victoryang1 / DAT-NYC-37

Course materials for General Assembly's Data Science course in New York (6/1/16 - 8/15/16)

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DAT-NYC-37

Course materials for General Assembly's Data Science course in New York (6/1/16 - 8/15/16)

Project folder

The repository to upload projects can be found here: https://github.com/ga-students/DAT-NYC-37-Students

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 6/1 What is Data Science
1 6/6 Research Design and pandas
2 6/8 Descriptive Statistics for Exploratory Data Analysis
2 6/13 Flexible Class Session: Exploratory Data Analysis Unit Project 1 due
3 6/15 Inferential Statistics for Model Fit
Unit 2 - Foundations of Data Modeling
3 6/20 Introduction to Regression and Model Fit Unit Project 2 due
4 6/22 Introduction to Regression and Model Fit, Part 2
4 6/27 In-Class Project: Building a model
5 6/29 Introduction to Classification Final Project 1 due
5 7/6 Classification with K-Nearest Neighbors
6 7/11 Decision Trees and Random Forests
6 7/13 Introduction to Logistic Regression
Unit 3 - Data Science in the Real World
7 7/18 Advanced Metrics and Communicating Results Final Project 2 Due due
7 7/20 Natural Language Processing and Text Classification Unit Project 3 Due due
8 7/25 Latent Variables and Natural Language Processing
8 7/27 Scraping data from an API
9 8/1 Flex Sesson
9 8/3 Wrapping Up and Next Steps
10 8/8 Final Project Presentations **Final Project presentation **
10 8/10 Final Project Presentations, Part 2

(Syllabus last updated on 6/16/2016)

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

Your Team

Instructor: Anthony Erlinger

Expert-in-Residence: Zunayed Ali Morsalin

Slack

You've all been invited to use Slack for chat (channel #dat-37) 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.

About

Course materials for General Assembly's Data Science course in New York (6/1/16 - 8/15/16)


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Language:Jupyter Notebook 99.0%Language:Python 0.9%Language:Shell 0.0%