Teacher: Jesse Ramirez | Subject: Data Science | Period(s): 4 and 6 | Week: Sept. 20 – 24 |
Essential Question(s): How can univariate data be described and visualized? How can you tell a story with univariate data? | |||
Content Standard(s): Interpret and compare data distributions using center (median, mean) and spread (interquartile range, standard deviation) through the use of technology. Test propositions or conjectures with specific examples. | |||
Technology Utilized: Projector, Chromebooks, CODAP, and Google Slides, Jamboard, Sheets, and Colab. | |||
Objective: Students will discuss measures of center, spread, and shape in univariate data. Students will explore the ACS data process. | Objective: Students will distinguish between numerical and categorical data. Students explore and ask questions of data using CODAP. | Objective: Students will discuss the different components of a box plot. Students will use univariate data in Google Sheets to tell a story. | Objective: Students will communicate their results by sharing their reasoning, conjecture, summary statistics, and story of their variable. | Objective: Students will be introduced to Google Colab and use it to create data visualizations for the story of their variable. |
Vocabulary: Univariate data, median, mean, mode, range, skewed, and symmetrical. | Vocabulary: Numerical and categorical data, variables, dataset, and histogram. | Vocabulary: Box plot, quartile, minimum, maximum, variability, and conjecture. | Vocabulary: Data key, predictive statement, summary statistic, and hypothesize. | Vocabulary: Python, Google Colab, distribution, and Not a Number (NaN). |
Activities/Strategies: Data Talk: Lifespan of a Variety of Mammals
Group Exploration: What is the American Community Survey (ACS)? (Handout 2 - view only) Class Debrief: Findings, Questions, and Ethics | Activities/Strategies: Class Discussion:
Group Exploration: Asking Questions of Data Class Jamboard: Questions, Interests, and Reflections Discussion and Group Exploration: Answering Questions Using CODAP | Activities/Strategies: Data Talk: Distribution of Number of Weeks #1 Songs Spent on the Billboard Hot 100, by year Class Discussion: Box Plot Group Exploration: Exploring and Asking Questions of the Data (Handout 3 - view only) | Activities/Strategies: Group Exploration: Telling Stories with Univariate Data (Handout 4 - view only) Group Share: Group Presentation Exchange (Handout 5 - view only) Maths Journal: Reflection on Group Presentations | Activities/Strategies: Weekly Response: Data Science Process Reflection Class Discussion: Google Colab Introduction Class Debrief: Adding Visuals and Outliers |
Homework: Maths Journal: What is univariate data and how can it be described? | Homework: Maths Journal: How does numerical data differ from categorical data? | Homework: Maths Journal: What are the different components of a box plot? | Homework: Maths Journal: How can you analyze data using Google Sheets? | Homework: Maths Journal: What insights did the data visuals add to your story? |
Teacher: Jesse Ramirez | Subject: Data Science | Period(s): 4 and 6 | Week: Sept. 27 – Oct. 1 |
Essential Question(s): How can univariate data be described and visualized? How can you compare data distributions? | |||
Content Standard(s): Interpret and compare data distributions using center (median, mean) and spread (interquartile range, standard deviation) through the use of technology. Test propositions or conjectures with specific examples. | |||
Technology Utilized: Projector, Chromebooks, CODAP, and Google Slides, Forms, and Sheets. | |||
Objective: Students will consider what questions to ask of their community and design a survey to collect data using Google Forms. | Objective: Students will discuss limitations of summary statistics. Students will create a histogram that represents weights usage. | Objective: Students will be introduced to standard deviation and explore attributes of normal distributions. | Objective: Students will investigate the community dataset, identify any irregularities, and discuss how to perform required cleaning. | Objective: Students will explore the community dataset in CODAP. Students will compare the community dataset and a state dataset. |
Vocabulary: Survey, subset, biased results, and ramifications. | Vocabulary: Anscombe’s quartet and shapes of distributions. | Vocabulary: Standard deviation and normal distribution. | Vocabulary: Cleaning data, participant, header, and outlier. | Vocabulary: Comma-separated values (CSV) and distributions. |
Activities/Strategies: Class Launch: What questions should we ask of our community? Telling Stories with Univariate Data: Visuals (Handout 6 - view only) Class Discussion: Designing a Survey Collecting Data: What approaches should we take in gathering our data? | Activities/Strategies: Data Talk: Anscombe’s Quartet Class Discussion: Limitations of Summary Statistics and Data Ethics Data Talk: Weights Usage Group Exploration: Making a Histogram of the Weights Visual | Activities/Strategies: Class Discussion: Attributes of Normal Distributions Class Investigation: Snapshots of Summer Temperatures and Data Distributions Group Exploration: Playing with Qunicunx | Activities/Strategies: Class Launch: Data Science Process Visual Group Exploration: Investigating the Data Class Discussion: How can we clean the data? Class Debrief: Data Sensibility Check | Activities/Strategies: Weekly Response: Growth Mindset Reflection Group Exploration: Initial Look at Data in CODAP Class Discussion: Comparing the Community and State Data Group Exploration: Introduction to Comparing Datasets |
Homework: Data Gathering: Students collect data from people in their local community. | Homework: Maths Journal: What are some limitations of summary statistics? | Homework: Maths Journal: What are some attributes of normal distributions? | Homework: Maths Journal: How can you clean a dataset using Google Sheets? | Homework: Maths Journal: How can you compare two data distributions? |
Teacher: Jesse Ramirez | Subject: Data Science | Period(s): 4 and 6 | Week: Oct. 4 – 8 |
Essential Question(s): How can univariate data be described and visualized? How can you compare data distributions? | |||
Content Standard(s): Interpret and compare data distributions using center (median, mean) and spread (interquartile range, standard deviation) through the use of technology. Test propositions or conjectures with specific examples. | |||
Technology Utilized: Projector, Chromebooks, and Google Slides, Sheets, and Colab. | |||
Objective: Students will discuss what is sampling and the pros and cons of using samples. Students will use Google Colab to compare datasets. | Objective: Students will present their comparison findings in groups. Students will reflect on the data science process in writing. | Objective: Students will translate their findings into a letter to their senator. | Objective: | Objective: |
Vocabulary: Sampling, random sample, and representative sample. | Vocabulary: Compare, contrast, and the data science process. | Vocabulary: Justify recommendations, and the data science process | Vocabulary: | Vocabulary: |
Activities/Strategies: Class Discussion: What is Sampling? Random and Representative Samples Group Exploration: Comparing Datasets (Handout 7 - view only) Class Debrief: Questions on the Final Product | Activities/Strategies: Group Share: Group Presentation Exchange Class Discussion: Interesting Take-Aways Individual Reflection: What have you learned about the data science process? | Activities/Strategies: Class Discussion: Communicating Your Recommendations (Handout 8 - view only) | Activities/Strategies: | Activities/Strategies: |
Homework: Maths Journal: What is a sample? What are different versions of sampling? | Homework: Maths Journal: How can univariate data be described and visualized? | Homework: | Homework: | Homework: |