Course materials for General Assembly's Data Science course in New York (11/17/15 - 2/16/16).
Course Producer: Daniel Demoray (email: ddemoray@generalassemb.ly)
Instructor: Joel Witten
EiRs: Oleh Dubno and Adam Silver
###Exit Ticket Fill me out at the end of each class!
###Course Description
Foundational course in data science, including machine learning theory, case studies and real-world examples, introduction to various modeling techniques, and other tools to make predictions and decisions about data. Students will gain practical computational experience by running machine learning algorithms and learning how to choose the best and most representative data models to make predictions. Students will be using Python throughout this course.
You can always reach out to Daniel by phone or email if you have any inquiries about enrollment, payments, graduation requirements or questions about how to get to know other students.
General Assembly's Part-time courses are pass/fail programs. We have certain requirements in order to be considered a graduate of our programs:
- Missing no more than 2 class sessions over the duration the course.
- Completing 80% of assigned homework
- Completing the final project
(Advanced topics will be finalized after student goals are defined)
| Week | Tuesday | Thursday |
|---|---|---|
| 1 | 11/17: Introduction to Data Science | 11/19: Introduction to Python for Data Science |
| 2 | 11/24: Intro to Pandas | 11/26: No Class |
| 3 | 12/1: Statistics for Data Science | 12/3: KNN and scikit & Cross Validation |
| 4 | 12/8: Linear Regression | 12/10: Logistic Regression & API |
| 5 | 12/15: K Means Discuss project idea with Instructors | 12/17: Decision Trees |
| 6 | 12/22: Naive Bayes & Imbalanced Classes and Model Evaluation Submit project question | 12/24: No Class |
| 7 | 12/29: No class | 12/31: No class |
| 8 | 1/5: Review | 1/7: Ensemble Techniques |
| 9 | 1/12: Support Vector Machine | 1/14: Dimensionality Reduction 5 Min Proj Presentations |
| 10 | 1/19: Natural Language Processing | 1/21: Neural Networks |
| 11 | 1/26: Final Project Work Day | 1/28: Time Series Analysis Draft of paper Due |
| 12 | 2/2: Kaggle Day | 2/4: Network Graphs Peer Review Due |
| 13 | 2/9: Course Review | 2/11: Final Project Presentations Day 1 Final Papers Due |
| 14 | 2/16: Final Project Presentations Day 2 |
syllabus last updated: 12/4/2015
Please submit your assignments to the Goolge Drives below.
-
Project Write Up (12/22/15)
- Project writeup should be less than half a page containing:
- Title
- Brief background about the project
- List of data sources
- Example
- Project writeup should be less than half a page containing:
| instructor | times available | method |
|---|---|---|
| Joel | by appointment | TBD |
| Oleh | by appointment | calendar |
| Adam | by appointment | calendar |
Please use email or Slack to schedule office hours. Use [office hours] in the subject line as it can help us find the emails easier and reply more quickly.
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. The TAs will be in Slack during class to handle questions. All instructors will be available on Slack during office hours (listed above).
Python Resources By Kevin Markham
- Codecademy's Python course: Good beginner material, including tons of in-browser exercises.
- DataQuest: Similar interface to Codecademy, but focused on teaching Python in the context of data science.
- Google's Python Class: Slightly more advanced, including hours of useful lecture videos and downloadable exercises (with solutions).
- A Crash Course in Python for Scientists: Read through the Overview section for a quick introduction to Python.
- Python 2.7x Reference Guide: Kevin's beginner-oriented guide that demonstrates a ton of Python concepts through short, well-commented examples.
- Python Tutor: Allows you to visualize the execution of Python code.