Course materials for General Assembly's Data Science course in San Francisco (2/25/16 - 5/3/16)
| Week | Date | Class | Dues and assigned |
|---|---|---|---|
| 0 | Pre-Work | ||
| Unit 1 - Research Design and Exploratory Data Analysis | |||
| 1 | 2/25 | What is Data Science | Unit Project 1 and Final Project 1 assigned |
| 1 | 3/1 | Research Design and Pandas | |
| 2 | 3/3 | Statistics Fundamentals I | Unit Project 1 due; Unit Project 2 assigned |
| 2 | 3/8 | Statistics Fundamentals II | Unit Project 2 due; Unit Project 3 assigned |
| 3 | 3/10 | Flexible Class Session | |
| Unit 2 - Foundations of Data Modeling | |||
| 3 | 3/15 | Introduction to Regression | |
| 4 | 3/17 | Evaluating Model Fit | |
| 4 | 3/22 | Introduction to Classification | Final Project 1 due; Final Project 2 assigned |
| 5 | 3/24 | Introduction to Logistic Regression | Unit Project 3 due; Unit Project 4 assigned |
| 5 | 3/29 | Communicating Logistic Regression Results | Unit Project 4 due |
| 6 | 3/31 | Flexible Class Session | |
| Unit 3 - Data Science in the Real World | |||
| 6 | 4/5 | Decision Trees and Random Forests | |
| 7 | 4/7 | Natural Language Processing | |
| 7 | 4/12 | Dimensionality Reduction | Final Project 2 due; Final Project 3 assigned |
| 8 | 4/14 | Time Series Data I | |
| 8 | 4/19 | Time Series Data II | Final Project 3 due; Final Project 4 assigned |
| 9 | 4/21 | Database Technologies | |
| 9 | 4/26 | Where to Go Next | Final Project 4 due; Final Project 5 assigned |
| 10 | 4/28 | Flexible Class Session | |
| 10 | 5/3 | Final Project Presentations | Final Project 5 due |
(Syllabus last updated on 2/16/2016)
(Flexible class sessions will be finalized after student goals are defined)
Instructor: Ivan Corneillet
Course Producer: Vanessa Ohta
- Missing no more than 2 class sessions over the duration the course
- Completing 80% of assigned unit project (4 unit projects)
- Completing the final project (one final project subdivided into 5 deliverables)
| Unit Project | Description | Goal | Assigned | Due |
|---|---|---|---|---|
| 1 | Research Design Write-Up | Create a problem statement, analysis plan, and data dictionary in iPython | 2/25 | 3/3 6:30PM Pacific |
| 2 | Exploratory Data Analysis | Explore data with visualizations and statistical analysis in an iPython notebook | 3/3 | 3/8 6:30PM Pacific |
| 3 | Basic Modeling Assignment | Perform logistic regressions, creating dummy variables, and calculating probabilities | 3/8 | 3/24 6:30PM Pacific |
| 4 | Notebook with Executive Summary | Present your findings in an iPython notebook with executive summary, visuals, and recommendations | 3/24 | 3/29 6:30PM Pacific |
| Final Project, Part | Description | Goal | Assigned | Due |
|---|---|---|---|---|
| 1 | Lightning Presentation | Prepare a one-minute lightning talk that covers 3 potential project topics | 2/25 | 3/22 12:00PM |
| 2 | Experiment Write-Up | Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics | 3/22 | 4/12 6:30PM |
| 3 | Exploratory Analysis | Confirm your data and create an exploratory analysis notebook with stat analysis and visualization | 4/12 | 4/19 6:30PM |
| 4 | Notebook Draft | Detailed iPython technical notebook with a summary of your statistical analysis, model, and evaluation metrics | 4/19 | 4/26 6:30PM |
| 5 | Presentation | Detailed presentation deck that relates your data, model, and findings to a non-technical audience | 4/26 | 5/3 12:00PM |