whitemt/dat_sf_14

DAT_SF_14

Course materials for General Assembly's Data Science course in San Francisco (4/28/15 - 7/9/15).

Instructor:

• Yuchen Zhao

Experts-in-Residence:

• Alex Chao (Office Hours: 2-6 pm Sundays)

• David Feldman (Office Hours: 4-6:30 pm Tuesdays,Thursdays)

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Course Schedule (Tentative)

Week	Tuesday	Thursday
1	4/28: Introduction to Data Exploration	4/30: Introduction to Machine Learning
2	5/05: Data Format, Access & Transformation	5/07: K-Nearest Neighbors Classification Final Project Kickoff
3	5/12: Naive Bayes Classification HW1 Due	5/14: Regression and Regularization
4	5/19: Logistic Regression HW2 Due	5/21: K-Means Clustering Project Milestone (PM1): Elevator Pitch
5	5/26: Clustering & Decision Trees	5/28: Tree-based Classifiers
6	6/02: Ensemble Techniques Project Milestone (PM2): Data Ready	6/04: Support Vector Machines
7	6/09: Dimensionality Reduction HW3 Due	6/11: Imbalanced Classes and Evaluation Metrics
8	6/16: Recommendation Systems Project Milestone (PM3): First Draft Due	6/18: Natural Language Processing
9	6/23: Final Project Work Session & Peer Feedback Project Milestone (PM4): Peer Feedback Due	6/25: Map-Reduce & Hadoop
10	6/30: Distributed Data Processing (Spark)	7/02: Network Analysis
11	7/07: Project Presentations Day 1 Project Milestone (PM5): Presentation	7/09: Project Presentations Day 2 Project Milestone (PM5): Presentation & Paper

Homework Schedule

HW	Topics	Dataset	Assigned	Due
1	Data Exploration	titanic	5/05	5/12
2	KNN & Cross Validation	iris	5/07	5/19
PM1	Elevator Pitch	project	-	5/21
PM2	Data Ready	project	-	6/02
3	Decision Trees	bank	6/02	6/09
PM3	First Draft Due	project	-	6/16 (Before Class)

Resources

Working in the terminal

• Data Science at the Command Line
• Learning Bash Scripting For Beginners
• Advanced Bash Scripting Guide

Statistical Learning Theory

• Elements of Statistical Learning

Algorithms

• Top 10 Data Mining Algorithms in Plain English

Python

• Data Science from Scratch

dat_sf_14's Issues

HW1 comments

Nice work!

You started showing your answers as comments at the end. It’s less work and more readable to print your answers like you did for the first problems.