DS-SF-23

Course materials for General Assembly's Data Science course in San Francisco (5/5/16 - 7/12/16)

Exit Ticket

Fill me out at the end of each class!

Schedule

Week	Date	Class	Due
0		Onboarding
		Unit 1 - Research Design and Exploratory Data Analysis
1	5/5	What is Data Science
1	5/10	Research Design and pandas
2	5/12	Descriptive Statistics for Exploratory Data Analysis	Unit Project 1 due
2	5/17	Flexible Class Session: Exploratory Data Analysis
3	5/19	Inferential Statistics for Model Fit	Unit Project 2 due
		Unit 2 - Foundations of Data Modeling
3	5/24	Introduction to Regression and Model Fit	Final Project 1 due
4	5/26	Introduction to Regression and Model Fit, Part 2
4	5/31	Introduction to Classification
5	6/2	Introduction to Logistic Regression	Final Project 2 due
5	6/7	Advanced Metrics and Communicating Results	Unit Project 3 due
6	6/9	Flexible Class Session: Modeling
		Unit 3 - Data Science in the Real World
6	6/14	Decision Trees and Random Forests	Final Project 3 due
7	6/16	Natural Language Processing and Text Classification
7	6/21	Latent Variables and Natural Language Processing	Unit Project 4 due
8	6/23	Time Series Data
8	6/28	Time Series Data, Part 2
9	6/30	Introduction to Databases	Final Project 4 due
9	7/5	Wrapping Up and Next Steps
10	7/8	Final Project Presentations	Final Project 5 due
10	7/12	Final Project Presentations, Part 2

(Syllabus last updated on 5/5/2016)

(Flexible class sessions will be finalized after student goals are defined)

Your Team

Instructor: Ivan Corneillet

Expert-in-Residence: Bob Stark

Course Producer: Tim Payne

Office Hours

• Bob on Tuesdays and Thursdays, 5:30PM to 6:30PM at GA (one hour before class)
• Ivan: Check his weekly announcements on 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. Bob will be on Slack during class and office hours to handle questions.

Unit Projects

| Unit Project | Description | Goal | Due | |:---:|:---|:---|:---:|:---: | | 1 | Research Design Write-Up | Create a problem statement, analysis plan, and data dictionary in iPython | 5/12 6:30PM Pacific | | 2 | Exploratory Data Analysis | Explore data with visualizations and statistical analysis in an iPython notebook | 5/19 6:30PM Pacific | | 3 | Basic Modeling Assignment | Perform logistic regressions, creating dummy variables, and calculating probabilities | 6/7 6:30PM Pacific | | 4 | Notebook with Executive Summary | Present your findings in an iPython notebook with executive summary, visuals, and recommendations | 6/21 6:30PM Pacific |

Final Project

| Final Project, Part | Description | Goal | Due | |:---:|:---|:---|:---:|:---:| | 1 | Lightning Presentation | Prepare a one-minute lightning talk that covers 3 potential project topics | 5/24 6:30PM Pacific | | 2 | Experiment Write-Up | Create an outline of your research design approach, including hypothesis, assumptions, goals, and success metrics | 6/2 6:30PM Pacific | | 3 | Exploratory Analysis | Confirm your data and create an exploratory analysis notebook with stat analysis and visualization | 6/14 6:30PM Pacific | | 4 | Notebook Draft | Detailed iPython technical notebook with a summary of your statistical analysis, model, and evaluation metrics | 6/30 6:30PM Pacific | | 5 | Presentation | Detailed presentation deck that relates your data, model, and findings to a non-technical audience | 7/8 6:30PM Pacific |