Course Overview

Working with NumPy, Pandas, SciKit Learn, SciPy, Spark, TensorFlow, Streaming & More...

Next Level Python in Data Science covers the essentials of using Python as a tool for data scientists to perform exploratory data analysis, complex visualizations, and large-scale distributed processing on “Big Data”. In this course we cover essential mathematical and statistics libraries such as NumPy, Pandas, SciPy, SciKit-Learn, frameworks like TensorFlow and Spark, as well as visualization tools like matplotlib, PIL, and Seaborn.

This course is ‘intermediate level’ as it assumes that attendees have solid data analytics and data science background and have basic Python knowledge.  Topics are introductory in nature, but are covered in-depth, geared for experienced students.

Key Learning Areas

This course provides indoctrination in the practical use of the umbrella of technologies that are on the leading edge of data science development.  Working in a hands-on learning environment led by our expert practitioner, students will learn:

  • How to work with Python in a Data Science Context
  • How to use NumPy, Pandas, and MatPlotLib
  • How to create and process images with PIL
  • How to visualize with Seaborn
  • Key features of SciPy and Scikit Learn
  • How to interact with Spark using DataFrames
  • How to use SparkSQL, MLlib, and Streaming in BigData

Course Outline

Python for Data Science

  • Python Review
  • iPython
  • numpy
  • scipy
  • A Tour of scipy subpackages
  • pandas
  • matplotlib
  • The Python Imaging Library (PIL)
  • Seaborn
  • SciKit-Learn Machine Learning Essentials
  • TensorFlow Overview

Python on Spark

  • PySpark Overview
  • RDDs and DataFrames
  • Spark SQL
  • Spark MLib
  • Spark Streaming

Who Benefits

This course is geared for experienced data analysts, developers, engineers or anyone tasked with utilizing Python for data analytics tasks.

Prerequisites

Attending students are required to have a background in basic Python development skills.

Take Before: Students should have attended or have incoming skills equivalent to those in the following courses:

  • Python Primer for Data Science
  • Applied Python for Data Science