Welcome to your Columbia Workshop on Data Science concepts including theory, clustering and decision trees.

Learning Objectives

After this lesson, students will be able to:

Part Zero: Data Science Workflow

• Review the Data science workflow
• Create problems from the analytical mind of a data science professional

Part One: KMeans and Clustering Techniques

• Determine the difference between supervised and unsupervised learning.
• Demonstrate how to apply k-means clustering.
• Explore additional clustering techniques

Part Two: Decision Trees

• Discuss the Fundamentals of Logic conditions with Decision trees and additional techniques

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Additional Resources

• Scikit-learn Clustering Methods
• K-Means Clustering (video)
• Clustering Overview
• Cluster Analysis and K-Means (PDF)
• K-Means Wikipedia Article

Links Reviewed:

• SenseTime Record Raise
• Bike Share Oversupply
• Baidu Cloud
• Alibaba Cloud
• Google Duplex Demo
• AI explains bitcoin
• AlphaGo Wins
• Who is Alan Turing?
• Rasberry Pi
• Drones and Self-Driving Lunar New Year
• Drones New Year Part II
• Schools Facial Tracking China
• Data Science Research
• MxNet Yolo
• Open Data
• Python Resources