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 society or your field. You shall suggest, search, locate, access, use and cite your data sources properly. Complete your study with data analytics and visual presentation tools available under Excel and/or Jupyter Notebook with Python.
Topic Submission: you are required to fill in and submit a given Word file ProjectTopicDescription.docx.
Full Submission: 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 Rubrics (with bonus points for extended outcomes)
Your work shall include at least the following elements:
The first item 3.0. shall be submitted by the Topic Submission Deadline.
3.0. Project title and description
3.0.1. A topic/project title.
3.0.2. A passage in 50-100 words: describe the topic you would like to inspect. Tell the background of your topic, and give objective(s) to achieve.
3.0.3. Provide source(s) of dataset(s) to work on, such as URL of the dataset(s).
3.0.4. Give an adequate explanation on how the dataset is coherently relevant to the proposed project topic and description.
Students are NOT expected to change the submitted project topic then.
(10%)
The following items shall be submitted by the Full Submission Deadline.
3.1. A passage in 150-200 words: describe your work and tell the marker 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.
(5%)
3.3. Data processing: cleansing, attribute/ variable definition, field and record setup filtering, sorting, etc.
(10%)
3.4. Data summarization/ description:
3.4.1. Statistics: count, mean, s.d., min, median (50-%), max, etc.; pivot table.
3.4.2. Observation: Briefly describe these statistics with logical explanation
(10%+5%)
3.5. Data visualization:
3.5.1. Plot charts and graphs with proper legend and labelling.
3.5.2. Observation: Explain the data visualization (e.g., selection/suitability the charts). Reason for selecting the charts/graphic visualization.
3.5.3. Finding trend: What trend do you observe/find with the data visualization, provide a logical reasoning in connection with the data visualization
(15%)
3.6. Data modeling:
3.6.1. Perform. data modeling: Select a suitable model to do data modeling. For example, curve fitting models, classification models or clustering models
3.6.2. Provide the data modeling graphs with proper legends, labels and equation(s) if any.
3.6.3. What is the logical reason for the data model selected, adopted and used in the project?
3.6.4. Explain the outcome(s) of data modeling results such as over fitting/under fitting or optimal fitting or predictions/curve fitting or clustering or classification.
(15%+5%)
3.7. Computational tools and techniques:
3.7.1. Excel: using conditionals such as if(), and(), or(), countif(), etc.; calling functions; and writing formulas.
3.7.2. Python: using branching statements if-elif-else; using repetition statement for; calling modules/ functions.
(10%+5%)
3.8. Summary, conclusion, and reflection.
(10%+5%)
3.9. Citations and References in Chicago format: including both offline and online sources.
(10%)
Total Points: 100%+20% Bonus