taksir/ds-sf-36

Course materials for General Assembly's Data Science course in San Francisco (6/21/17 - 8/30/17)

DS-SF-36

Course materials for General Assembly's Data Science course in San Francisco (6/21/17 - 8/30/17)

Schedule

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

Your Team

Lead Instructor: Ivan Corneillet

Associate Instructor: George McIntire

Course Producer: Matt Jones

Office Hours

• George: Tuesdays, 6 PM to 8 PM
• Ivan: On demand; check sign-up sheet; usually just before class

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. George 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	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

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

Exit Tickets

Fill me out at the end of each class!