Session 2 — Playing with Data

Duration: 75 min · Format: live online · Ages: 12–15

Session goal: by the end, students can store many values in a list, loop through them, and compute a total, an average, and the maximum.

Before class — prep (5 min)

• Open Google Colab → New notebook, ready to screen-share. You'll type the list and loop live.
• Have the diagram below ready to share on screen (a list and a loop building a total), and the DataFrame diagram for fast finishers.
• Have students reopen last session's Colab notebook (or open a fresh one) so they can code along.

Agenda

0:00 · Hook (5 min)

Ask the class and take a few answers (chat or unmute):

• "Last session you stored one value in a variable. What if you had 500 test scores — would you make score1, score2 … all the way to score500?"

Let them react, then land it: data science means handling thousands of values at once. Today they learn the two tools that make that possible — a list holds all the values in one place, and a loop processes them in just a few lines.

0:05 · Teach — A list holds many values (13 min)

Explain: a list stores many values in order. Each value has a position called an index, and indexes start at 0, not 1.

Share this diagram and trace the loop visiting each number to build a total:

Type/run this together in Colab:

scores = [12, 7, 25, 9]

print(scores[0])      # 12  (first item)
print(len(scores))    # 4   (how many)

Ask: "Why does scores[0] give 12 and not 7?" (Answer: lists start counting at 0, so index 0 is the first item.)

⚠ Watch for the off-by-one trap: students expect scores[1] to be the first item. It's actually the second. Counting from 0 is the single most common list mistake — call it out early.

0:18 · Teach — A loop repeats for every item (15 min)

Explain: a for-loop runs the same steps for each item — whether the list has 4 numbers or 4 million. Point out the indentation: the indented line runs once per item.

Type/run this together in Colab:

scores = [12, 7, 25, 9]
total = 0

for s in scores:
    total = total + s     # add each score to the total

print("Total:", total)          # 53
print("Average:", total / len(scores))   # 13.25
print("Highest:", max(scores))  # 25

Walk through one pass of the loop out loud: total starts at 0, then becomes 12, then 19, then 44, then 53.

Ask: "What would happen if we put print("Total:", total) inside the loop, indented?" (Answer: it would print the running total four times, not once.)

⚠ Watch for: Python has handy shortcuts — sum(scores), max(scores), min(scores) — and students may reach for them straight away. That's fine, but make sure they can also explain how the loop builds the total underneath. That's what makes them a real coder.

0:33 · Activity — Analyse your own data (25 min)

Have students open Google Colab and analyse a real list from their own life. Screen-share your notebook as a model.

Type/run this together in Colab:

sleep = [8, 7, 6, 9, 7, 10, 8]

Then have them: 1. Use a loop (or sum) to print the total and the average. 2. Print the most and least with max() and min(). 3. Bonus: count how many nights they slept 8 or more hours using a loop and an if.

Circulate for (or watch the chat for) the classic "biggest" bug. Share this broken code and ask why it only ever prints 9:

scores = [12, 7, 25, 9]
for s in scores:
    biggest = s
print(biggest)

Ask: "Why is biggest always the last number?" Then fix it live: biggest is overwritten every loop, so it ends on the last item. To find the real biggest, start biggest = scores[0], then inside the loop if s > biggest: biggest = s. (Or just use max(scores).)

0:58 · Check for understanding (10 min)

Ask these aloud or drop them in the chat. Answer key (for you):

1. What is scores[0] in scores = [12, 7, 25, 9]? → 12 — lists start counting at 0, so index 0 is the first item.
2. What does a for-loop do? → Runs the same steps for every item in a list, automatically.
3. How do you get the average of a list? → sum(list) / len(list) — the total divided by how many items.

1:08 · Wrap-up + homework (7 min)

• Ask one student to explain in one sentence why a loop beats writing score1 … score500.
• Homework — Real numbers from your life: collect a real list of numbers (steps per day, minutes of reading, goals scored). In Colab, print the total, average, highest, and lowest — plus one sentence about what you notice. Bring it to Session 3.

Teaching notes

• Correct this misconception: "the first item is index 1." In Python it's index 0 — this trips up almost everyone at first.
• Common coding errors students hit: forgetting to indent the line inside the for loop (or indenting inconsistently); starting total at nothing instead of 0; overwriting a variable inside the loop instead of comparing (the "biggest" bug above); dividing by the wrong count.
• Fast finishers (extension): it's time for real data. Pandas turns a data file into a DataFrame — a table you can code with. Share this diagram and have them run the code:

```python import pandas as pd

# load a real, public dataset (thousands of rows!) url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/penguins.csv" df = pd.read_csv(url)

print(df.shape) # (rows, columns) df.head() # peek at the first 5 rows print(df["body_mass_g"].mean()) # average penguin mass ```

Explain that real data has gaps — df = df.dropna() removes empty rows, then check df.shape again. Challenge them to print the average of another column, and the count of each species with df["species"].value_counts(). - Low-tech fallback: if devices can't run Colab, work the scores = [12, 7, 25, 9] loop by hand on the shared screen — write the value of total after each pass — then have students trace their own sleep list on paper before typing it up later.

Vocabulary

Resources

• Google Colab — run your code.
• W3Schools — Python Loops — clear examples.
• Kaggle — Pandas (free course) — the next level for fast finishers.
• Our World in Data — free real datasets to download.

Next session

Session 3 — Your First Prediction: students build a real model that learns a pattern from data and predicts a new value with scikit-learn.