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Apr. 13th, 2025 08:42 pm![[personal profile]](https://www.dreamwidth.org/img/silk/identity/user.png){kind=small}
shiningfractalLet's structure one of the **professionalizing courses** around the **project-based learning (PBL)** and **flipped classroom model**. I’ll choose **Data Science for Social Good** as an example course. This course focuses on applying data science techniques to address real-world social challenges, which aligns well with both PBL and flipped classroom principles.
---
### **Course Title:** Data Science for Social Good
**Duration:** 6 months (24 weeks)
**Level:** Professionalizing course, foundational level (ideal for individuals with basic familiarity with statistics or programming)
**Learning Objectives:**
- Apply fundamental data science techniques to real-world social problems.
- Learn how to collect, clean, and analyze data from diverse sources.
- Use machine learning models to generate actionable insights for social good.
- Understand ethical implications and data privacy concerns in social data analysis.
- Present and communicate data-driven insights effectively to non-technical audiences.
---
### **Course Structure**
#### **Phase 1: Pre-Class Content (Flipped Classroom)**
**Weeks 1-2: Introduction to Data Science and Problem Framing**
- **Pre-Class Material**:
- **Video Lectures**: Introduction to data science, types of data, data science lifecycle.
- **Readings**: Key articles and case studies on data science for social good, such as how data science is used in social policy or healthcare.
- **Interactive Exercises**: Data cleaning and exploration using a dataset (e.g., an open dataset from government or non-profit organizations).
- **Quiz**: Basics of data science and identifying problems that can be addressed through data science.
**Week 2: Application in Social Good**
- **Pre-Class Material**:
- **Case Studies**: Case studies where data science has been applied to address issues like homelessness, poverty, climate change, or public health.
- **Interactive Exercises**: Exercises in framing a social problem, identifying the data required, and developing hypotheses.
- **Podcast/Article**: Ethics in social good projects—an introduction to data privacy and biases in social data.
---
#### **Phase 2: In-Class Project Work (PBL)**
**Weeks 3-5: Data Collection and Cleaning**
- **Project Start**: Students are divided into small groups, each working on a different social problem. Example problems include:
- Predicting areas with high risk of flooding based on climate data.
- Analyzing public health data to identify correlations between income and access to healthcare.
- Analyzing educational data to determine factors affecting graduation rates.
- **In-Class Activities**:
- **Collaborative Work**: Students collect and clean real-world data using tools like Python, Jupyter Notebooks, and pandas. They work together to find the right datasets and clean them for analysis.
- **Hands-On Lab Sessions**: Guided labs on data wrangling, including handling missing values, merging datasets, and formatting data for analysis.
- **Guest Speaker**: A data scientist working in the social good sector provides insight on real-world applications of data science.
- **Outcome**: By the end of the week, groups should have a cleaned dataset ready for analysis.
**Weeks 6-8: Exploratory Data Analysis (EDA) and Feature Engineering**
- **Pre-Class Content**:
- **Video Tutorials**: Data visualization, exploratory data analysis (EDA), and feature engineering.
- **Interactive Exercises**: Use Python and libraries like Matplotlib and Seaborn to visualize data.
- **Reading**: Articles on the importance of EDA in creating reliable predictive models and the ethical considerations when analyzing social data.
- **In-Class Activities**:
- **Group Discussions**: Groups discuss patterns observed in the data, potential correlations, and ethical considerations. They identify the key features (variables) that can help predict or explain their social problem.
- **Hands-On Lab**: Guided hands-on lab on feature engineering, where students create new variables from raw data to improve model performance.
- **Outcome**: Students produce initial visualizations of their data and identify the key features for predictive modeling.
**Weeks 9-12: Model Building and Evaluation**
- **Pre-Class Content**:
- **Video Lessons**: Introduction to machine learning algorithms (e.g., decision trees, linear regression, k-nearest neighbors).
- **Interactive Exercises**: Build basic models using libraries like scikit-learn, including fitting a model, making predictions, and evaluating performance (accuracy, precision, recall).
- **Reading**: Literature on ethical AI and the implications of predictive models in social good contexts.
- **In-Class Activities**:
- **Group Work**: Build machine learning models using the cleaned data and feature set. Evaluate the models using appropriate metrics (accuracy, precision, etc.).
- **Peer Review**: Each group presents their model to the class, explaining how they built it and its potential impact on the social problem they are addressing.
- **Instructor Feedback**: After each presentation, instructors give feedback on technical aspects and how the model could be improved.
- **Outcome**: Each group will have a trained model and an evaluation of its performance on the dataset.
**Weeks 13-16: Communicating Results**
- **Pre-Class Content**:
- **Video Tutorials**: Communicating data findings to non-technical audiences, creating reports, and visualizing results.
- **Interactive Exercises**: Build a compelling data story using visualizations, reports, and executive summaries.
- **In-Class Activities**:
- **Group Work**: Students prepare their final presentations for stakeholders (could be simulated as local government officials or non-profit leaders).
- **Final Presentation Preparation**: Focus on the importance of storytelling with data, making technical content accessible, and discussing the social impact of their findings.
- **Outcome**: Each group will prepare a presentation that clearly explains their findings, methodology, and recommendations.
---
#### **Phase 3: Evaluation and Reflection**
**Weeks 17-20: Iteration and Model Refinement**
- **In-Class Activities**:
- **Peer Review and Feedback**: Groups refine their models based on feedback received during presentations.
- **Hands-On Work**: Groups make final adjustments to their models, working on optimizing performance and improving visualizations.
- **Outcome**: Each group submits a refined version of their predictive model and accompanying final report.
**Weeks 21-24: Final Presentation and Public Reporting**
- **Project Presentation**: Groups present their refined models and reports to a panel of external experts (e.g., local policymakers, non-profit leaders, data science professionals).
- **Reflection**: After the presentation, groups reflect on what worked well, what challenges they faced, and how they would improve the project in the future.
- **Final Report**: Students submit a detailed final report that outlines their process, methodologies, and outcomes. This report also includes recommendations for how their findings could be applied in real-world policy or business.
---
### **Evaluation Criteria:**
- **Project Deliverables**: Cleaned datasets, data visualizations, machine learning models, and final reports.
- **Final Presentation**: Clear communication of data-driven insights to a non-technical audience, with an emphasis on the social impact.
- **Peer Review**: Collaborative efforts and contributions within the group.
- **Instructor Feedback**: Assessment of technical understanding and application of concepts.
---
### **Conclusion**
By combining the **flipped classroom model** with **project-based learning**, this **Data Science for Social Good** course provides students with hands-on, practical experience in solving real-world problems, while also encouraging active, collaborative learning. This approach helps students become well-rounded professionals who can not only perform technical tasks but also communicate their findings effectively and ethically, making them more prepared for careers in data science with a focus on social impact.
***
---
### **Course Title:** Data Science for Social Good
**Duration:** 6 months (24 weeks)
**Level:** Professionalizing course, foundational level (ideal for individuals with basic familiarity with statistics or programming)
**Learning Objectives:**
- Apply fundamental data science techniques to real-world social problems.
- Learn how to collect, clean, and analyze data from diverse sources.
- Use machine learning models to generate actionable insights for social good.
- Understand ethical implications and data privacy concerns in social data analysis.
- Present and communicate data-driven insights effectively to non-technical audiences.
---
### **Course Structure**
#### **Phase 1: Pre-Class Content (Flipped Classroom)**
**Weeks 1-2: Introduction to Data Science and Problem Framing**
- **Pre-Class Material**:
- **Video Lectures**: Introduction to data science, types of data, data science lifecycle.
- **Readings**: Key articles and case studies on data science for social good, such as how data science is used in social policy or healthcare.
- **Interactive Exercises**: Data cleaning and exploration using a dataset (e.g., an open dataset from government or non-profit organizations).
- **Quiz**: Basics of data science and identifying problems that can be addressed through data science.
**Week 2: Application in Social Good**
- **Pre-Class Material**:
- **Case Studies**: Case studies where data science has been applied to address issues like homelessness, poverty, climate change, or public health.
- **Interactive Exercises**: Exercises in framing a social problem, identifying the data required, and developing hypotheses.
- **Podcast/Article**: Ethics in social good projects—an introduction to data privacy and biases in social data.
---
#### **Phase 2: In-Class Project Work (PBL)**
**Weeks 3-5: Data Collection and Cleaning**
- **Project Start**: Students are divided into small groups, each working on a different social problem. Example problems include:
- Predicting areas with high risk of flooding based on climate data.
- Analyzing public health data to identify correlations between income and access to healthcare.
- Analyzing educational data to determine factors affecting graduation rates.
- **In-Class Activities**:
- **Collaborative Work**: Students collect and clean real-world data using tools like Python, Jupyter Notebooks, and pandas. They work together to find the right datasets and clean them for analysis.
- **Hands-On Lab Sessions**: Guided labs on data wrangling, including handling missing values, merging datasets, and formatting data for analysis.
- **Guest Speaker**: A data scientist working in the social good sector provides insight on real-world applications of data science.
- **Outcome**: By the end of the week, groups should have a cleaned dataset ready for analysis.
**Weeks 6-8: Exploratory Data Analysis (EDA) and Feature Engineering**
- **Pre-Class Content**:
- **Video Tutorials**: Data visualization, exploratory data analysis (EDA), and feature engineering.
- **Interactive Exercises**: Use Python and libraries like Matplotlib and Seaborn to visualize data.
- **Reading**: Articles on the importance of EDA in creating reliable predictive models and the ethical considerations when analyzing social data.
- **In-Class Activities**:
- **Group Discussions**: Groups discuss patterns observed in the data, potential correlations, and ethical considerations. They identify the key features (variables) that can help predict or explain their social problem.
- **Hands-On Lab**: Guided hands-on lab on feature engineering, where students create new variables from raw data to improve model performance.
- **Outcome**: Students produce initial visualizations of their data and identify the key features for predictive modeling.
**Weeks 9-12: Model Building and Evaluation**
- **Pre-Class Content**:
- **Video Lessons**: Introduction to machine learning algorithms (e.g., decision trees, linear regression, k-nearest neighbors).
- **Interactive Exercises**: Build basic models using libraries like scikit-learn, including fitting a model, making predictions, and evaluating performance (accuracy, precision, recall).
- **Reading**: Literature on ethical AI and the implications of predictive models in social good contexts.
- **In-Class Activities**:
- **Group Work**: Build machine learning models using the cleaned data and feature set. Evaluate the models using appropriate metrics (accuracy, precision, etc.).
- **Peer Review**: Each group presents their model to the class, explaining how they built it and its potential impact on the social problem they are addressing.
- **Instructor Feedback**: After each presentation, instructors give feedback on technical aspects and how the model could be improved.
- **Outcome**: Each group will have a trained model and an evaluation of its performance on the dataset.
**Weeks 13-16: Communicating Results**
- **Pre-Class Content**:
- **Video Tutorials**: Communicating data findings to non-technical audiences, creating reports, and visualizing results.
- **Interactive Exercises**: Build a compelling data story using visualizations, reports, and executive summaries.
- **In-Class Activities**:
- **Group Work**: Students prepare their final presentations for stakeholders (could be simulated as local government officials or non-profit leaders).
- **Final Presentation Preparation**: Focus on the importance of storytelling with data, making technical content accessible, and discussing the social impact of their findings.
- **Outcome**: Each group will prepare a presentation that clearly explains their findings, methodology, and recommendations.
---
#### **Phase 3: Evaluation and Reflection**
**Weeks 17-20: Iteration and Model Refinement**
- **In-Class Activities**:
- **Peer Review and Feedback**: Groups refine their models based on feedback received during presentations.
- **Hands-On Work**: Groups make final adjustments to their models, working on optimizing performance and improving visualizations.
- **Outcome**: Each group submits a refined version of their predictive model and accompanying final report.
**Weeks 21-24: Final Presentation and Public Reporting**
- **Project Presentation**: Groups present their refined models and reports to a panel of external experts (e.g., local policymakers, non-profit leaders, data science professionals).
- **Reflection**: After the presentation, groups reflect on what worked well, what challenges they faced, and how they would improve the project in the future.
- **Final Report**: Students submit a detailed final report that outlines their process, methodologies, and outcomes. This report also includes recommendations for how their findings could be applied in real-world policy or business.
---
### **Evaluation Criteria:**
- **Project Deliverables**: Cleaned datasets, data visualizations, machine learning models, and final reports.
- **Final Presentation**: Clear communication of data-driven insights to a non-technical audience, with an emphasis on the social impact.
- **Peer Review**: Collaborative efforts and contributions within the group.
- **Instructor Feedback**: Assessment of technical understanding and application of concepts.
---
### **Conclusion**
By combining the **flipped classroom model** with **project-based learning**, this **Data Science for Social Good** course provides students with hands-on, practical experience in solving real-world problems, while also encouraging active, collaborative learning. This approach helps students become well-rounded professionals who can not only perform technical tasks but also communicate their findings effectively and ethically, making them more prepared for careers in data science with a focus on social impact.
***
