Study-Unit Description

Study-Unit Description


TITLE Foundations of Data Science

UM LEVEL 01 - Year 1 in Modular Undergraduate Course



DEPARTMENT Artificial Intelligence

DESCRIPTION The world today is increasingly becoming data-driven with the result that data science and artificial intelligence are becoming an important part of our lives and society in general. There is vast amounts of data coming from science and industry, ranging from health to social media, business and retail. Furthermore, emerging technologies to process large-scale data and machine learning are creating the need to analyze such data to understand and glean insights and for building AI systems to automate decision making.

The overall aim of this study-unit is to cover the fundamental aspects of data science and to prepare students for an increasingly data-driven world. It will cover the technical pipeline from data collection, to processing, analysis and visualization using the popular language R, allowing students to develop software for data-intensive and AI-related applications.

Study-Unit Aims:

The aim of this study-unit is to provide students with skills to deal with an increasingly data-driven world. In particular, this study-unit will:

- expose students to different data science platforms and the language R;
- discuss a variety of techniques used for data collection, storage and data access, data cleaning, transformation, loading data (from multiple sources) for modeling and managing data;
- familiarize students with data analytics using probability theory, distributions, sampling techniques, multivariate thinking, correlation & causation, hypothesis testing;
- introduce students to different visualization tools and libraries to create clear, efficient and compelling visualizations and dashboards.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:

- gain an in depth understanding of the key concepts in data science through the use of different tools and approaches in a variety of application scenarios;
- analyse and evaluate the challenges behind data collection, sampling, quality assessment;
- use descriptive statistics (mean, mode, standard deviation, distributions etc.) to summarize a given dataset and formulate a hypothesis.

2. Skills:

By the end of the study-unit the student will be able to:

- employ the R programming language to analyse datasets;
- employ different libraries to programmatically create graphs describing the underlying datasets;
- use hypothesis testing to find if differences in datasets are statistically significant;
- employ data science concepts to solve problems based around artificial datasets.

Main Text/s and any supplementary readings:

Main Texts:

- Practical Data Science with R, 2nd Edition (2019) Nina Zumel, John Mount, ISBN 9781617295874
An introductory textbook which takes you from start to finish.

- Basic statistics for the behavioral sciences, 7th Edition (2014) Gary W. Heiman, ISBN-13: 978-1285055749
Fully explains statistics in a lively and reader-friendly manner.

Supplementary Readings:

- Think Stats: Probability and Statistics for Programmers, Allen B. Downey, Franklin W. Olin, ISBN 13: 9781491907337 (2014).

- Think Bayes: Bayesian Statistics Made Simple, Allen B. Downey, Franklin W. Olin, ISBN 13: 9781449370787 (2014).

STUDY-UNIT TYPE Lecture, Independent Study & Tutorial

Assessment Component/s Assessment Due Sept. Asst Session Weighting
Project SEM1 Yes 40%
Project SEM1 Yes 60%

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.