Introduction to Data Analysis - Begin the week with a brief overview of the project and its goals. Discuss the importance of data in decision making, and introduce the essential question: 'How can we use data to make informed decisions about real-world issues?' (15 min)

Data Detective Challenge - Engage students in a group activity where they analyze a mysterious data set. Provide clues to identify variables, potential biases, and the story the data tells. This will foster critical thinking and teamwork. (40 min)

Exploring Variables and Bias - Independently, students will investigate different types of variables and identify possible sources of bias in data sets. Utilize online resources and tools to analyze sample data. (55 min)

Data Visualization Tools - Students will familiarize themselves with data visualization tools such as spreadsheets and graphing software. They will practice creating simple data visualizations to represent given data sets. (55 min)

Analyzing Data Displays - Students will independently analyze data displays from the New York Times, applying essential questions to evaluate the data's story and biases. Reflective journaling to document insights and challenges. (55 min)

In-Person Data Analysis Workshop - Facilitate a hands-on session where students collaboratively evaluate data sets using visualization tools. Focus on identifying patterns and relationships, with a Q&A session to address questions. (60 min)

Presentation Preparation - Students will independently refine their analyses from Week 1 and develop a concise presentation outline focusing on their key findings. (30 min)

Peer Feedback Session - In pairs, students will exchange outlines and provide constructive feedback on clarity, persuasiveness, and use of data visualization. (25 min)

Crafting Visual Narratives - Students will use data visualization tools to create visual elements for their presentation, ensuring they align with their narrative and strengthen their argument. (55 min)

Rehearsal and Refinement - Students will rehearse their presentations independently, making note of areas that require improvement. They will refine their visuals and narrative based on self-assessment. (55 min)

Reflective Journaling - Students will write a journal entry reflecting on the challenges and successes encountered while preparing their presentation. They will consider ethical implications and real-world applications of their findings. (25 min)

Final Touches - Students will finalize their presentations, ensuring all elements are cohesive and polished for the in-person presentation session. (30 min)

In-Person Presentation Day - Students will present their findings to the class and engage in a Q&A session. They will focus on communicating their data-driven arguments clearly and addressing audience questions effectively. (60 min)

Data Collection Techniques - Introduce methods of data collection and encourage students to design their own data collection process for a chosen topic. Discuss ethical considerations in data collection. (25 min)

Group Planning Session - Facilitate small group discussions where students plan the implementation of their data collection strategies. Focus on defining variables and potential biases. (30 min)

Independent Data Collection - Students independently execute their planned data collection process, ensuring ethical considerations are met. Document observations and initial findings. (55 min)

Data Processing and Organization - Guide students in organizing their collected data using spreadsheets or other data management tools. Emphasize data integrity and accuracy. (30 min)

Reflection and Sharing - Encourage students to reflect on their data collection experience, identifying challenges and learning opportunities. Share insights with peers in small groups. (25 min)

Exploring Data Relationships - Students analyze their organized data to identify potential relationships and correlations. Use visual tools to represent findings. (55 min)

In-Person Analysis and Feedback Session - Conduct a hands-on workshop where students present their data analysis to peers, focusing on identified relationships and correlations. Facilitate a feedback session for constructive critique. (60 min)

Data Processing Review - Students will independently review their collected data and refine it for accuracy and completeness, focusing on ensuring data integrity. (25 min)

Collaborative Data Analysis Planning - In small groups, students will discuss strategies for analyzing their data, considering variables, bias, and potential storylines. (30 min)

Independent Data Analysis - Students will use data visualization tools to begin analyzing their data, identifying patterns, trends, and correlations. Document findings in a reflective journal. (55 min)

Identifying Bias and Variables - Students will conduct a critical analysis of their data, specifically looking for potential biases and unmeasured variables that could affect their conclusions. (30 min)

Peer Feedback Session - In small groups, students will present their findings and receive feedback on their analysis techniques and interpretations. (25 min)

Data Story Development - Students will draft a narrative that explains their data findings, integrating visualizations to support their story. Focus on clarity and coherence. (35 min)

Reflective Journaling - Students will reflect on their progress, challenges, and insights, considering how their data story aligns with real-world issues or decisions. (20 min)

In-Person Data Story Workshop - Facilitate a session where students present their data stories to the class, engaging in a Q&A session to refine their arguments and address audience questions. (60 min)

Presentation Outline Review - Students review and refine their presentation outlines independently, focusing on clarity and coherence. Ensure alignment with data findings. (25 min)

Feedback Exchange - In pairs, students share their outlines and provide feedback on each other's work, emphasizing strengths, weaknesses, and alignment with learning targets. (30 min)

Visual Enhancement - Students use data visualization tools to enhance their presentation visuals, ensuring clarity and impact of their data narrative. (55 min)

Rehearsal and Critique - Independently rehearse presentations, focusing on narrative flow and data interpretation. Critique peers' rehearsals in small groups for constructive feedback. (55 min)

Reflective Journal Entry - Write a journal entry reflecting on the presentation preparation process, identifying challenges, successes, and ethical considerations. (25 min)

Final Presentation Preparations - Finalize all presentation elements, ensuring coherence and readiness for audience engagement. (30 min)

In-Person Presentation and Q&A - Conduct presentations to the class, focusing on effective communication of data-driven arguments. Engage in a Q&A session to address audience inquiries and feedback. (60 min)

Final Presentation Preparation - Students will work independently to finalize their data stories and visualizations. They will ensure that their data is clearly represented and supports their conclusions, preparing to present their findings to a real audience. (55 minutes)

Rehearsal and Peer Review - Students will pair up to practice their presentations with a peer. They will provide and receive feedback on clarity, persuasiveness, and data storytelling skills. (55 minutes)

Revisions and Practice - Students will incorporate feedback received from their peers to refine their presentations and visualizations. Additional practice will be encouraged to improve presentation skills. (55 minutes)

Reflective Preparation - Students will engage in a reflective session, considering the ethical implications and real-world applications of their data stories. They will refine their presentations based on these reflections. (55 minutes)

Final Presentation Day - In-person session where students present their digital storytelling projects to classmates, teachers, and invited community members. Engage in a Q&A session to defend their interpretations and conclusions. (60 minutes)