| CODE | CIS5231 | ||||||||
| TITLE | Topics in Applied Data Science | ||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||
| MQF LEVEL | 7 | ||||||||
| ECTS CREDITS | 6 | ||||||||
| DEPARTMENT | Computer Information Systems | ||||||||
| DESCRIPTION | Business and industry are becoming data-driven in today's world. The need for data analytics and information visualization have become more important than ever. In this study unit the student will learn how to perform exploratory data analysis with the aim to summarize, understand, discover hidden patterns, and identify relationships. Visualization packages and tools, such as Matplotlib and Seaborn, are used to bring the data to life. Commercial tools, such as Tableau and Infogram, will also be discussed. The second part of this study-unit will focus on text analytics. From social media to product reviews, text has become an increasingly important type of data across many applications. In this study-unit students will learn to use the latest packages and tools to wrangle and visualize text, perform sentiment analysis, and run and interpret various topic models. We shall also discuss the latest deep learning models that are used for text analytics and mining. These include Google BERT and AlBERTa. Study-Unit Aims: The principal aims of this study-unit are: - To introduce students to the important area of data visualization. This will include coverage of the main concepts, ideas, and techniques of data visualization; - To provide students with a thorough grounding in text analytics and mining; - To develop students’ understanding, and development, of the tools and practices that are used to create data visualization and text mining analytics in business, industry, and academia. Learning Outcomes: 1. Knowledge & Understanding: By the end of the study-unit the student will be able to: - Explain the fundamentals, as well as some of the advanced methods, of data visualization; - Appreciate the complexity, and scope, of modern data visualization tools such as Tableau and Infogram; - Explain the fundamentals of the interdisciplinary area of text analytics and mining; - Have a good understanding of how text analytics and NLP (natural language processing) tools and packages work. 2. Skills: By the end of the study-unit the student will be able to: - Design, build, test, and deploy data and information visualization solutions for various domains; - Analyse, design, and implement text analytics and mining solutions for tasks such as sentiment analysis, text forensics, SPAM detection, etc; - Make informed technology and design choices for the implementation of data visualization and text analytics solutions in business, industry, and academia. Main Text/s and any supplementary readings: Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures 1st Edition. Claus O. Wilke, O'Reilly Media, Inc. ISBN-13: 978-1492031086 ISBN-10: 1492031089. Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications. Jens Albrecht , Sidharth Ramachandran , et al. O'Reilly Media, Inc. December 2020. ISBN-13: 978-1492074083 ISBN-10: 149207408X. |
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| ADDITIONAL NOTES | Pre-Requisite Study-Unit: CCE5108 | ||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||
| METHOD OF ASSESSMENT |
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| LECTURER/S | John M. Abela Joseph Bonello (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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