|Course Code and Name: SEEM2460/ ESTR2540 Introduction to Data Science|
This course presents an introductory roadmap into the newly emerged and rapidly evolving field of data science, with the objective of introducing the problem-solving mindset in a data-intensive context. The course projects data science as a productive synthesis of its parent disciplines, including mathematics, statistics, computing, data mining, system science and data visualization, etc. Such a productive synthesis is applicable across many fields to bring about scientific discovery through data-intensive, analytical methods. This course aims to help navigate students in taking more advanced courses in its parent disciplines to build up their expertise in data science. We cover topics including:
• History and impact of Data Science.
• Collecting Data: sources, types and categorization of data.
• Visualizing Data: summary statistics, data display, data dictionaries, schema and graphical visualization.
• Analyzing Data: pattern recognition, correlations and relationships, hypotheses testing, statistical significance.
• Investigating Data: data mining, machine learning, inference, meta-data, modeling, eliciting meaning and validation.
• Application contexts: Examples of useful applications from case studies.
Upon completion of this course, students would be able to:
• Understand the different stages of data life cycle from data creation to decision making;
• Learn the key techniques that model, visualize, store, retrieve, process, and analyze data;
• Understand the nature of data and the application of data science in different domains through case studies.
(P1) The ability to apply knowledge of mathematics, science, and engineering appropriate to the degree discipline (K/S)
(P2) The ability to design and conduct experiments, as well as to analyze and interpret data (K/S)
(P3) The ability to design a system, component, or process to meet desired needs within realistic constraints, such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability (K/S)
(P4) The ability to function in multi-disciplinary teams (S/V)
(P5) The ability to identify, formulate, and solve engineering problems (K/S)
(P6) The understanding of professional and ethical responsibility (V)
(P7) The ability to communicate effectively (S)
(P8) The ability to understand the impact of engineering solutions in a global and societal context, especially the importance of health, safety and environmental considerations to both workers and the general public (V)
(P9) The ability to recognize the need for, and to engage in life-long learning (V)
(P10) The ability to stay abreast of contemporary issues (S/V)
(P11) The ability to use the techniques, skills, and modern engineering tools necessary for engineering practice appropriate to the degree discipline (K/S)
(P12) The ability to use the computer/IT tools relevant to the discipline along with an understanding of their processes and limitations (K/S/V)
(P13) The ability to apply the skills relevant to the discipline of operations research and information technology and their applications in engineering and managerial decision making, especially in financial services, logistics and supply chain management, business information systems, and service engineering and management (K/S)
K = Knowledge outcomes
S = Skills outcomes
V = Values and attitude outcomes