook.com

2. The WEKA book:
"Data Mining: Practical Machine Learning Tools and Techniques", Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal
http://cs.du.edu/~mitchell/mario_books/Data_Mining:_Practical_Machine_Learning_Tools_and_Techniques_-_2e_-_Witten_&_Frank.pdf
Official website: http://www.cs.waikato.ac.nz/ml/weka/book.html
"Data Mining", I Witten, E Frank, M. Hall.
Its a good read for introductory concepts and covers more material
than the above textbook.

3. "The Elements of Statistical Learning", Hastie, Tibshirani, Friedman
https://statweb.stanford.edu/~tibs/ElemStatLearn/