Abstract

Why Learn with Python for Data Scientists?

Your time is valuable and I don't want to waste it. This material packs my findings after working with data in Python for over 20 years, writing a few books about it, and teaching it to thousands through live and virtual trainings.

  •         Teaches you rules to avoid common errors
  •         Learn best practices
  •         Understand where confusing constructs come from and how to use them
  •         Grok Jupyter
  •         Master basic data structures
  •         Write code that you can read
  •         Write code that you can debug
  •         Write code that you can deploy
  •         Prepare yourself to use NumPy and Pandas

Instructor

Python & Data Science Corporate Trainer / Consultant | MetaSnake

Matt Harrison

Author and instructor of Python and Data Science material. Co-chair Utah Python user group. Speaker/presenter at various conferences (PyCon, OSCON, Strata, SciPy, SCALE, OpenWest, StartFest). Corporate trainer to companies big (HP, Adobe, Cisco, Samsung, Qualcomm) and small (Instructure, Fusion-IO). Taught multiple 6 week courses for elementary students on Drone programming, Python programming, web development (html and css), and ebook production (published an ebook and physical book at the end).

DIFICULTY LEVEL: INTERMEDIATE

Course Outline

- Install
- Jupyter
- Jupyter Exercise

Python - Part One
- Variables
- Variables Exercise
- Math
- Math Exercise
- Getting Help
- Getting Help Exercise
- Strings
- Strings Exercise
- Files
- Files Exercise
- Lists
- Lists Exercise
- Slicing
- Slicing Exericse
- Dicts
- Dicts Exercise
- Looping
- Looping Exercise
- Functions
- Functions Exercise

Python - Part Two
- Modules
- Modules Exercise
- Classes
- Classes Exercise
- Errors
- Exception Exercise
- Numpy
- Numpy Exercise
- Slicing
- Numpy Slicing Exercise
- Boolean Arrays
- Numpy Boolean Arrays
- Ufuncs
- Numpy Ufunc Exercise
- Pandas
- Pandas Exercise