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
- Data types
* 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
|Week 1||Lessons 1.1 - 1.5|
|Week 2||Lessons 1.6 - 1.8|
- 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
- Reduce or eliminate some Challenges
- Reduce or eliminate the "if time permits" parts of lessons
- 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