Analyzing Survey Data in Python

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

Whether it is a company looking to understand its employees' work preferences or a marketing campaign wanting to know how to best cater to its dominant audience, survey data is one of the best tools out there. Here, you'll learn the purpose of analyzing survey data and when it is appropriate to apply statistical tools that are descriptive and inferential. You will learn to apply multiple sampling methods using Python, and visualize your survey results to qualitatively interpret the variables and outcomes associated with modeling tests depending on the type of survey you're analyzing.

• Why Analyze Survey Data & When to Apply Statistical Tools
• What is survey data, and how do we determine which statistical test to use to analyze the data? To answer this, you’ll be able to define all sorts of survey data types, encounter important concepts like descriptive and inferential statistics, and visualize survey data to determine the appropriate statistical modeling technique needed. In doing so, you will know how to best qualitatively and quantitatively define the trends and insights you come across in surveys.
• Sampling and Weighting
• In this chapter, you’ll learn the different ways of creating sample survey data out of population survey data by analyzing the parameters by which the survey data was taken.
• Descriptive & Inferential Statistics
• Now it’s time to understand the difference between descriptive and inferential statistics concerning survey data analysis with some real-life examples. Through hands-on exercises, you’ll further interpret the meaning of different variables, key measures such as central tendency and zscore, and interpret results for actionable steps.
• Statistical Modeling
• Last but not least, it’s time to apply statistical modeling to survey data analysis with regression analysis, the two-sample t-test, chi-square test, and interpret the assumptions associated with these tests.