Course materials for General Assembly's Data Science course in San Francisco (6/21/17 - 8/30/17)
| Class | Date | Topic | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 01 | 6/21 | What is Data Science | ||
| 02 | 6/26 | Python | ||
| 03 | 6/28 | pandas | ||
| 04 | 7/5 | Databases and Scrapping | ||
| 05 | 7/10 | Exploratory Data Analysis | ||
| 06 | 7/12 | k-Nearest Neighbors | Unit Project 1 | |
| 07 | 7/17 | Data Wrangling and Exploratory Data Analysis Challenge | Unit Project 1 | |
| 08 | 7/19 | Linear Regression | Final Project 1 | |
| 09 | 7/24 | Linear Regression, Part 2 | Unit Project 2 | |
| 10 | 7/26 | Linear Regression, Part 3 | Final Project 1 | |
| 11 | 7/31 | Regularization | Unit Project 2 | |
| 12 | 8/2 | Logistic Regression | ||
| 13 | 8/7 | Machine Learning Modeling Challenge | Final Project 2 | |
| 14 | 8/9 | Trees | Unit Project 3 | |
| 15 | 8/14 | Intermediate Project Presentations | Final Project 2 | |
| 16 | 8/16 | Review | Unit Project 3 | |
| 17 | 8/21 | Machine Learning Modeling Challenge, Take 2 | ||
| 18 | 8/23 | Natural Language Processing | ||
| 19 | 8/28 | Time Series | ||
| 20 | 8/30 | Final Project Presentations and Wrap-Up | Final Project 3 | Final Project 3 |
Lead Instructor: Ivan Corneillet
Associate Instructor: George McIntire
Course Producer: Matt Jones
- George: Tuesdays, 6 PM to 8 PM
- Ivan: On demand; check sign-up sheet; usually just before class
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. George 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 | Create a problem statement, analysis plan, and data dictionary | 7/12 | 7/17 |
| 2 | Exploratory Data Analysis | Perform exploratory data analysis using visualizations and statistical analysis | 7/24 | 7/31 |
| 3 | Machine Learning Modeling and Executive Summary | Engineer features, perform logistic regressions, and predict class probabilities; write up an executive summary that outlines your findings and the methods used | 8/9 | 8/16 |
| Final Project | Description | Objective | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 1 | Lightning Pitch | Prepare a two- to three-minutes lightning talk covering three potential project topics | 7/19 | 7/26 |
| 2 | Research Design, Exploratory Data Analysis, and Intermediate Presentation | Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics; confirm your data and create an exploratory data analysis notebook with statistical analysis and visualization | 8/7 | 8/14 |
| 3 | Machine Learning Modeling and Final Presentation | Detailed technical Jupyter notebook with a summary of your statistical analysis, model, and evaluation metrics; presentation deck that relates your data, model, findings, and recommandations to a non-technical audience | 8/30 | 8/30 |