This module is not classroom-tested. For some classroom-tested assignments on this topic, see our Fundamentals modules.
Module Description¶
The goal of this module is to introduce the fundamentals of data manipulation, privacy, and ethical considerations in the context of social sciences and data science. Students will explore how data is generated, processed, and regulated while gaining practical skills such as file handling, sampling, and data manipulation. Through lectures, discussions, and labs, students will learn about privacy regulations, ethical concerns, and the role of data in shaping society. Case studies and hands-on projects will provide opportunities to apply technical skills and reflect on the broader implications of data use.
Week 1¶
Lecture 1: What is Data? Ethical Considerations and Practicalities¶
Learning Outcomes:
Understand how user data is generated and stored.
Explain the implications of sharing or selling data.
Perform basic file operations (open, read, write).
Activities:
Introduction to data ethics and privacy.
Opening files and loading data into tables.
Lecture 2: Data Privacy Regulations and Practical File Operations¶
Learning Outcomes:
Explain regulatory structures for data privacy (CCPA, GDPR).
Identify circumstances for anonymizing data.
Handle I/O errors in file operations.
Activities:
Discussion on regulatory frameworks and privacy vs. security.
Practical skills for closing and saving files.
Discussion 1: Data Privacy and Regulatory Guidelines¶
Learning Outcomes:
Recognize data analysis’s role in power structures.
Understand data regulations (CCPA, GDPR).
Activities:
Analyze real-world privacy guidelines and their societal impact.
Lab 1: File Handling and Basic Data Operations¶
Learning Outcomes:
Perform basic file operations (open, read, write).
Understand file paths and error handling.
Project/Homework Part 1: Data File Manipulation and Basic Table Operations¶
Learning Outcomes Covered:
Perform basic file operations.
Process different file formats (CSV, JSON).
Activities:
Hands-on exercises for working with data files.
Week 2¶
Lecture 3: Basics of Sampling and Population in Social Sciences¶
Learning Outcomes:
Define key terms related to sampling (e.g., unit of analysis, sample).
Explain the importance of representative samples.
Activities:
Discuss sampling methods and challenges in data representation.
Lecture 4: Sampling Methods and Bias¶
Learning Outcomes:
Understand different sampling methods (probability, non-probability).
Identify sampling bias and its implications.
Activities:
Review case studies (Election Polling, COVID-19).
Visualize sampling designs.
Discussion 2: Sampling in Research and Its Implications¶
Learning Outcomes:
Analyze the potential for bias in sampling.
Understand strengths and weaknesses of various sampling methods.
Activities:
Discuss ethical concerns and biases in real-world sampling
Lab 2: Sampling Techniques and Data Aggregation¶
Jupyter Notebook on Github
Learning Outcomes:
Define key sampling terms and identify bias.
Perform data aggregation techniques using tables.
Project/Homework Part 2: Advanced Table Operations and Visualization¶
Learning Outcomes:
Conduct data manipulation using complex table operations.
Perform data visualization techniques.
Activities:
Work with data tables to aggregate and visualize data.
Week 3¶
Lecture 5: Sampling Case Studies and Complex Data Operations¶
Learning Outcomes:
Identify sampling designs and bias in real research studies.
Apply advanced data manipulation techniques.
Activities:
Analyze case studies (Tuskegee Syphilis, Cambridge Analytica).
Perform complex data operations.
Lecture 6: Data Anonymization and Privacy Regulations¶
Learning Outcomes:
Apply anonymization techniques in data.
Explain the role of privacy regulations in data ethics.
Activities:
Case study walkthroughs focused on ethical data handling.
Discussion 3: Ethical Considerations in Data Analysis¶
Learning Outcomes:
Recognize data’s role in reinforcing power structures.
Understand the limitations and transparency issues in data-based decision-making.
Activities:
Engage in discussions on algorithmic bias and data ethics.
Lab 3: Advanced Data Manipulation and Privacy Techniques¶
Learning Outcomes:
Apply data anonymization techniques.
Conduct complex data manipulations.
Project/Homework Part 3: Anonymization Techniques and Data Privacy¶
Learning Outcomes:
Apply custom data anonymization techniques.
Process data with a focus on regulatory compliance.
Activities:
Complete exercises related to data privacy and regulation.