| CODE | ARI1104 | ||||||||
| TITLE | Foundations for Data Science 2 | ||||||||
| UM LEVEL | 01 - Year 1 in Modular Undergraduate Course | ||||||||
| MQF LEVEL | 5 | ||||||||
| ECTS CREDITS | 5 | ||||||||
| DEPARTMENT | Artificial Intelligence | ||||||||
| DESCRIPTION | Data preparation and feature engineering is a crucial, and sometimes overlooked, step in the machine learning and AI pipeline. The study-unit brings together advanced techniques from Data and Feature Engineering that are required for the successfull training and deployment of Machine Learning models. Topics covered in this unit include: - Web scraping with Python (beautiful soup library) - Exploratory Data Analysis (pandas_profiling) - Missing data imputation - Categorical Variable Encoding - Variable Transformation and Discretisation - Outlier Handling - Feature Selection - Handling data imbalance - Explore feature extraction techniques (Images and text) Study-Unit Aims: - Teach the core mathematical and algorithmic components that students would require to understand the latest advancements in Data and Feature Engineering. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Define the mathematical and algorithmic framework required to conduct sound Data and Feature Engineering. - Identify the strengths and weaknesses, and applicability, of the different techniques. 2. Skills: By the end of the study-unit the student will be able to: - Employ the Python programming language in the analysis of datasets; - Employ Data and Feature Engineering as part of the Machine Learning / AI pipeline as part of the strengths, pitfalls, challenges and processes required in Python programming. Main Text/s and any supplementary readings: Feature Engineering for Machine Learning, Principles and Techniques for Data Scientists. Authors: Alice Zheng Publisher: O'Reilly Media Published: 2018 Python Feature Engineering Cookbook Authors: Soledad Galli Publisher: Packt Publishing Published: 2020 |
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| ADDITIONAL NOTES | Pre-requisite Study-unit: Foundations for Data Sience 1 | ||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | Brandon Birmingham Vincent Vella (Co-ord.) |
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The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
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 2024/5. It may be subject to change in subsequent years. |
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