| CODE | CIS5232 | ||||||||
| TITLE | Topics in Applied Data Science | ||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||
| MQF LEVEL | Not Applicable | ||||||||
| ECTS CREDITS | 5 | ||||||||
| DEPARTMENT | Computer 福利在线免费 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 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 Word2Vec and Transformer models such as BERT and GPT. 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 the student with a thorough grounding in text analytics and mining; - To develop the students鈥� 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 core principles and foundational concepts of data visualization; - Appreciate the complexity, and scope, of modern data visualization tools such as Tableau and Infogram; - Assess the effectiveness of various text analytics methods in addressing interdisciplinary challenges; - Assess the performance and effectiveness of NLP (natural language processing) tools and packages in real-word applications. 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: Main Texts: - Python Data Science Handbook: Essential Tools for Working with Data. 2nd Edition. Jake VanderPlas. O'Reilly, January 17,2023. ISBN-10 1098121228 ISBN-13 978-1098121228. - Python for Data Analysis 3e: Data Wrangling with pandas, NumPy, and Jupyter. Wes McKinney. O'Reilly, August 2022. ISBN-13 978-1098104030. |
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| STUDY-UNIT TYPE | Lecture, Tutorial and Project | ||||||||
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| LECTURER/S | John M. Abela Joseph Bonello |
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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 2025/6. It may be subject to change in subsequent years. |
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