Lesson 9: Real-World Data Analysis
Putting it all together: load a real dataset, clean it, compute statistics, and visualize it. This is the full data science workflow you'll use in every future project.
Key Concepts
Data Cleaning
Real data is messy: missing values, wrong types, duplicates, outliers. Before analyzing, check: Are there empty cells? Are numbers stored as strings? Remove or fix problems first.
Filtering Data
filtered = [row for row in data if row["score"] > 80]
List comprehensions let you filter rows in one line. You can chain multiple conditions with and/or.
Grouping & Aggregating
Group data by a category, then compute stats per group:
groups = {}
for row in data:
city = row["city"]
groups.setdefault(city, []).append(row["score"])
for city, scores in groups.items():
print(city, sum(scores)/len(scores))
The Full Workflow
1. Load CSV 2. Clean & validate 3. Filter/group 4. Compute stats 5. Build chart 6. Write a sentence summarizing what you found. Step 6 is the most important — data without interpretation is useless.
🔬 Interactive Lab: Data Dashboard
A mini dashboard showing city averages, top scorers, and score distribution side-by-side.
✅ Check Your Understanding
1. What is the first step of the data analysis workflow?
2. In Python, how do you keep only rows where score > 80?
3. Why is writing a conclusion the most important step?