Course materials for General Assembly's Data Science course in San Francisco (1/16/18 - 3/22/18)
Instructor: Gus Ostow
Instructional Assistant: Stewart Knox
Course Times
Tuesday/Thursday: 6:30pm - 9:30pm
IA Office hours:
Slack: Monday 6-7PM
In-person: Thursday 5:30-6:30PM
| Week | Tuesday | Thursday | Project Milestone | HW |
|---|---|---|---|---|
| 1 | 1/16: Introduction / Git & Command-line | 1/18: Numpy & Pandas Part 1 | ||
| 2 | 1/23: Exploratory Data Analysis | 1/25: Pandas Part 2 | HW 1 Assigned (T) | |
| 3 | 1/30: Statistics Fundamentals + Hypothesis Testing | 2/1: K-Nearest Neighbors | HW 1 Due (Th) | |
| 4 | 2/6: Linear Regression | 2/8: Evaluating Model Fit | HW 2 Assigned (T) | |
| 5 | 2/13: Logistic Regression | 2/15: Bias Variance Tradeoff / Regularization | HW 2 Due (Th) | |
| 6 | 2/20: Group Classification Challenge | 2/22: Decision Trees | Project Proposal due (Th) | |
| 7 | 2/27: Ensembles | 3/1: Advanced Sklearn: Gridsearch and Pipelines | ||
| 8 | 3/6: Group Regression Challenge | 3/8: SQL and Databases | HW 3 Assigned (Tu) | |
| 9 | 3/13: Natural Language Processing | 3/15: Dimensionality Reduction | HW 3 Due (Th) | |
| 10 | 3/20: Project work & review session | 3/22: Final Project Presentations | Final Project due (Th) |
Flex topics:
- Deep learning
- Clustering
- Install the Anaconda distribution of Python 2.7x.
- Install Git and create a GitHub account.
- Once you receive an email invitation from Slack, join our "DAT-SF-42" team and add your photo!
- Day one Github setup
- Class wiki
- Daily class information, including objectives, and pre-resources
- Elements of Statistical Learning
- Plenty more to come in this section.