Study-Unit Description

Study-Unit Description


TITLE Data Analysis Techniques

UM LEVEL 05 - Postgraduate Modular Diploma or Degree Course



DEPARTMENT Artificial Intelligence

DESCRIPTION This study-unit will introduce students to techniques related to the analysis of data based around the OSEMN (Obtain, Scrub, Explore, Mode and iNterprete) data analysis model. They will explore why data is needed and how it is used to make decisions, including how data is collected, wrangled and transformed and analysed in preparation for Machine Learning. Students will furthermore learn and apply a suite of algorithms in the process. A number of case studies will be used so that students will learn how to solve problem, generate evidence, and present solutions using data handling approaches. The topics that will be covered include:
- Quantitative vs Qualitative Data;
- Descriptive, Predictive, and Prescriptive Analytics;
- Data Collection;
- Data Wrangling and Transformation;
- Data Modeling;
- Bias and Variance;
- Algorithms such as Naïve Bayes and kNNs, Clustering;
- Gradient Boost;
- PCA & Dimensionality Reduction;
- Over and Under Sampling Techniques (SMOTE);
- Anomaly detection;
- Text, Image and Video Analytics.

Study-Unit Aims:

The aims of this study-unit are to:
- Give student the necessary fundamentals of data analysis techniques to solve data science related problems;
- Provide the students with the skills of applying data analysis in real-life scenarios;
- Expose the student to different techniques and technologies for the purposes of solving the above-mentioned data analysis problems.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:
- Explain the foundations of data analysis techniques;
- Outline the importance of a wide variety of data collection methods;
- Discuss different types of Data Wrangling and Transformation techniques;
- Evaluate a repertoire of data analysis techniques to be used in different situations and across different types of data, including text, image and video;
- Recognise where to apply different techniques intended to reduce dimensionality and address situations of class imbalance.

2. Skills:

By the end of the study-unit the student will be able to:
- Solve problems of intermediate complexity in data analysis;
- Determine the appropriate data collection method to use in a variety of different contexts;
- Use the appropriate data wrangling and transformation techniques based on a number of given scenarios;
- Use techniques to solve problems related to dimensionality reduction, class imbalance;
- Use appropriate techniques to perform analysis over different types of data, including, text, image and video data.

Main Text/s and any supplementary readings:

Main Texts:

Introduction to Data Science: Data Analysis and Prediction, Rafael A. Irizarry, ISBN-13: 978-0367357986.

Different content available on the web will also be recommended.

ADDITIONAL NOTES Pre-requisite Qualifications: Degree in ICT, Computing, Mathematics or Physics

STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

Assessment Component/s Assessment Due Sept. Asst Session Weighting
Presentation (15 Minutes) SEM2 Yes 20%
Project SEM2 Yes 80%

LECTURER/S Charlie Abela (Co-ord.)
Konstantinos Makantasis


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The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2023/4. It may be subject to change in subsequent years.