โ˜• The Data-Driven Barista

Running your shop with science, not just caffeine.

๐ŸŽฏ Sampling

WHAT Picking 50 customers to represent all 5,000 who visit your shop.

WHY You want to know if people like your new "Lavender Latte," but you can't interview every person in line without causing a traffic jam!

HOW Interviewing every 10th person who walks in, regardless of what they look like.

WHEN Before you commit to buying 50lbs of lavender syrup.

The Coffee Problem: If you only ask people who are already drinking lattes, your sample is biased. You need to ask the black coffee drinkers too!

๐Ÿ” Exploratory Data Analysis (EDA)

WHAT Plotting your sales to see the "shape" of your customers.

WHY To see if your "Average Customer" actually exists.

HOW Creating a histogram of Customer Age vs. Sugar Packets.

WHEN When you notice your sugar inventory is disappearing faster than expected.

The Coffee Problem: Your average customer age is 40, but the graph shows two humps: 20-year-olds (syrup fans) and 60-year-olds (black coffee fans). Nobody at age 40 actually comes to your shop!

๐Ÿ“ Estimation

WHAT Guessing the total monthly milk demand based on just one week of data.

WHY You need to order milk from the farm, but you need a "safety margin" so you don't run out.

HOW Calculate your average daily milk use + a Confidence Interval (the +/- range).

WHEN Every Sunday night before placing the supply order.

The Coffee Problem: Don't just order for your "average" day. Order for the "Average + Margin" so that if a sudden group of tourists arrives, you're safe.

Projected Milk Order

42 Gallons

ยฑ 4 Gallons

๐Ÿงช Testing

WHAT Checking if your "Free Cookie" promotion actually increased profits.

WHY You made more money today, but was it the cookies? Or was it just a rainy day (which makes people buy more coffee)?

HOW Compare "Cookie Days" to "No Cookie Days" using a P-Value.

WHEN Before you give away 1,000 more free cookies.

The Coffee Problem: If your profit only went up by $2, that's just "noise." If it went up by $200, that's a Statistically Significant signal!

Is the profit increase real?

๐Ÿ”ฎ Prediction

WHAT Using the weather forecast to decide how many muffins to bake.

WHY Baking 100 muffins when only 20 people show up is a waste of money.

HOW Building a model where Temp + Day of Week = Estimated Muffin Sales.

WHEN At 5:00 AM every morning when you turn on the oven.

The Coffee Problem: Prediction turns "I hope we sell these" into "I know we need these."

Bake Exactly:

85

Muffins for tomorrow