diff --git a/02_activities/assignments/assignment_3.md b/02_activities/assignments/assignment_3.md
index 584248b5f..e8bd488bb 100644
--- a/02_activities/assignments/assignment_3.md
+++ b/02_activities/assignments/assignment_3.md
@@ -9,22 +9,96 @@
 - For each visualization, describe and justify: 
     > What software did you use to create your data visualization?
 
+    1. I used Python for Visualization 1, specifically the pandas and matplotlib libraries, to create this data visualization. These tools allow for flexible, programmatic creation of charts, and the process is fully reproducible via the provided script.  
+
+    2. Excel Visualization: https://docs.google.com/spreadsheets/d/19LMgMAZcGp6kbCVO7gvoZC8CvoBJDsxb/edit?usp=sharing&ouid=109009859095318812232&rtpof=true&sd=true
+
+    I used Microsoft Excel to create a horizontal bar chart that visualizes the top contaminants contributing to fish consumption advisories in Ontario.
+
     > Who is your intended audience? 
+
+    1. The intended audience includes policy-makers, environmental analysts, and members of the public concerned with fish consumption safety in Ontario. It's also suitable for students and educators exploring environmental data or health advisories
+
+    2. The intended audience includes members of the general public, health-conscious consumers, and policy-makers interested in environmental health and food safety, especially those in Ontario.
     
     > What information or message are you trying to convey with your visualization? 
+
+    1. This visualization communicates how advisory levels (i.e., consumption recommendations based on contaminant risks) vary by fish species. By focusing on the top 20 most commonly tested species, the visualization helps identify which species are most frequently associated with higher-risk advisories.
+
+    2. The visualization highlights the most common chemical contaminants, such as PCBs, mercury, and dioxins—that lead to fish consumption advisories. The goal is to raise awareness of what substances most frequently pose health risks in Ontario’s lakes and rivers.
     
     > What aspects of design did you consider when making your visualization? How did you apply them? With what elements of your plots? 
+
+    1. Initially I had intended to build an interactive map but it proved to be too difficult for me at this time. 
+
+        Key aspects of design included:
+        - A stacked horizontal bar chart for legibility of long fish species names.
+        - A custom color palette progressing from cool blues to deep purples, - - - highlighting increasing advisory levels while avoiding the overuse of aggressive red hues.
+        - A clean layout with clear labels, sorted species, and a legend for interpretability.
+        - Careful use of proportions (not just raw counts) to show distribution of advisory levels per species.
     
+    2. Key design considerations included:
+        - Clarity: I used a horizontal bar chart for easy label readability.
+        - Color accessibility: I applied a blue-to-indigo gradient to visually represent contaminant frequency without overwhelming color variation.
+        - Minimalism: I removed non-essential elements (e.g., gridlines and borders) to keep the focus on the data.
+        - Typography: Axis labels and titles were adjusted for legibility using bold and larger fonts.
+
+
     > How did you ensure that your data visualizations are reproducible? If the tool you used to make your data visualization is not reproducible, how will this impact your data visualization? 
+
+    1. This piece is fully reproducable. It uses a shared dataset (Guide_to_Eating_Ontario_Fish_Advisory_Database_2024.csv). The script includes all steps from data loading and cleaning to plotting, and dependencies are standard open-source Python libraries. 
+
+    2. The chart is based on a CSV file derived from the 2024 Ontario Fish Advisory dataset. The process involved:
+        - Preprocessing data in Python to extract relevant contaminant counts.
+        - Exporting a clean, summarized CSV that can easily be reloaded in Excel or Sheets.
+        - Anyone with the same dataset and instructions can reproduce the chart. I also used consistent formulas and cell references in Excel, making it transparent and easy to replicate.
     
     > How did you ensure that your data visualization is accessible?  
+
+    1. The use of color gradients was chosen for perceptual clarity, with a conscious effort to avoid red-green combinations that are difficult for people with colorblindness. The use of a horizontal layout helps support screen reader parsing and reduces label overlap, and species names and axis labels are presented in plain language, with no technical jargon.
+
+    2.  - Chose high-contrast color schemes that are friendly to colorblind viewers.
+        - Used text labels and clear titles so that the visualization can be interpreted without color alone.
+        - Avoided relying on hover effects or interactivity, which may not be accessible to all users.
+        - Ensured that fonts were readable and elements were spaced to avoid clutter
     
     > Who are the individuals and communities who might be impacted by your visualization?  
+
+    1. Communities impacted may include:
+        - Anglers and fish consumers in Ontario
+        - Indigenous communities who may rely on fish as a key part of their diet
+        - Environmental justice organizations concerned with pollution exposure
+        - Public health stakeholders who communicate consumption safety
+    
+    2.  - Ontario residents, especially those who fish or eat locally caught fish.
+        - Indigenous communities who rely on traditional fishing practices.
+        - Public health agencies and environmental organizations aiming to educate the public or create policy.
     
     > How did you choose which features of your chosen dataset to include or exclude from your visualization? 
+
+    1. I chose to:
+        - Include the Specname (fish species) and Adv Level (advisory level) columns
+        - Focus on the top 20 species by frequency to keep the plot readable and relevant
+        - Exclude geographic location, contaminant cause, and fish length in this visualization to avoid overcomplication and keep the message focused.
+
+    2. I chose to focus on the “Advisory Cause” column and aggregated counts to show which contaminants are most frequently flagged. I excluded other dimensions like fish species, location, or advisory level to keep the message focused and clear for a general audience.
+
+    
     
     > What ‘underwater labour’ contributed to your final data visualization product?
 
+    1. This visualization involved several invisible steps:
+        - Data cleaning: resolving inconsistent column names and formats
+        - Exploration: assessing distributions, null values, and advisory level breakdowns
+        - Trial and error: testing various chart types, sort orders, and color schemes
+        - Debugging: handling CSV encoding issues, column coercion, and filtering errors
+        - Design iteration: adjusting palette and layout to meet both clarity and accessibility standards
+
+    2.  - Data cleaning and transformation in Python to extract advisory causes.
+        - Manual inspection of column names, value formatting, and encoding issues.
+        - Trial-and-error chart design in Excel to adjust colors, labels, and formatting.
+        - Iterative feedback and troubleshooting visualization techniques across different platforms (Tableau, Python, Excel) to land on a practical and effective approach.
+
 - This assignment is intentionally open-ended - you are free to create static or dynamic data visualizations, maps, or whatever form of data visualization you think best communicates your information to your audience of choice! 
 - Total word count should not exceed **(as a maximum) 1000 words** 
  
diff --git a/02_activities/assignments/fish_advisory_analysis.ipynb b/02_activities/assignments/fish_advisory_analysis.ipynb
new file mode 100644
index 000000000..43e517255
--- /dev/null
+++ b/02_activities/assignments/fish_advisory_analysis.ipynb
@@ -0,0 +1,103 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "id": "eb5daa55",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1100x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 1: Load the CSV locally\n",
+    "# -----------------------------\n",
+    "df = pd.read_csv(\"Guide_to_Eating_Ontario_Fish_Advisory_Database_2024.csv\", encoding=\"latin1\")\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 2: Clean and prep\n",
+    "# -----------------------------\n",
+    "df = df.dropna(subset=[\"Specname\", \"Adv Level\"])\n",
+    "df[\"Adv Level\"] = pd.to_numeric(df[\"Adv Level\"], errors=\"coerce\")\n",
+    "df = df[df[\"Adv Level\"].between(0, 4)]\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 3: Top 20 species\n",
+    "# -----------------------------\n",
+    "top_species = df[\"Specname\"].value_counts().nlargest(20).index\n",
+    "df_top = df[df[\"Specname\"].isin(top_species)]\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 4: Pivot for stacked bar\n",
+    "# -----------------------------\n",
+    "pivot = df_top.pivot_table(index=\"Specname\", columns=\"Adv Level\", aggfunc=\"size\", fill_value=0)\n",
+    "pivot = pivot.reindex(columns=[0, 1, 2, 3, 4], fill_value=0)\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 5: Custom color palette\n",
+    "# -----------------------------\n",
+    "color_map = {\n",
+    "    0: \"#6baed6\",  # light blue\n",
+    "    1: \"#3f88c5\",  # bolder blue\n",
+    "    2: \"#7b6fd0\",  # lavender-violet\n",
+    "    3: \"#9b4dca\",  # purple\n",
+    "    4: \"#6a1b9a\"   # deep plum\n",
+    "}\n",
+    "\n",
+    "# -----------------------------\n",
+    "# Step 6: Plot!\n",
+    "# -----------------------------\n",
+    "ax = pivot.plot(\n",
+    "    kind=\"barh\",\n",
+    "    stacked=True,\n",
+    "    color=[color_map.get(level, \"#cccccc\") for level in pivot.columns],\n",
+    "    figsize=(11, 8)\n",
+    ")\n",
+    "\n",
+    "plt.title(\"Advisory Levels for Top 20 Ontario Fish Species\", fontsize=14)\n",
+    "plt.xlabel(\"Number of Advisories\")\n",
+    "plt.ylabel(\"Fish Species\")\n",
+    "plt.legend(title=\"Advisory Level\", loc=\"lower right\")\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save if needed\n",
+    "# plt.savefig(\"ontario_fish_advisory_levels.png\", dpi=300)\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "dsi_participant",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.19"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}