• 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 NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Software Development