Key Concepts Learned
- Algorithm：A set of rules or instructions for solving a problem or completing a task
- Data Science → Computer science/engineering focus on algorithms and methods
- Data Analytics → Application of data science methods to other disciplines
- Machine Learning → Algorithms for classification & prediction that learn from data
- AI → Algorithms that adjust and improve across iterations (neural networks, etc.)
- Decennial Census (2020): Everyone counted every 10 years; 9 basic questions: age, race, sex, housing; Constitutional requirement; Determines political representation
- American Community Survey (ACS): 3% of households surveyed annually;Detailed questions: income, education, employment, housing costs; Replaced the old “long form” in 2005; A big source of data we’ll use this semester
Coding Techniques
- Data Cleaning Essentials: str_remove(), str_extract(), str_replace()
- case_when() for categories, MOE calculations
- kable() for professional formatting
Questions & Challenges
- need more coding practice
Connections to Policy
- Algorithmic decision making is especially appealing because it promises: Efficiency - process more cases faster Consistency - same rules applied to everyone Objectivity - removes human bias Cost savings - fewer staff needed
- Algorithmic fairness: Unreliable data can bias automated decisions Resource allocation: Know which areas need extra attention Equity analysis: Some communities may be systematically under-counted Professional credibility: Always assess your data quality This connects directly to our algorithmic bias discussion
Reflection
- [What was most interesting] Exploring algorithm bias