Properly renaming and organizing Assignments.

02_assignments/a1_sampling_and_reproducibility/a1_sampling_and_reproducibility.md

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+# Assignment 1: Sampling and Reproducibility in Python
+
+Read the blog post [Contact tracing can give a biased sample of COVID-19 cases](https://andrewwhitby.com/2020/11/24/contact-tracing-biased/) by Andrew Whitby to understand the context and motivation behind the simulation model we will be examining.
+
+Examine the code in `whitby_covid_tracing.py`. Identify all stages at which sampling is occurring in the model. Describe in words the sampling procedure, referencing the functions used, sample size, sampling frame, any underlying distributions involved, and how these relate to the procedure outlined in the blog post.
+
+Run the Python script file called whitby_covid_tracing.py as is and compare the results to the graphs in the original blog post. Does this code appear to reproduce the graphs from the original blog post?
+
+Modify the number of repetitions in the simulation to 1000 (from the original 50000). Run the script multiple times and observe the outputted graphs. Comment on the reproducibility of the results.
+
+Alter the code so that it is reproducible. Describe the changes you made to the code and how they affected the reproducibility of the script file. The output does not need to match Whitby’s original blogpost/graphs, it just needs to produce the same output when run multiple times
+
+# Author: YOUR NAME
+
+```
+Please write your explanation here...
+
+```
+
+
+## Criteria
+
+|Criteria|Complete|Incomplete|
+|--------|----|----|
+|Altercation of the code|The code changes made, made it reproducible.|The code is still not reproducible.|
+|Description of changes|The author explained the reasonings for the changes made well.|The author did not explain the reasonings for the changes made well.|
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`
+* The branch name for your repo should be: `assignment-1`
+* What to submit for this assignment:
+    * This markdown file (sampling_and_reproducibility.md) should be populated.
+    * The `whitby_covid_tracing.py` should be changed.
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/sampling/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `sampling-and-reproducibility`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-3-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.

02_assignments/a1_sampling_and_reproducibility/whitby_covid_tracing.py

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+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Note: Suppressing FutureWarnings to maintain a clean output. This is specifically to ignore warnings about
+# deprecated features in the libraries we're using (e.g., 'use_inf_as_na' option in Pandas, used by Seaborn),
+# which we currently have no direct control over. This action is taken to ensure that our output remains
+# focused on relevant information, acknowledging that we rely on external library updates to fully resolve
+# these deprecations. Always consider reviewing and removing this suppression after significant library updates.
+import warnings
+warnings.simplefilter(action='ignore', category=FutureWarning)
+
+# Constants representing the parameters of the model
+ATTACK_RATE = 0.10
+TRACE_SUCCESS = 0.20
+SECONDARY_TRACE_THRESHOLD = 2
+
+def simulate_event(m):
+  """
+  Simulates the infection and tracing process for a series of events.
+
+  This function creates a DataFrame representing individuals attending weddings and brunches,
+  infects a subset of them based on the ATTACK_RATE, performs primary and secondary contact tracing,
+  and calculates the proportions of infections and traced cases that are attributed to weddings.
+
+  Parameters:
+  - m: Dummy parameter for iteration purposes.
+
+  Returns:
+  - A tuple containing the proportion of infections and the proportion of traced cases
+    that are attributed to weddings.
+  """
+  # Create DataFrame for people at events with initial infection and traced status
+  events = ['wedding'] * 200 + ['brunch'] * 800
+  ppl = pd.DataFrame({
+      'event': events,
+      'infected': False,
+      'traced': np.nan  # Initially setting traced status as NaN
+  })
+
+  # Explicitly set 'traced' column to nullable boolean type
+  ppl['traced'] = ppl['traced'].astype(pd.BooleanDtype())
+
+  # Infect a random subset of people
+  infected_indices = np.random.choice(ppl.index, size=int(len(ppl) * ATTACK_RATE), replace=False)
+  ppl.loc[infected_indices, 'infected'] = True
+
+  # Primary contact tracing: randomly decide which infected people get traced
+  ppl.loc[ppl['infected'], 'traced'] = np.random.rand(sum(ppl['infected'])) < TRACE_SUCCESS
+
+  # Secondary contact tracing based on event attendance
+  event_trace_counts = ppl[ppl['traced'] == True]['event'].value_counts()
+  events_traced = event_trace_counts[event_trace_counts >= SECONDARY_TRACE_THRESHOLD].index
+  ppl.loc[ppl['event'].isin(events_traced) & ppl['infected'], 'traced'] = True
+
+  # Calculate proportions of infections and traces attributed to each event type
+  ppl['event_type'] = ppl['event'].str[0]  # 'w' for wedding, 'b' for brunch
+  wedding_infections = sum(ppl['infected'] & (ppl['event_type'] == 'w'))
+  brunch_infections = sum(ppl['infected'] & (ppl['event_type'] == 'b'))
+  p_wedding_infections = wedding_infections / (wedding_infections + brunch_infections)
+
+  wedding_traces = sum(ppl['infected'] & ppl['traced'] & (ppl['event_type'] == 'w'))
+  brunch_traces = sum(ppl['infected'] & ppl['traced'] & (ppl['event_type'] == 'b'))
+  p_wedding_traces = wedding_traces / (wedding_traces + brunch_traces)
+
+  return p_wedding_infections, p_wedding_traces
+
+# Set the random seed for reproducibility
+np.random.seed(10)
+
+# Run the simulation 1000 times
+results = [simulate_event(m) for m in range(1000)]
+props_df = pd.DataFrame(results, columns=["Infections", "Traces"])
+
+# Plotting the results
+plt.figure(figsize=(10, 6))
+sns.histplot(props_df['Infections'], color="blue", alpha=0.75, binwidth=0.05, kde=False, label='Infections from Weddings')
+sns.histplot(props_df['Traces'], color="red", alpha=0.75, binwidth=0.05, kde=False, label='Traced to Weddings')
+plt.xlabel("Proportion of cases")
+plt.ylabel("Frequency")
+plt.title("Impact of Contact Tracing on Perceived Infection Sources")
+plt.legend()
+plt.tight_layout()
+plt.show()

02_assignments/a2_questionnaire_design_part_a.md

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+# Assignment 2: Questionnaire Design (Part A)
+
+## Requirements
+Select one of the scenarios below and design a survey to meet the need(s) outlined in the prompt.
+
+1.	In two to three sentences, describe the purpose of your survey
+2.	Describe your target population, sampling frame, sampling units, and overall sampling strategy.
+3.	Write a 5-10 question survey to address your chosen scenario below.
+
+
+### Scenarios
+1.	You work in the Human Resources Department at a large tech company. Over the past few months, the company has been experiencing a high turnover rate across many of its departments, specifically within the entry- and lower-level positions. The company wishes to understand why this turnover is happening, and what changes need to occur to improve employee satisfaction.
+2.	You work for a Canadian national political party during a federal election. Throughout the campaign period, your party has seen relatively high approval ratings, but an opposing party is also polling favorably and may still have a chance to win the election. You are one month away from the election and you want to understand what voters want from your party and its leader in order to maintain your lead and eventually win the election.
+3.	You are a student researcher in the sociology department at the University of Toronto. You are working on a research project that concerns the relationship between music taste and age. This involves both comparisons between different people of different ages and comparisons of the same individual at different ages during their lifetime. You wish to understand to what extent age influences music taste, specifically as it relates to perceptions of popular music. Your results will be written into an academic paper that you hope to publish.
+
+
+# Your Changes
+
+The number of your chosen topic: `#`
+
+Describe the purpose of your survey:
+```
+write your answer here...
+```
+
+Describe your target population, sampling frame, sampling units, and observational units:
+```
+write your answer here...
+```
+
+Your 5-10 question survey:
+```
+1. write your question here...
+2. write your question here...
+3. write your question here...
+4. write your question here...
+5. write your question here...
+6. write your question here... (optional)
+7. write your question here... (optional)
+8. write your question here... (optional)
+9. write your question here... (optional)
+10. write your question here... (optional)
+```
+
+## Rubric
+
+-	All required components are present and complete **Complete / Incomplete**
+-	Choice of sampling strategy is justified and related to survey purpose **Complete / Incomplete**
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`
+* The branch name for your repo should be: `assignment-2`
+* What to submit for this assignment:
+    * This markdown file (questionnaire_design_part_a.md) should be populated and should be the only change in your pull request.
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/sampling/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `questionnaire-a`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-3-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.

02_assignments/a3_questionnaire_design_part_b.md

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+# Assignment 3: Questionnaire Design (Part B)
+
+## Requirements
+1. Comment on the quality of your sample. Do you expect it to be representative of your target population? Why or why not?
+
+    ```
+    write your answer here...
+    ```
+
+2. Given the class content (error, survey quality, ethics) covered since Part A of the assignment, do you wish to make any changes to your original survey? If so, make a copy of your original survey and make any desired changes. Describe the changes and your reasoning. Provide a link here for submission.
+
+    ```
+    write your answer here...
+    ```
+
+3. Could the purpose of your survey have been fulfilled through an observational study? If yes, how? If no, why not?
+
+    ```
+    write your answer here...
+    ```
+
+## Why am I doing this assignment?
+
+This assignment supports learning outcomes 1, 2, 3, and 4:
+	1.	Develop ability to implement simple probability samples.
+	2.	Understand more complicated sampling procedures and the tradeoffs involved.
+	3.	Identify and understand sources of error or inaccuracies in data as a result of sampling strategies.
+	4.	Develop intuition around survey quality.
+
+## Rubric
+
+-	All required components are present and complete **Complete / Incomplete**
+-	Assessment of representativeness is justified based on class content **Complete / Incomplete**
+-	Changes to original survey are justified based on class content and best practices for sampling **Complete / Incomplete**
+
+## Submission Information
+
+🚨 **Please review our [Assignment Submission Guide](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md)** 🚨 for detailed instructions on how to format, branch, and submit your work. Following these guidelines is crucial for your submissions to be evaluated correctly.
+
+### Submission Parameters:
+* Submission Due Date: `HH:MM AM/PM - DD/MM/YYYY`
+* The branch name for your repo should be: `assignment-3`
+* What to submit for this assignment:
+    * This markdown file (questionnaire_design_part_b.md) should be populated and should be the only change in your pull request.
+* What the pull request link should look like for this assignment: `https://github.com/<your_github_username>/sampling/pull/<pr_id>`
+    * Open a private window in your browser. Copy and paste the link to your pull request into the address bar. Make sure you can see your pull request properly. This helps the technical facilitator and learning support staff review your submission easily.
+
+Checklist:
+- [ ] Create a branch called `questionnaire-b`.
+- [ ] Ensure that the repository is public.
+- [ ] Review [the PR description guidelines](https://github.com/UofT-DSI/onboarding/blob/main/onboarding_documents/submissions.md#guidelines-for-pull-request-descriptions) and adhere to them.
+- [ ] Verify that the link is accessible in a private browser window.
+
+If you encounter any difficulties or have questions, please don't hesitate to reach out to our team via our Slack at `#cohort-3-help`. Our Technical Facilitators and Learning Support staff are here to help you navigate any challenges.