2. Python Basics (09/03)¶

This lecture discusses the general strategic impact of data, open data, data encoding, data provenance, data wrangling, includeing merging, aggregation, filtering. Continued introduction to coding includes conditionals, loops, functions, missing values, filtering, group-by. We will also introduce a basic Kaggle model for the Titantic dataset. Link

2.1. Before Class¶

Session	Content
2	Chapter 1: The Machine Learning Landscape Link

2.2. In Class¶

Session	Content
2	Presentation - Section 01 Link
2	Presentation - Section 02 Link
2	Python Overview Link
2	Basic Data Structures Link
2	Video Recording of Class Sec 01 Link
2	Video Recording of Class Sec 02 Link

1. Course Overview & Introduction to the Data Science Lifecycle (08/31) 3. Python Basics (First in Person Class, Tuesday follow Monday Schedule) (09/08)