Data Science with Python

Overview

This Data Science with Python training course teaches engineers, data scientists, statisticians, and other quantitative professionals the Python programming skills they need to analyze and chart data.

Objectives

At the end of Data Science with Python training course, participants will be able to

• Understand the difference between Python basic data types
• Know when to use different python collections
• Implement python functions
• Understand control flow constructs in Python
• Handle errors via exception handling constructs
• Be able to quantitatively define an answerable, actionable question
• Import both structured and unstructured data into Python
• Parse unstructured data into structured formats
• Understand the differences between NumPy arrays and pandas dataframes
• Understand where Python fits in the Python/Hadoop/Spark ecosystem
• Simulate data through random number generation
• Understand mechanisms for missing data and analytic implications
• Explore and Clean Data
• Create compelling graphics to reveal analytic results
• Reshape and merge data to prepare for advanced analytics
• Find test for group differences using inferential statistics
• Implement linear regression from a frequentist perspective
• Understand non-linear terms, confounding, and interaction in linear regression
• Extend to logistic regression to model binary outcomes
• Understand the difference between machine learning and frequentist approaches to statistics
• Implement classification and regression models using machine learning
• Score new datasets, evaluate model fit, and quantify variable importance

Prerequisites

All attendees should have prior programming experience and an understanding of basic statistics.

Course Outline

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