Applied Python for Data Science – Intermediate
Course Duration
5 Days
Audience
Employees of federal, state and local governments; and businesses working with the government.
Prerequisites
Attending students are required to have a background in basic Python development skills .
Course Description
Intermediate 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.
Learning Objectives
- Perform exploratory data analysis using pandas, NumPy, and SciPy
- Create complex data visualizations using matplotlib, Seaborn, and PIL
- Apply machine learning algorithms using SciKit-Learn for classification, regression, and clustering
- Work with large-scale distributed data using Apache Spark and PySpark
- Build and evaluate deep learning models using TensorFlow