dmarinav/ds-sf-27

Course materials for General Assembly's Data Science course in San Francisco (9/8/16 - 11/15/16)

DS-SF-27

Course materials for General Assembly's Data Science course in San Francisco (9/8/16 - 11/17/16)

Exit Ticket

Fill me out at the end of each class!

Schedule

Class	Date	Topic	Soft Deadline	Hard Deadline (by 6:30 PM)
1	9/8	What is Data Science
2	9/13	Research Design and pandas
3	9/15	Exploratory Data Analysis
4	9/20	Flexible Class Session #1: Exploratory Data Analysis	Unit Project 1
5	9/22	Model Fit
6	9/27	Linear Regression	Unit Project 2	Unit Project 1
7	9/29	Linear Regression and Model Fit, Part 2
8	10/4	k-Nearest Neighbors	Final Project 1	Unit Project 2
9	10/6	Logistic Regression
10	10/11	Flexible Class Session #2: Machine Learning Modeling	Final Project 2	Final Project 1
11	10/13	Advanced Metrics and Communicating Results	Unit Project 3
12	10/18	Decision Trees and Random Forests		Final Project 2
13	10/20	Flexible Class Session #3: Machine Learning Modeling, Part 2	Final Project 3	Unit Project 3
14	10/25	Flexible Class Session #4: Market Segmentation
15	10/27	Introduction to Time Series	Unit Project 4	Final Project 3
16	11/1	Introduction to Natural Language Processing
17	11/3	Introduction to Databases	Final Project 4	Unit Project 4
18	11/8	Wrapping Up and Next Steps
19	11/10	Final Project Presentations		Final Project 4
20	11/15	Final Project Presentations, Part 2	Final Project 5	Final Project 5

(last updated on 10/25)

Your Team

Lead Instructor: Ivan Corneillet

Associate Instructor: Dan Bricarello

Course Producer: Vanessa Ohta

Office Hours

• Dan: Wednesdays, 6:30-7:30 PM and Thursday, 5:30-6:30 PM
• Ivan: Per request; usually just before or after class and online (e.g., Slack, phone)

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. Dan 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 Write-Up	Create a problem statement, analysis plan, and data dictionary	9/20	9/27
2	Exploratory Data Analysis	Perform exploratory data analysis using visualizations and statistical analysis	9/27	10/4
3	Basic Machine Learning Modeling	Transform variables, perform logistic regressions, and predict class probabilities	10/13	10/20
4	Notebook with Executive Summary	Present your findings in a Jupyter notebook with executive summary, visuals, and recommendations	10/27	11/3

Final Project

Final Project	Description	Objective	Soft Deadline	Hard Deadline (by 6:30 PM)
1	Lightning Presentation	Prepare a one-minute lightning talk that covers 3 potential project topics	10/4	10/11
2	Experiment Write-Up	Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics	10/11	10/18
3	Exploratory Data Analysis	Confirm your data and create an exploratory data analysis notebook with statistical analysis and visualization	10/20	10/27
4	Notebook Draft	Detailed technical Jupyter notebook with a summary of your statistical analysis, model, and evaluation metrics	11/3	11/10
4	Presentation	Detailed presentation deck that relates your data, model, findings, and recommandations to a non-technical audience	11/10	11/10