Overview. Syllabus. How to succeed.
A crash course on programming in Python.
Data models, Databases, Data Warehouses, SQL
Linear Algebra, Nearest Neighbors Classification, Dimensionality Reduction, Decision Trees
A data science perspective on probability and statistics.
Ethical considerations and policy implications.
How to deal with data?
How do we find natural groupings in the data?
Applying data science methodology in a project.