• Online, Self-Paced
Course Description

This course introduces the concepts of data science and provides a brief overview of the Python skills needed to work with data. In this course, you will learn about IPython components, Notebook, and the NumPy module. There's still more to come as the course guides you toward managing financial statistics with financial big data.

Learning Objectives

Overview of Python for Data Science

  • start the course
  • describe elements of data science and datasets with various modeling and prediction relationships
  • recognize the various pipelines in data science and the stages of the data science cycle

Python Essentials

  • define and describe the various libraries and packages for data analysis
  • perform the key steps involved in installing Anaconda including all the necessary packages for this course
  • describe the various Python containers for data management
  • create lists, tuples, and dictionaries with Python to drive data
  • use Python list comprehensions to create lists

IPython Interactive Interpreter

  • describe the IPython shell and shell commands
  • run the Jupyter Notebook and familiarize with the basics of its user interface
  • capture Python code output in Jupyter Notebook
  • run the Jupyter QT Console and familiarize with the basics of its user interface
  • use IPython to perform debugging and error management on Python code

NumPy Open Source Extension Module

  • basic access and usage of the NumPy package in a Python development environment
  • describe the various components of NumPy
  • describe ndarray object attributes
  • describe the various NumPy array operations applicable to data science
  • describe different ways of creating NumPy arrays

Read/Write in Python

  • describe how Pandas library may be used to read and write various formats of data
  • use Pandas library to read data from a CSV file and write data out to a CSV file
  • use Python's standard JSON package to read JSON data

Data Loadig and Preprocessing

  • use the pandas library to generate and parse date values
  • perform data clean up by handling missing and erroneous data
  • download and load a sample dataset into Python from a URL
  • load a large dataset as smaller chunks by obtaining an iterator for the dataset

Practice: Work with Python for Data Science

  • recognize the main concepts in data science using Python

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.