Skip to content

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

Notifications You must be signed in to change notification settings

rtamiri/DS-SF-30

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

62 Commits
 
 
 
 
 
 
 
 
 
 

Repository files navigation

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!

About

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

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published