Day 14: Data Project - Day 1

Learning Objectives

• DAT-2.A: Describe what information can be extracted from data.
• DAT-2.B: Extract information from data using a program.
• DAT-2.C: Identify the challenges associated with processing data.
• DAT-2.D: Extract information from data using a program.

Essential Questions

• How can we apply data analysis techniques to answer meaningful questions?
• What insights can be gained from analyzing real-world datasets?
• How do we design an effective data analysis process?

Materials Needed

• Project guidelines handout
• Sample datasets (or links to public datasets)
• Data analysis tools (programming environments, spreadsheets, visualization tools)
• Project planning templates
• Computers with necessary software

Vocabulary

• Data analysis
• Research question
• Hypothesis
• Dataset
• Data cleaning
• Exploratory analysis
• Insight
• Visualization

Procedure (50 minutes)

Opening (10 minutes)

1. Introduction to Data Analysis Project (7 minutes)

   • Explain the project goals and expectations:
     • Apply data concepts from the unit to analyze a real dataset
     • Extract meaningful insights and answer research questions
     • Create visualizations to communicate findings
     • Present results to the class
   • Review project requirements and rubric
   • Share timeline (today: planning and exploration, next class: completion and presentation)

2. Project Brainstorming (3 minutes)

   • Students individually brainstorm potential research questions they're interested in exploring
   • Consider what types of data would help answer these questions

Main Activities (30 minutes)

3. Teams Select Datasets and Research Questions (10 minutes)

   • Form teams of 2-3 students
   • Teams review available datasets or propose their own
   • Teams select a dataset and develop 2-3 research questions to explore
   • Questions should be specific, answerable with the available data, and interesting
   • Teacher approves dataset and research questions

4. Initial Data Exploration and Planning (10 minutes)

   • Teams begin exploring their chosen dataset:
     • Examine the structure and contents
     • Identify variables and data types
     • Check for missing or inconsistent data
     • Consider potential challenges
   • Teams document initial observations and questions
   • Teams create a plan for their analysis, including:
     • Data cleaning steps needed
     • Analysis methods to apply
     • Potential visualizations to create
     • Division of responsibilities

5. Begin Data Processing and Analysis (10 minutes)

   • Teams start implementing their analysis plan:
     • Clean and prepare the data
     • Perform initial analyses
     • Begin creating visualizations
     • Document process and preliminary findings
   • Teacher circulates to provide guidance and troubleshooting

Closing (10 minutes)

6. Project Check-in and Planning (7 minutes)

   • Teams report on their progress:
     • Dataset and research questions
     • Initial findings or challenges
     • Plan for completing the project
   • Address any common issues or questions
   • Provide guidance for efficient use of next class period

7. Preview Next Class (3 minutes)

   • Remind students to continue working on their projects outside of class if possible
   • Explain that next class will focus on completing analysis and preparing presentations
   • Outline presentation expectations

Assessment

• Formative: Quality of research questions and initial data exploration
• Project Plan: Thoroughness and feasibility of analysis plan

Differentiation

For Advanced Teams

• Encourage more complex research questions
• Suggest advanced analysis techniques
• Recommend combining multiple datasets

For Struggling Teams

• Provide more structured research questions
• Offer pre-cleaned datasets
• Suggest specific analysis approaches

Homework/Extension

• Continue data analysis if not completed in class
• Research additional context for the dataset
• Prepare for team presentation

Teacher Notes

• Have diverse datasets available that will appeal to different interests
• Be prepared to help with technical issues in data tools
• Ensure teams choose appropriately sized datasets for the time available
• Monitor team dynamics to ensure equitable participation
• Encourage teams to document their process, not just results
• Remind students to consider potential biases in their datasets