Data Analysis: Statistics in Action

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6 sessions

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About

Ever wondered how data scientists turn raw numbers into real-world decisions? In this 3-week hands-on series, we'll break down core statistics concepts — from hypothesis testing to A/B testing — and bring them to life through Python simulations. No prior stats experience needed; we'll build your intuition from the ground up before diving into code.

Tutored by

Changhoon K 🇰🇷

Certified in 1 topic

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Hi! I’m a high school student who is passionate about math and computer science. I enjoy problem-solving, coding, and exploring how algorithms can be applied to real-world challenges. I’m joining Schoolhouse to share my knowledge, help other students build confidence in these subjects, and learn from the global community along the way. My goal is to make math and computer science more approachable and enjoyable for everyone.

Sessions

Upcoming

All

✋ ATTENDANCE POLICY

Learners are expected to attend at least 5 out of 6 sessions. Each session builds on the previous one, so consistent attendance is crucial. If you must miss a session, please notify in advance. Missing more than 2 sessions without prior notice may result in withdrawal from the series. Active participation during simulations is required — simply joining and staying idle does not count as attendance.

SESSION 1

19

Mar

SESSION 1

Python

Thu 3:15 PM - 3:45 PM UTCMar 19, 3:15 PM - 3:45 PM UTC

An introduction to hypothesis testing — the backbone of statistical reasoning. What does it really mean to "prove" something with data? We'll unpack the logic behind null and alternative hypotheses, p-values, and significance levels in plain language, with intuitive examples that make the abstract feel concrete.

SESSION 2

20

Mar

SESSION 2

Python

Fri 3:15 PM - 3:45 PM UTCMar 20, 3:15 PM - 3:45 PM UTC

Now that we speak the language, let's make the computer speak it too. We'll translate everything from Session 1 into working Python simulations — running real hypothesis tests, visualizing distributions, and seeing firsthand how sample size and variance shape our conclusions.

SESSION 3

26

Mar

SESSION 3

Python

Thu 3:15 PM - 3:45 PM UTCMar 26, 3:15 PM - 3:45 PM UTC

What happens when we need to compare more than just two things? Enter Chi-Square and ANOVA — two powerful tools that let us ask bigger questions. We'll break down when and why to use each, using relatable real-world scenarios that make these intimidating names feel approachable.
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SESSION 4

27

Mar

SESSION 4

Python

Fri 3:15 PM - 3:45 PM UTCMar 27, 3:15 PM - 3:45 PM UTC

Time to get our hands dirty again. We'll build Python simulations for both Chi-Square and ANOVA tests, interpret the outputs, and learn how to spot when results are meaningful — and when they're just noise dressed up as insight.

SESSION 5

2

Apr

SESSION 5

Python

Thu 3:15 PM - 3:45 PM UTCApr 2, 3:15 PM - 3:45 PM UTC

From tech startups to global corporations, A/B testing drives some of the most important decisions in business. We'll explore the methodology, the pitfalls, and the surprisingly elegant statistics behind deciding whether Version A truly beats Version B.

SESSION 6

3

Apr

SESSION 6

Python

Fri 3:15 PM - 3:45 PM UTCApr 3, 3:15 PM - 3:45 PM UTC

The grand finale. We'll tackle real-world A/B testing case studies, dissect what went right (and wrong), and solve problems end-to-end in Python. By the end of this session, you won't just understand statistics — you'll wield it.
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If you would like to do more case studies with a/b test, you're welcome to ask me for that.

Public Discussion

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Mar 19 - Apr 3

3 weeks

30 mins

/ session

Next session on March 19, 2026

SCHEDULE

Thursdays

3:15PM

Fridays

3:15PM