Course materials for General Assembly's Data Science course in San Francisco (4/17/17 - 6/26/17)
| Class | Date | Topic | Soft Deadline | Hard Deadline (by 6:30 PM) |
|---|---|---|---|---|
| 01 | 4/17 | What is Data Science | ||
| 02 | 4/19 | The pandas Library | ||
| 03 | 4/24 | Databases, Scrapping, and APIs | ||
| 04 | 4/26 | Exploratory Data Analysis | ||
| 05 | 5/1 | k-Nearest Neighbors | Unit Project 1 | |
| 06 | 5/3 | Applied Data Wrangling and Exploratory Data Analysis | ||
| 07 | 5/8 | Linear Regression | Unit Project 1 | |
| 08 | 5/10 | Linear Regression, Part 2 | Final Project 1 | |
| 09 | 5/15 | Linear Regression, Part 3 | Unit Project 2 | |
| 10 | 5/17 | Regularization | Final Project 1 | |
| 11 | 5/22 | Logistic Regression | Unit Project 2 | |
| 12 | 5/24 | Applied Machine Learning Modeling | ||
| 13 | 5/31 | Advanced Metrics | Final Project 2 | |
| 14 | 6/5 | Clustering | Unit Project 3 | |
| 15 | 6/7 | Intermediate Project Presentations | Final Project 2 | |
| 16 | 6/12 | Trees | Unit Project 3 | |
| 17 | 6/14 | Applied Machine Learning Modeling, Part 2 | ||
| 18 | 6/19 | Natural Language Processing | ||
| 19 | 6/21 | Time Series | ||
| 20 | 6/26 | Final Project Presentations and Wrap-Up | Final Project 3 | Final Project 3 |
Lead Instructor: Ivan Corneillet
Associate Instructors: Gus Ostow and Mohit Nalavadi
Course Producer: Matt Jones
- Gus/Mohit: Mondays and Wednesdays, 4:30 PM to 6:30 PM.
- Ivan: On demand/per request; usually just before or after class and online (e.g., Slack)
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. Gus and Mohit 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 | 5/1 | 5/8 |
| 2 | Exploratory Data Analysis | Perform exploratory data analysis using visualizations and statistical analysis | 5/15 | 5/22 |
| 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 | 6/5 | 6/12 |
| 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 | 5/10 | 5/17 |
| 2 | Experimental Write-Up and Exploratory Data Analysis | 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 | 5/31 | 6/7 |
| 3 | Notebook 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 | 6/26 | 6/26 |