Course title

Pre-requisite

Course description

Per request of Jennifer Pelton on 12/27; I am submitting 1 of 2 courses (for a Math and Science class) for approval to meet competency requirements.

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In this course students will learn to be data explorers through project-based units. The units will give students opportunities to ask questions of data; through active engagement; developing their understanding of data analysis; sampling; correlation/causation; bias and uncertainty; modeling with data; making and evaluating data-based arguments; and the importance of data in society. At the end of the course; students will have a portfolio of their data science work to showcase their newly developed knowledge and understanding. †

COURSE GOALS:†

This data science course will provide students with opportunities to make sense of complex problems; then through an iterative process of formulation and reformulation to come to a reasoned argument for the choices they will make. All of the Standards of Mathematical Practice (SMP) will be addressed in this course.† The practices that will be drawn upon most frequently are:

?† † † †Make sense of problems and persevere in solving them. (SMP 1)

?† † † Model with mathematics (SMP 4)

?† † † Construct viable arguments and critique the reasoning of others (SMP 3)

?† † † Strategically choose and make use of (mathematical and technological) tools (SMP 5)

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TECHNOLOGY:†

This course is dependent upon the use and application of a variety of technologies. The appropriate and strategic use of these tools will be demonstrated and required throughout the course. The tools required will include CODAP(https://codap.concord.org/) for analyzing and visualizing data; Google Sheets for analyzing and visualizing large amounts of data (on the order of hundreds of data points); the Google Data Commons API (a website wherein students will gather; sort; visualize; and export country data that is freely available to the public;https://www.datacommons.org/); Tableau for analyzing data and creating visuals; and Python through Google Collaboratory; as students learn to use coding with larger data sets.†

COURSE OUTLINE:

• Unit 1 - Data Tells a Story

• Unit 2 - Measures of Center and Spread

• Unit 3 - Bivariate Data: Causality vs. Correlation†

• Unit 4 - Making Decisions with Data

• Unit 5 - Categorical Data and Linear Algebra

• Unit 6 - Modeling with Data and Understanding Bias†

• Unit 7 - Data Predictions†

• Unit 8 - Being a Data Scientist

School country

School state

School city

School / district Address

School zip code

Requested competency code

Date submitted

Approved

Approved competency code

• LINT
• Integrated science

Approved date

Online / Virtual