« High School Courses
This course is included with our Coding/STEAM Curriculum - High School Plan

A high school course designed to introduce students to the foundations of data analysis using Python.

  • Grades 9+
  • Advanced
  • Web


Explore data in areas such as science, sports, politics, climate change, and much more!

Data is everywhere around us, transforming our world. The applications are countless: the world of Moneyball and sports statistics, archeologists discovering hidden civilizations with LiDAR data, climate scientists tracking temperature change and polar ice melt—data science and our data-driven world are bringing exciting insights, discoveries, and analysis to bear in almost every field imaginable.

In this introductory course, students will learn how to interrogate a data set, just like a data pro, then make their own conclusions. Students will:

  • Learn how to make use of Python, the most popular language for data science
  • Understand different types of data (categorical vs. numerical) as well as statistical analysis
  • Model how things work, using data! Make a prediction, then test the results
  • Create their own, original graphs and visualizations
  • Learn the strengths and weaknesses of different visualization types: And how to filter, clean, and interpret data
  • Apply basic statistical concepts like mean, median, mode, and standard deviation
  • Learn the industry-standard Data Science libraries: numpy, pandas, and matplotlib

Whether they're curious about data journalism, science, or just want to tell a story with data, this course is a fast and fun way to get started. Learn more about becoming a Data Scientist, sometimes called "America's Hottest Job," and explore a variety of real-world, hands-on labs.

Previous Python programming experience is recommended but not strictly necessary (complete Tynker's Python 101, or Tynker's AP CS Principles). The only prerequisite is Algebra 1.


  • Python basics
  • Loops
  • Functions
  • Expressions
  • Operators
  • Data types
  • Variables
  • Lists
  • matplotlib
  • numpy
  • pandas

What Students Learn

  • Analyze and Categorize Data
  • Visualize Data with numpy, matplotlib, and Python
  • Manipulate Datasets Using pandas
  • Use Statistics
  • Create Complex Charts and Figures
  • Draw Conclusions

Technical Requirements

* Online courses require a modern desktop computer, laptop computer, Chromebook, or Netbook with Internet access and a Chrome (29+), Firefox (30+), Safari (7+), or Edge (20+) browser. No downloads required.

Data Science 1 Lesson Plan

Unit 1: Introduction

In this unit, students will explore common data tasks which include finding the average, inspecting data, and more. They will also learn about different data types such as numerical and categorical data. Additionally, they'll explore how bias in an analyst or in data can impact results. Finally, they'll take on the role of a data analyst as they inspect datasets about baseball stats, Asian elephant ages, and city populations!

Suggested Unit Pacing Guide

WeekUnit Pacing
Week 1Lessons 1.1 - 1.5
Week 2Lessons 1.6 - 1.8

This pacing guide assumes a school calendar that will have:
  • One or two days at the start of the school year for school and classroom orientation activities
  • A few days when class time will be shorten due to unexpected interruptions such as fire drills or assemblies
  • A few days that school does not meet due to weather or teacher professional development
  • Several days that class will not meet due to school-wide standardized testing

If you are falling behind:
  • Reduce or eliminate some Challenges
  • Reduce or eliminate the "if time permits" parts of lessons

If you are getting ahead:
  • Provide students more time on the end-of-Unit Labs and their Final Project
  • Allow students to revise and resubmit their Labs based on your feedback
  • Revisit Challenges and expect students to complete more of the Challenges

Class Presentations

These student-facing slide presentations help educators seamlessly run Tynker lessons in a virtual or physical classroom setting. Each lesson has its own set of slides that introduce the big ideas, suggest unplugged activities, and include a section for each activity module. While running lesson slides, you can switch back and forth between the activity, the slides, answer keys and other lesson materials.
A sample slide presentation is available for your review. Please log in to view all the class presentations available with your plan..
Lesson 1
Unit 1: Introduction
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Lesson 2
Unit 2: Graphs
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Lesson 3
Unit 3: DataFrames
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Lesson 4
Unit 4: Complex Charts
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Lesson 5
Unit 5: Capstone Project
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