DS-SF-30

Course materials for General Assembly's Data Science course in San Francisco (12/6/16 - 2/21/17)

Schedule

Class	Date	Topic	Soft Deadline	Hard Deadline (by 6:30 PM)
01	12/6	What is Data Science
02	12/8	The pandas Library
03	12/13	Databases, Scrapping, and APIs
04	12/15	Exploratory Data Analysis
05	12/20	k-Nearest Neighbors	Unit Project 1
06	1/3	Applied Data Wrangling and Exploratory Data Analysis		Unit Project 1
07	1/5	Linear Regression
08	1/10	Linear Regression, Part 2	Final Project 1
09	1/12	Linear Regression, Part 3	Unit Project 2
10	1/17	Regularization		Final Project 1
11	1/19	Logistic Regression		Unit Project 2
12	1/24	Applied Machine Learning Modeling
13	1/26	Advanced Metrics	Final Project 2
14	1/31	Clustering	Unit Project 3
15	2/2	Intermediate Project Presentations		Final Project 2
16	2/7	Trees		Unit Project 3
17	2/9	Applied Machine Learning Modeling, Part 2
18	2/14	Natural Language Processing
19	2/16	Time Series
20	2/21	Final Project Presentations	Final Project 3	Final Project 3

Your Team

Lead Instructor: Ivan Corneillet

Associate Instructor: Megan O'Rorke

Course Producer: Matt Jones

Office Hours

• Megan: Tuesdays and Thursdays, 5:15-6:15 PM at the GA campus
• Ivan: On demand/per request; usually just before or after class and online (e.g., Slack)

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. Megan will be on Slack during class and office hours to handle questions.

Unit Projects

Unit Project	Description	Objective	Soft Deadline	Hard Deadline (by 6:30 PM)
1	Research Design	Create a problem statement, analysis plan, and data dictionary	12/20	1/3
2	Exploratory Data Analysis	Perform exploratory data analysis using visualizations and statistical analysis	1/12	1/19
3	Machine Learning Modeling	Transform variables, perform logistic regressions, and predict class probabilities	1/31	2/7

Final Project

Final Project	Description	Objective	Soft Deadline	Hard Deadline (by 6:30 PM)
1	Lightning Pitch	Prepare a one-minute lightning talk that covers 3 potential project topics	1/10	1/17
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	1/26	2/2
3	Notebook and Final Presentation	Detailed technical Jupyter notebook with a summary of your statistical analysis, model, and evaluation metrics; detailed presentation deck that relates your data, model, findings, and recommandations to a non-technical audience	2/21	2/21

Exit Tickets

Fill me out at the end of each class!