SEEM1100 Introduction to Data Science

   

 

 

 

 

 

 

 
Course Code SEEM1100  
Course Title Introduction to Data Science  
Class Date 13, 15, 16, 17, 18 and 19 August 2022
Class Time 9:00am – 11:30am
Teaching Mode Online with live lessons  

 
Teacher Professor Helen Meng

Patrick Huen Wing Ming Professor of Systems Engineering & Engineering Management

Department of Systems Engineering & Engineering Management

Stanley Ho Big Data Decision Analytics Research Centre

The Chinese University of Hong Kong

  

 
  More about Professor Helen Meng:
Professor Wu Xixin

Research Assistant Professor

Department of Systems Engineering & Engineering Management

Stanley Ho Big Data Decision Analytics Research Centre

The Chinese University of Hong Kong

 

Prof. Hoi To WAI

Assistant Professor

Department of Systems Engineering & Engineering Management

Stanley Ho Big Data Decision Analytics Research Centre

The Chinese University of Hong Kong

 

Dr. Patrick TSANG

Lecturer

Department of Systems Engineering & Engineering Management

Stanley Ho Big Data Decision Analytics Research Centre

The Chinese University of Hong Kong

 

Dr. Kevin NG

Senior Lecturer

Department of Systems Engineering & Engineering Management

Stanley Ho Big Data Decision Analytics Research Centre

The Chinese University of Hong Kong
Medium of Instruction English
Course Description 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, systems science and data visualization, etc. Such a productive synthesis is applicable across may fields to bring about scientific discovery through data-intensive analytical methods. This course aims to help secondary school students acquire knowledge in disciplines related to data science and get experience in solving problems with advanced techniques.
Course Content We cover topics including:

  1. History and impact of Data Science.
  2. Visualizing Data: summary statistics, data display, data dictionaries, schema and graphical visualization.
  3. Analyzing Data: pattern recognition, correlations and relationships, hypotheses testing, statistical significance.
  4. Investigating Data: data mining, machine learning, inference, meta-data, modeling, eliciting meaning and validation.
  5. Application Contexts: Examples of useful applications from case studies
Learning Outcomes Upon completion of this course, students would be able to:

  1. Understand the different stages of data life cycle from data creation to decision-making.
  2. Learn the key techniques that model, visualize, store, retrieve, process, and analyze data.
  3. Understand the nature of data and the application of data science in different domains through case studies.
Recommended Reading(s) / Reference(s) The corresponding reading materials will be provided in each session.
Course Assessment Assignment (40%)

Final Exam (60%)
 
 
Last updated on 2 August 2022
   
SI |