Course materials for General Assembly's Data Science course in San Francisco (9/8/16 - 11/17/16)
Fill me out at the end of each class!
| Class | Date | Topic | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 1 | 9/8 | What is Data Science | ||
| 2 | 9/13 | Research Design and pandas | ||
| 3 | 9/15 | Exploratory Data Analysis | ||
| 4 | 9/20 | Flexible Class Session #1: Exploratory Data Analysis | Unit Project 1 | |
| 5 | 9/22 | Model Fit | ||
| 6 | 9/27 | Linear Regression | Unit Project 2 | Unit Project 1 |
| 7 | 9/29 | Linear Regression and Model Fit, Part 2 | ||
| 8 | 10/4 | k-Nearest Neighbors | Final Project 1 | Unit Project 2 |
| 9 | 10/6 | Logistic Regression | ||
| 10 | 10/11 | Flexible Class Session #2: Machine Learning Modeling | Final Project 2 | Final Project 1 |
| 11 | 10/13 | Advanced Metrics and Communicating Results | Unit Project 3 | |
| 12 | 10/18 | Decision Trees and Random Forests | Final Project 2 | |
| 13 | 10/20 | Flexible Class Session #3: Machine Learning Modeling, Part 2 | Final Project 3 | Unit Project 3 |
| 14 | 10/25 | Introduction to Time Series | ||
| 15 | 10/27 | Introduction to Natural Language Processing | Unit Project 4 | Final Project 3 |
| 16 | 11/3 | Introduction to Databases | ||
| 17 | 11/8 | Wrapping Up and Next Steps | Final Project 4 | Unit Project 4 |
| 18 | 11/10 | Flexible Class Session #4: Market Segmentation | ||
| 19 | 11/15 | Final Project Presentations | Final Project 5 | Final Project 4 Final Project 5 |
| 20 | 11/17 | Final Project Presentations, Part 2 |
(last updated on 9/15)
Lead Instructor: Ivan Corneillet
Associate Instructor: Dan Bricarello
Course Producer: Vanessa Ohta
- Dan: Wednesdays, 6:30-7:30 PM and Thursday, 5:30-6:30 PM
- Ivan: Per request; usually just before or after class and online (e.g., Slack, phone)
You've all been invited to use Slack for chat during class and the day. Please consider this the primary way to contact other students. Dan will be on Slack during class and office hours to handle questions.
| Unit Project | Description | Objective | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 1 | Research Design Write-Up | Create a problem statement, analysis plan, and data dictionary | 9/20 | 9/27 |
| 2 | Exploratory Data Analysis | Perform exploratory data analysis using visualizations and statistical analysis | 9/27 | 10/4 |
| 3 | Basic Machine Learning Modeling | Transform variables, perform logistic regressions, and predict class probabilities | 10/13 | 10/20 |
| 4 | Notebook with Executive Summary | Present your findings in a Jupyter notebook with executive summary, visuals, and recommendations | 10/27 | 11/8 |
| Final Project | Description | Objective | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 1 | Lightning Presentation | Prepare a one-minute lightning talk that covers 3 potential project topics | 10/4 | 10/11 |
| 2 | Experiment Write-Up | Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics | 10/11 | 10/18 |
| 3 | Exploratory Data Analysis | Confirm your data and create an exploratory data analysis notebook with statistical analysis and visualization | 10/20 | 10/27 |
| 4 | Notebook Draft | Detailed technical Jupyter notebook with a summary of your statistical analysis, model, and evaluation metrics | 11/8 | 11/15 |
| 4 | Presentation | Detailed presentation deck that relates your data, model, findings, and recommandations to a non-technical audience | 11/15 | 11/15 |