• Online, Self-Paced
Course Description

Python is a high-level programming language that has code readability and simplicity as its primary design goals. Coupled with a few key APIs, it also becomes a very powerful data analysis tool. This course will cover basic data science fundamentals and apply them to Python.

Learning Objectives

Introduction to Data Science for Python

  • start the course
  • demonstrate how to set up and use Anaconda for Python
  • describe the key features of Jupyter as well as how to install it
  • work with the Notebook server and dashboard
  • detail the key characteristics and how to install and use NumPy
  • create an example that utilizes NumPy arrays
  • detail the key characteristics and how to install pandas
  • perform basic data manipulation using pandas
  • create a data visualization using matplotlib
  • use scikit-learn to perform data normalization
  • perform supervised learning by using the scikit-learn library to perform optical recognition of hand-written digits
  • install and use the ArcGIS Python API in a Python app
  • use NLTK and Python to tokenize words and sentences
  • analyze an ego network using Python and Networkx
  • perform web scraping using BeautifulSoup 4 in Python
  • install and configure PySpark for Python

Practice: Working with Python and Data Science

  • perform basic data manipulation using pandas

Framework Connections

The materials within this course focus on the Knowledge Skills and Abilities (KSAs) identified within the Specialty Areas listed below. Click to view Specialty Area details within the interactive National Cybersecurity Workforce Framework.