Data Science
for
Administrative datasets
April 23rd - 25th, 2018
Data Science
Data visualization
Machine learning
Software
Reproducibility
Why is programming important?
Flexibility
Automation
Reproducibility
Administrative datasets
Repurposed
Often messy
Data about people
Significant ethical and practical implications
Tabular data
Observations in rows, variables in columns, datatypes in tables
Pandas
Data maturity
U Chicago data maturity framework
Problem Definition
Data and Technology Readiness
Organizational Readiness
Structure of this course
Modular
Hands-on
Schedule
Day 1
| Time | Topic |
|---|---|
| 9 - 9:50 | Introduction |
| 9:50 - 10 | Break |
| 10 - noon | Programming in Python |
| noon - 1 | Lunch |
| 1 - 1:45 | Version control with git |
| 1:45 - 2 | Break |
| 2 - 4 | Git (continued) |
Schedule
Day 2
| Time | Topic |
|---|---|
| 9 - 10:30 | Tabular data in Python |
| 10:30 - 10:45 | Break |
| 11 - noon | Tabular data in Python (continued) |
| noon - 1 | Lunch |
| 1 - 1:45 | Data manipulations in Pandas |
| 1:45 - 2 | Break |
| 2 - 4 | Data manipulations in Pandas (continued) |
Schedule
Day 3
| Time | Topic |
|---|---|
| 9 - 10:30 | Statistics in Pandas |
| 10:30 - 10:45 | Break |
| 11 - noon | Statistics in Pandas (continued) |
| noon - 1 | Lunch |
| 1 - 2:15 | Data visualization with Matplotlib |
| 2:15 - 2:30 | Break |
| 2:30 - 4 | Discussion: next steps |