ENGG1003 Digital Literacy and Computational Thinking—P
(2023-2024 Term 2) Project Specification
Topic Deadline: 29-Mar-2024 (Fri) 23:59
Full Deadline: 23-Apr-2024 (Tue) 23:59
Max attainable score: 100+20 (bonus)
1. Objective and Expected Outcomes
In this project, students are expected to apply the knowledge and to demonstrate the skills learnt in the course independently. The area of study can be related to your discipline, your interest and/or some real-life cases. The study should be data driven with proper data analysis and good information presentation. Data for the study can be open-source or collected from existing databases. Students shall use computational tools and software introduced in the course.
2. Introduction
Conduct a study to focus on a problem or phenomenon that is related to the society or your field. You shall suggest, search, locate, access, u your data sources properly. Complete your study with data analytics and visual presentation tools available under Excel and/or Jupyter Notebook with Python.
You are required to complete and package all your work in at most two files: Project.xlsx and/or Project.ipynb. Upload and submit one or two files to Blackboard Project submission entry.
3. Requirements and Rubric (with bonus points for extended outcomes)
Your work shall include at least the following elements:
The first item 0 shall be submitted by the Topic Deadline. 3.0. A topic and a passage in 50-100 words: describe the topic you would like to inspect. Tell the audience the background of your chosen topic and what objective you would like to achieve. (5%) The following shall be submitted by the Full Deadline.
3.1. A passage in 150-200 words: describe your work and tell audience how to read/ use your file(s); point-form is accepted; Excel: in column A of worksheet [Declaration]; Python: in a text block at the top in [.ipynb]. (5%)
3.2. Data retrieval: such as URL download, API use; in structural data formats. (10%)
3.3. Data processing: cleansing, attribute/ variable definition, field and record setup, filtering, sorting, etc. (10%)
3.4. Data summarization/ description: count, mean, s.d., min, median (50-%), max, etc.; pivot table. (10%)
3.5. Data visualization: plotting charts and graphs with proper legend and labelling. (10%)
3.6. Data modeling: curve fitting, trend modeling, missing value prediction, etc. (10% + 5%)
3.7. Data interpretation/ user interaction: observations, finding trend, predictions, suggestions, etc. (10% + 5%)
3.8. Computational tools and techniques:
3.8.1. Excel: using conditionals such as if(), and(), or(), countif(), etc.; calling functions; and writing formulas.3.8.2. Python: using branching statements if-elif-else; using repetition statement for; calling modules/ functions. (10% + 5%)
3.9. Summary, conclusion, and reflection. (10% + 5%)
3.10. Citations and References in Chicago format: including both offline and online sources. (10%)
4. Academic Honesty and Declaration Statement
Attention is drawn to University policy and regulations on honesty in academic work, and to the disciplinary guidelines and procedures applicable to breaches of such policy and regulations. Details may be found at https://www.cuhk.edu.hk/policy/academichonesty/.
You must place the followingdeclaration statement (with your information filled in) in cell A1 of a worksheet named [Declaration] in your submitted Project.xlsx; and as a text block in the beginning of your submitted Project.ipynb file.
# ENGG1003 Digital Literacy and Computational Thinking - P
#
# Course Project
#
# I declare that the project here submitted is original
# except for source material explicitly acknowledged,
# and that the same or closely related material has not been
# previously submitted for another course.
# I also acknowledge that I am aware of University policy and
# regulations on honesty in academic work, and of the disciplinary
# guidelines and procedures applicable to breaches of such
# policy and regulations, as contained in the website.
#
# University Guideline on Academic Honesty:
# https://www.cuhk.edu.hk/policy/academichonesty/
#
# Student Name : <your name>
# Student ID : student ID>
# Class/Section : <your class/section>
# Date :
5. Grading Platforms
Excel workbooks will be graded on Microsoft Excel 2016 or later; in xlsx (but not older xls) format.
We will grade your work using Jupyter Notebook with Python and some preinstalled libraries and packages. The official development and testing platform has been standardized for all students on the provided Micro those on your own VM AND you shall also state so at the beginning of your Jupyter Notebook.
6. Submission
Please follow the steps below to submit your work by the deadline specified on the first page.
By the Topic Deadline:
1. Login Blackboard
2. Go to “Project” → “Project Topic and Description”
3. Under “Assignment Submission” > “Write Submission” > Type your topic and description
4. Click “Submit”
By the Full Deadline:
1. Login Blackboard
2. Go to “Project” → “Project Full Submission”
3. “Upload Files” → “Attach Files” → “Browse Local Files” → Pick Project.xlsx and/or Project.ipynb → Click “Open” to upload
. Note: need NOT Create Submission; do NOT type in Add Comments boxes.
4. Click “Submit” at the bottom Re-submissions are allowed. But only the latest one will be graded. A 30% mark deduction will be applied for late submissions within one week. Late submissions more than one week will not be graded.