Topics and schedule:
Week 1. Introduction to the course
Discussion of the syllabus and classroom processes and assignments. Discussion of history of data analytics and contexts for data analysis in the “data revolution” such as visualization, machine learning, big data, social networks. Identification of a topic for research for the semester and review of methods for searching for and evaluating data sources, including a “data biography.”

Week 2. Critical thinking about data
Discussion of contexts of real world data analytics. Discussion of data ethics. Construction of research questions and discussion of the logic of analysis. Do: Assignment 1: Logic of analysis

Week 3. Introduction to data visualization
Discussion of principles of visual communication. Data narratives. Introduction to visualization in Python. Do: Assignment 2. Telling a story with data

Week 4. Planning an analysis
Discussion of project planning, timelines, benchmarks. Identifying Python tools needed for plan. Using Google Colab for analysis and markup.

Week 5. Finalizing a project plan
Discussion of projects and data sources. Presentations of data biography. Proposal due.

Week 6. Statistical inference
Discussion of probability and inference. Examination and practice of code for descriptive statistics and odds. Do: Assignment 3. Descriptive statistics

Week 7. Data preparation and management
Discussion of data types and data libraries. Discussion of sampling and recoding. Do: Assignment 4. Recoding and indexing

Week 8. Introduction to modeling
Code: Discussion of linear models.

Week 9. Intermediate modeling

Further discussion of linear models. Do: Assignment 5: Linear models #1

Week 10. Beginning the machine learning project
Discussion of a research question on our common data source. Consideration of applications of machine learning to the question.

Week 12. Finishing the machine learning project
Testing and troubleshooting the code for the machine learning project. Assessing the information gained from the analysis.

Week 13. Revisiting the projects
Open discussion of benchmarks and troubleshooting code for the projects.

Week 14. Reviewing data narratives
Examining visualizations and drafts of data narratives.

Week 15. Presenting the projects