ENGG1004N/K: Digital Literacy and Computational Thinking
Term 2, 2024-2025

The Most Important Messages Before You Scroll Down:

• Please read Honesty in Academic Work: A Guide for Students and Teachers for issues related to academic honesty. The Chinese University of Hong Kong places very high importance on honesty in academic work submitted by students, and adopts a policy of zero tolerance on cheating and plagiarism.

• Please read Guidelines To Academic Honesty for some more examples of improper usage of other's work.

• Please read Use of AI Tools in Teaching, Learning and Assessments – A Guide for Students for the University's policy on the use of AI in academic work.

• Please read Student/Faculty Expectations on Teaching and Learning.

By taking this course, you are assumed to have read and understood the contents and aspects described inside the above-listed documents.

News

• (Mar 23) The project starter file is now available here.

• (Feb 12) Several datasets to be used for data science demo are listed as follows:

  • book_price.csv

  • houseprice.csv

  • shark.csv

• (Jan 22) The completed demo-B file is available.

• (Jan 15) The demo-A file completed in class is now available.

• (Jan 13) Two datasets to be used in class are now available:

  • demo-A.xlsx

  • demo-B.xlsx

• (Jan 1) Welcome to the course.

General Informaiton

Instructor: W.-Y. Keung

• Office: HCA320D

• Email: wykeung@cse.cuhk.edu.hk

• Office Hours: by appointment

Tutors

• ENGG1004K: Miss Rita LIU

  • Office: HCA328

  • Email: ritaliu@cse.cuhk.edu.hk

• ENGG1004N: Miss Cherry CHEUNG

  • Office: HCA328

  • Email: cherryccy@cse.cuhk.edu.hk

Time and Venue

• ENGG1004K: Wednesday 08:30-11:15 @LSB C1

• ENGG1004N: Tuesday 10:30-13:15 @LSB C4

See the list of abbreviations here.

Course Descripton/ Learning Outcomes

• Office of Digital Literacy Education

• Blackboard

Assessment Scheme

• Weekly laboratory: 20%

• Online assignments: 40%

• Active participation: 10%

• Final project: 30%

Important: You shall pass (i.e., scoring at least 60%) in all the above listed assessment items to be eligible to obtain an overall passing grade in this course.

Lecture Slides

The main notes are provided on blackboard. We provide the presented (and very often abridged) version herein for your reference.

Course Overview and Logistics

• Course Information

• Digital Technologies

Software Installation Instructions

• MS Azure

  • Azure Lab Service

  • Microsoft Remote Desktop (for Mac users)

• R and RStudio

Digital Literacy

• Spreadsheet

• Digital Literacy

• Information Security

• Data Science

  • Part 1: Misuse of Statistics

  • Part 2: Data Science Process

Computational Thinking

• R Basics

• R Iterations

• R Functions and Vectors

• Curve Fitting on R

• Machine Learning on R

• Machine Learning Applications

• Advanced Topics