1/8 MIE 1624 Introduction to Data Science, Analytics and AI – Assignment 3

MIE 1624 Introduction to Data Science and Analytics – Winter 2026
Assignment 3
Due Date: 11:59pm, April 3rd, 2026
Submit via Quercus

Introduction:

For this assignment, you are responsible for answering the questions below based on the dataset provided.
You will then need to submit a 3-page report in which you present the results of your analysis. In your
report, you should use visual forms to present your results. How you decide to present your results (i.e.,
with tables/plots/etc.) is up to you, but your choice should make the results of your analysis clear and
obvious. In your report, you will need to explain what you have used to arrive at the answer to the research
question and why it was appropriate for the data/question. You must interpret your final results in the
context of the dataset for your problem.

Background:

Data science, analytics, AI, big data are becoming widely used in many fields, which leads to the ever-
increasing demand for data analysts, data scientists, ML engineers, managers of analytics, and other data
professionals. As a result, data science education has become a hot topic for educators and entrepreneurs.
In this assignment, you will need to design a course curriculum for a new “Master of Business and
Management in Data Science and Artificial Intelligence” program at University of Toronto with a focus not
only on technical but also on business and soft skills. Your curriculum would need to contain optimal
courses (and topics covered in each course) for students to obtain necessary technical and business skills to
pursue a successful career as data scientist, analytics and data manager, data analyst, business analyst, AI
system designer, etc. You are required to extract skills that are in demand in the job market from job
vacancies posted on Indeed.com web-portal and apply clustering algorithms to group/segment skills into
courses.

You are provided with a sample Python code to web-scrape job postings from Indeed.com web-portal, that
you would need to modify for your assignment. You can decide on the geographical locations of the job
postings (e.g., Canada, USA, Canada and USA) and job roles (e.g., “data scientist”, “data analyst”,
“manager of analytics”, “director of analytics”) of the posting that you will be web-scraping, but your
dataset should contain at least 1000 unique job postings. You can use a combination of locations and job
titles as search parameters in developing your dataset. You are also provided with a complete dataset of
over 1000 web-scraped job postings from Indeed that you may use if you are unable to complete the web-
scraping; however, you will not receive marks for this portion of the assignment.

Experiment with different Natural Language Processing (NLP) algorithms to extract skills (features) from
the web-scraped job postings. You may manually define your own list of keywords/key-phrases (N-grams)
that represent skills, e.g., “Python”, “R”, “deep learning”, “problem solving”, “communications”,
2/8 MIE 1624 Introduction to Data Science, Analytics and AI – Assignment 3

“teamwork”, or use pre-trained NLP algorithms that automatically extract skills/features from your dataset.
You will need to use OpenAI's ChatGPT API in order to have a list of skills automatically generated, as
well as an interpretation of your final results. If you do not wish to use OpenAI’s API you are free to use
any major LLM that offers an API (i.e., Claude or Gemini API) or run a model locally, like qwen3, using
Ollama.

Finally, you will need to use skills extracted from job postings as features and run two clustering algorithms
to create clusters of skills that can be interpreted as courses. The first clustering algorithm that you are
required to use is the hierarchical clustering algorithm with a single input feature (a distance matrix
which represents the distance between each pair of skills). The idea is that if a pair of skills is found together
in many job postings, those two skills would be required together on the job, and it makes sense to teach
those skills (topics) together within the same course (cluster). Using this idea, you will need to define your
own distance measure, create a dendrogram (see slides 43-44 of “Lecture 8 – Advanced Machine Learning”
for an example), and interpret each cluster as a course. For the second clustering algorithm, you can choose
between k-means and DBSCAN. You will be required to use at least 10 features as inputs for your
second clustering algorithm.

Based on your first and second clustering analysis separately, create a sequence of 8-12 courses. For each
course, include 3 or more topics (based on skills) that should be taught in each course. You may find
that you need to manually adjust your clusters to form 8-12 clusters of 3 or more skills/topics, this is fine,
but you must explain your reasoning for the adjustments you choose to make. Please list your courses in a
logical order, i.e., a course that requires another course as a pre-requisite should be listed after the pre-
requisite course. You can use your own judgement for deciding about a logical sequence of courses or try
to interpret your clustering results for that. For visualizing your course curriculum, feel free to use Python
or any other software, such as Tableau and Power BI. As a bonus, you are asked to develop another course
curriculum by creating text embeddings of your dataset using embeddings and clustering the embeddings
using a model of your choice to form another course curriculum, compare your developed curriculum to an
existing curriculum, or update the web-scraping code we provided you.


Learning objectives:

1. Understand how to clean and prepare data for machine learning, including transforming
unstructured web-scraped data into structured data for analysis. Convert categorical features into
numerical features and perform data standardization/normalization, if necessary, prior to modeling.
2. Understand how to apply unsupervised machine learning algorithms (clustering) to the task of
grouping similar items.
3. Interpret your modeling results and visualize those.
4. Improve on skills and competencies required to collate and present domain-specific, evidence-
based insights.


3/8 MIE 1624 Introduction to Data Science, Analytics and AI – Assignment 3

Questions:

The following sections should be included, but the order does not need to be followed. The discussion for
each section is included in that section’s marks.

1. [1 pt] Data collection and cleaning:
a) Adapt the provided web-scraping code:
i. Decide on geographical location and job role/title.
ii. Scrape at least 1000 unique Indeed job postings. You are encouraged to scrape
more that 1000 as more postings will generate more insightful data and better
clustering results.
Note: you have been provided a dataset of 1000 unique postings; if you choose to use the given
dataset, you will forfeit the 1 mark for this step. You are allowed to aggregate the supplemental
dataset with your clustering results to have a larger total dataset but you still must scrape at
least 1000 of your own postings for full marks.

2. [3 pts] Data processing, feature engineering, and visualization:
a) Engineer features for clustering analysis and visualization:
i. Define skills by combining a list of skills you come up with from your own
knowledge/research with skills generated by ChatGPT API (or API of your
choice). You are responsible for attaining your own API keys (these should be
removed from your final submission).
ii. Extract skills using N-grams or pre-trained NLP algorithms.

b) Visualize key information:
Generate at least six visual depictions of your data and engineered features. At
least half of your visualizations must depict engineered features (e.g., education
level required vs salary, average salary by skill, co-occurrence patterns, whether
the skill appears more in junior vs. senior postings, geographic concentration,
etc.) rather than raw data alone.

3. [2 pts] Hierarchical clustering implementation:
a) Implement hierarchical clustering algorithm:
i. Generate a distance matrix to describe the relationship between skills.
ii. Perform hierarchical clustering of skills.
iii. Generate a dendrogram and interpret it to develop a course curriculum (8-
12 courses with at least 3 skills/topics covered in each). You may need to make
manual adjustments to your clusters that differ from what you get from your
dendrogram; reasoning for any adjustments must be explained. Repeating skills
across separate courses should be avoided.

b) Quantify the quality of your clustering results by calculating silhouette scores or an
alternative measure of your choice.
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4. [4 pts] K-means or DBSCAN clustering implementation:
a) Implement k-means clustering algorithm or DBSCAN clustering algorithm:
i. Engineer 10 or more unique features to describe each skill for clustering (e.g., skill
frequency, average salary for skill, whether the skill appears more in junior vs.
senior postings, geographic concentration, etc.). You will be penalized for overly
simplistic or redundant features.
b) Include visualization of elbow method used to determine the optimal k number of clusters
for k-means clustering or eps value if using DBSCAN clustering.
c) Develop a course curriculum based on clustering results (8-12 courses with 3 or more
skills/topics covered in each). You may need to make manual adjustments to your
clusters that differ from the optimal k value; reasoning for any adjustments must be
explained. Repeating skills across separate courses should be avoided.
d) Generate a labeled scatterplot from k-means clustering algorithm (with optimal k number
of clusters determined by elbow method) or DBSCAN clustering algorithm:
i. To generate a scatterplot you will first need to perform dimensionality reduction
via the method of your choice (e.g., t-SNE, PCA).
e) Quantify the quality of your clustering results by calculating silhouette scores or an
alternative measure of your choice.

5. [1 pt] Combine results from Sections 3 and 4:
Develop an ensemble method to combine your clustering results from the hierarchical and k-means
or DBSCAN algorithms. Examples of ensemble approaches include voting between clustering
results, creating a consensus curriculum, or using the results of one method to refine those of
another. When combining clustering results, document your methodology and justify why your
ensemble approach produces a better curriculum than either method alone.

6. [2 pts] Interpretation of results using LLM API:
a) Decide on a final course curriculum and feed it into your LLM API to expand on your
clustering results:
i. Have the model write a short description of your course curriculum that should
entice potential students to enroll in this program.
ii. Use the model to validate your course curriculum against industry standards.
Prompt the AI to analyze whether your proposed curriculum covers essential data
science skills and identify potential gaps in your course offerings.
iii. Write a third prompt of your choice that adds to your overall analysis/project goal.

7. [2 pts] Discussion and final course curriculum:
Discuss your results. Present and justify your final course curriculum. You may select curriculum
from Section 3 (hierarchical clustering algorithm), from Section 4 (k-means or DBSCAN), or from
Section 5 (ensembled clustering results) as your final course curriculum. As part of your discussion,
reference the provided DataCamp State of Data & AI Literacy 2026 report to evaluate whether your
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curriculum addresses the key skill gaps and priorities identified by industry leaders. For example,
are there gaps your curriculum does not address?

8. [+1 bonus pt] Text embeddings to describe clustering results:
Create text embeddings of the job descriptions using an embedding model of your choice (e.g.,
OpenAI embeddings, Word2Vec, GloVe, or a sentence transformer). Use the embeddings to cluster
skills (not job postings) and develop an additional course curriculum. Compare this curriculum to
your results from Sections 3 and 4. What does the embedding-based approach capture that co-
occurrence and feature engineering do not?
Or compare your curriculum against an existing program:
Select an existing Master's program in data science or AI (e.g., UofT's MScAC, Queen's MMAI,
or another program of your choice). Extract the skills and topics covered by that program's
curriculum by reviewing publicly available course descriptions. Map those skills to your extracted
skill list and compute a quantitative coverage metric (e.g., what percentage of the top 50 most in-
demand skills from your analysis are covered by the existing program vs. your proposed
curriculum). Then use your LLM to perform a structured gap analysis comparing the two curricula,
and critically evaluate its output against your own clustering results. Discuss where your data-
driven curriculum improves upon the existing program and where the existing program covers areas
your clustering may have missed.
Or improve the given web-scraping code:
Improve the provided web-scraping code or develop your own improved code. Some ideas for
improvements are:
i. Increase the number of sources used (e.g., scrape job postings from SimplyHired, Monster,
LinkedIn, Google Jobs, ZipRecruiter, or any other suitable job aggregation website).
ii. Improve the speed, reliability, number of data fields extracted, or overall usability of the
code.

Note: your max assignment mark cannot exceed 15 pts, including bonus.

Submission:

1) Produce an IPython Notebook (.ipynb file) detailing the analysis you performed to answer the questions
based on your data set. Your .csv file containing the results of your webscraping must also be submitted.
Your webscraping code does not need to be submitted unless you’ve made changes to the code beyond your
“location” and “job title” parameters.

2) Produce a 3-page report explaining your response to each question for your data set and detailing the
analysis you performed. When writing the report, make sure to explain for each step, what you are doing,
why it is important, and the pros and cons of that approach. You may choose to include an appendix for
your figures, LLM API outputs, and bonus question material. To receive full marks, all figures
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(including elbow curve) must be contained in the report, be labeled, have figure captions, and be
large enough that they can be easily read and interpreted. LLM API outputs must also be contained
in the report. Your report should include a detailed description of your engineered features and how they
were calculated, clustering algorithm implementation and results, any manual adjustments you made to
your clustering results, interpretation of elbow curve, etc.

Tools:
● Software:
○ Python version 3.9+ is required for this assignment. Make sure that your Jupyter notebook
runs on Google Colab (https://colab.research.google.com) portal. All libraries are allowed
but here is a list of the major libraries you might consider: Numpy, Scipy, Sklearn,
Matplotlib, Pandas, NLTK, OpenAI.
○ No other tool or software besides Python and its component libraries can be used to collect
your data and touch the data files. For instance, using Microsoft Excel to clean the data is
not allowed. Please dump your web-scraping results into a file, submit it with the
assignment, and comment your web-scraping code in the notebook.
○ Upload the required data file to your notebook on Google Colab – for example,
from google.colab import files
uploaded = files.upload()
○ You are allowed to use any software for visualizing your course curricula (Tableau,
Power BI), but you should use Python for everything else.

● Required data files to be submitted:
○ webscraping_results_assignment3.csv: file to be submitted with the assignment. You do
not need to submit this if you are using the given dataset.
○ The notebook will be run using the local version of this data file. Do not save anything to
file within the notebook and read it back.

● Auxiliary files:
○ Assignment_3_Template.ipynb: this code will help you get started with NLP feature
extraction, OpenAI API implementation, and clustering.
○ Supplemental_webscraping_results.csv: Web-scraping results of over 1000 job
postings.
○ Indeed_job_vacancies.csv: Sample Indeed dataset with extracted skills.
○ Indeed_webscraping_notebook.ipynb: Jupyter notebook used to web-scrape job
postings from Indeed web-portal (runs Indeed_scrape_camoufox.py).
○ Indeed_scraper_camoufox.py: Python file which contains the functions for bypassing
cloudflare and scraping jobs from indeed.com.

7/8 MIE 1624 Introduction to Data Science, Analytics and AI – Assignment 3

What to submit:

1. Submit via Quercus a Jupyter (IPython) notebook containing your implementation and motivation
for all the steps of the analysis with the following naming convention:
lastname_studentnumber_assignment3.ipynb
Make sure that you comment your code appropriately and describe each step in sufficient detail.
Respect the above convention when naming your file, making sure that all letters are lowercase and
underscores are used as shown. A program that cannot be evaluated because it varies from
specifications will receive zero marks.
2. Before submitting your Jupyter notebook please remove your personal API key.
3. Submit a report in PDF (up to 3 pages) including the findings from your analysis. Use the following
naming conventions lastname_studentnumber_assignment3.pdf.
4. If you choose to complete the bonus mark by doing your own web-scraping please submit the
following
a. Submit via Quercus a csv file with your web-scraping results with the following naming
convention: webscraping_results_assignment3.csv
b. Submit via Quercus a Jupyter notebook of your web-scraping code with the following
naming convention: lastname_studentnumber_webscraping.ipynb

Late submissions will receive a standard penalty:

● up to one hour late - no penalty
● one day late - 15% penalty
● two days late - 30% penalty
● three days late - 45% penalty
● more than three days late - 0 mark

Other requirements and tips:

1. A large portion of marks are allocated to analysis and justification. Full marks will not be given for
the code alone.
2. Output must be shown and readable in the notebook. The only file that can be read into the notebook
is the file webscraping_results_assignment3.csv with your web-scraping results.
3. Ensure the code runs in full before submitting. Open the code in Google Colab and navigate to
Runtime -> Restart runtime and Run all Cells. Ensure that there are no errors.
4. You have a lot of freedom with how you want to approach each step and with whatever library or
function you want to use. As open-ended as the problem seems, the emphasis of the assignment is
for you to be able to explain the reasoning behind every step.
5. If you choose to complete the web-scraping bonus mark, be aware that web-scraping can sometimes
have spontaneous issue due to internet firewalls and other uncontrollable factors. To avoid
complications, it is suggested that you try to complete this step early on.

8/8 MIE 1624 Introduction to Data Science, Analytics and AI – Assignment 3

Policy for using LLMs:

You may complete any part of this assignment (including both code and report text generation) using an
LLM such as ChatGPT, Claude, or Gemini. Please specify how the LLM was used. For example, if
ChatGPT was used for code generation, structuring, idea generation, or troubleshooting, that should be
stated within the report. Additionally, if a LLM is used to generate large sections of code/ the writing of
your report, you can include examples of the prompts used within the appendix of the report as well as
comment on how you validated the LLM responses. There is no requirement that you implement exactly
what the LLM produced, and if you modified a response, you can comment on why/ how you changed it.
If you use ChatGPT, you could also provide a public link to your chat.


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