Introduction to Data Science
Code: CML25
Type
Fundamental
For Grades
6-12
Description
Unlock the power of data with Python in this hands-on intro to Data Science. From decoding messy datasets with Pandas, crunching numbers with NumPy, to building stunning visuals with Matplotlib, you will learn how to turn raw data into real insights by analyzing trends, uncovering patterns, and telling compelling data stories that support smart, evidence-based decisions. Whether you are curious about business, science, or social issues, this course gives you the tools to explore the world through data.
We will begin by building a strong foundation in Python and core data science tools—manually writing code to clean, analyze, and visualize data. You’ll learn how to think like a data scientist: breaking down problems, debugging code, and experimenting with solutions.
Then, we will introduce AI as a collaborator—using tools like ChatGPT to:
- Generate code snippets to save time
- Explain difficult concepts and errors
- Compare different ways to solve the same problem
- Reflect on how human logic and AI thinking differ
With this hybrid approach you will not only master core concepts, but also develop AI literacy—a critical skill in the data-driven future.
Throughout the course, you will build a portfolio of real-world data projects—rom tracking climate change to analyzing earnings across professions, or even investigating your school’s lunch choices. These will serve as evidence of what you can actually do with data. You will walk away with a set of polished, shareable projects that you can include in:
- College applications (especially for STEM, CS, economics, or business majors)
- Internship and summer program submissions
- Competitions, hackathons, and science fairs
- Personal research or club projects
- Conversations with future mentors, recruiters, or admissions officers
Research shows that portfolios help students demonstrate real-world skills, reflect on their growth, and stand out in competitive environments. They're not just a showcase but a launchpad for the future.
Who should take this course?
This course requires approval after registration. The minimum requirements for this course is as follows:
- Programming: Successfully completed CS21F-2: Programming with Python - Part 2 course
- Math: Pre-algebra
Competition background is not required but is a plus: USACO Bronze, AMC 8 or above
Content
The course is composed of 16 lessons:- Lesson 1: Introduction to Data Science - Go from Data to Insights
- Lesson 2: Probability & Distributions - How likely is that?
- Lesson 3: Inferential Statistics - Putting Claims to the Test
- Lesson 4: Correlation - Are they connected?
- Lesson 5: Python - The Friendly Language that Powers Data Science Magic
- Lesson 6 & 7: Numpy - Supercharge Your Calculations with Lightning-Fast Arrays
- Lesson 8 & 9: Pandas - Master Data-Wrangling with Powerful Tables
- Lesson 10 - 14: Matplotlib: Create Stunning Visual Stories from Raw Data
- Lesson 15 & 16: Portfolio Project - From Chaos to Clarity
