| CODE | CIS5003 | ||||||||||||||||
| TITLE | Principles of Data Science | ||||||||||||||||
| UM LEVEL | 05 - Postgraduate Modular Diploma or Degree Course | ||||||||||||||||
| MQF LEVEL | 7 | ||||||||||||||||
| ECTS CREDITS | 5 | ||||||||||||||||
| DEPARTMENT | Computer Information Systems | ||||||||||||||||
| DESCRIPTION | Data science is a multi-disciplinary field that is concerned with using automated techniques to extract value and knowledge from data. These techniques can be applied in numerous domains, such as biology, physics and economics. As data becomes more important in different domains and as its volume grows due to new technologies, so will the techniques required to process this data also need to be explored and improved to meet the new challenges posed by this growth. The study-unit focuses on giving the students the right tools to work with data. This involves an introduction to Python, a programming language that is widely used in data science projects. It will provide the students with the necessary techniques to process, transform and manage data, perform exploratory data analysis to learn more about the data and improve any hypothesis formed from the data, apply the appropriate machine learning techniques and present conclusions reached from the data suitably according to best practices. Study-unit Aims: The aims of the study-unit are to: i) Introduce the best practices, standards, techniques, and concepts for obtaining data; ii) Use case studies to clean obtained data and use best practices to share the data; iii) Differentiate between different algorithms that can be applied to explore and learn more about the data; iv) Evaluate which is the best Machine Learning algorithm to apply depending on the type of data available; v) Apply visualisation best practises to present interesting conclusions drawn from the data. Learning Outcomes: 1. Knowledge & Understanding By the end of the study-unit the student will be able to: 1) Have an in depth appreciation of the basic principles of data gathering and management; 2) Outline the research landscape of exploratory data analysis; 3) Choose the most suitable technique/s to analyse data; 4) Apply suitable Machine Learning techniques to a data set; 5) Contrast different visualisation techniques and methods of presenting data. 2. Skills By the end of the study-unit the student will be able to: 1) Discuss and critically analyse outcomes from use-cases covering current open problems in the field; 2) Search for state of the art techniques to solve domain specific problems; 3) Present a solution using proper visualisation tools; 4) Analytical skills: Develop analytical skills by applying machine learning techniques to a dataset; 5) Assessment Skills: Use industry standard techniques to evaluate the performance of the algorithm chosen; 6) Communications skills: Use techniques to share data with colleagues and present findings to a non-technical audience effectively. Main Text/s and any supplementary readings: Course notes available on VLE. - Data Science for Business: What you need to know about data mining and data-analytic thinking, Foster Provost, Tom Fawcett (2013), ISBN 1449361323 (ISBN13: 9781449361327). - Doing Data Science, Rachel Schutt, Cathy O'Neil (2013), ISBN 1449358659 (ISBN13: 9781449358655). - Python for Data Analysis, Wes McKinney (2012), ISBN 1449319793 (ISBN13: 9781449319793). |
||||||||||||||||
| ADDITIONAL NOTES | Pre-Requisite Study-units: CIS3187 or a similar study-unit | ||||||||||||||||
| STUDY-UNIT TYPE | Lecture, Independent Study & Tutorial | ||||||||||||||||
| METHOD OF ASSESSMENT |
|
||||||||||||||||
| LECTURER/S | John M. Abela Joseph Bonello Lalit Garg |
||||||||||||||||
|
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. |
|||||||||||||||||