Abstract: 

For the Python Fundamentals session, we will start by ensuring we have Jupyter Notebook installed and then jump right into Python. It will be a fast-paced crash course, covering the basics from data types and looping operations to more intermediate topics such as lambda expressions and object-oriented programming. By the end of it, you will feel comfortable with Python syntax, and be able to use Python code to solve data analysis and mathematical problems that will form the foundation for more advanced sessions.

Hour 1:

Anaconda Installation / jupyter notebook installation

Data types
Numeric
Sequence
Mapping
Set

Operations for each data type

Indexing
In-place algorithms
Copy / shallow copy / deep copy
String formatting
Packing and unpacking

Hour 2:

If statements
Looping
For loops
While loops
break/continue/pass
Enumerate
Zipping and unzipping

Hour 3:

Functions
Default and arbitrary arguments
Return statements
Scope (local vs. global)
Lambda functions

Object-Oriented Programming
Constructors
Methods

Bio: 

Dimitri applies statistical and machine learning modeling techniques as a data scientist at Scribd, a subscription service for ebooks and audiobooks. He also has a passion for sharing his knowledge with others, and consequently, he teaches the Beginner Python and Math for Data Science course at Metis, an organization that provides data science training programs and where he is a fellow alum. Prior to that, Dimitri implemented deep learning algorithms and analyzed their performance at Intel for 3 years as a systems engineer in Phoenix, Arizona. He received both a BS and MS in electrical engineering with an emphasis on signal processing from Arizona State University.