Courses
Analyzing Survey Data in Python
中间的技能水平
更新 2023年11月PythonProbability & Statistics4小时14 videos46 Exercises3,700 XP2,692成就声明
深受数千家公司学员的喜爱
培训2人或以上？
试试DataCamp for Business课程描述
Learn How to Use Python on Survey Data
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 used to better understand a population and how to proceed on a matter. Here, you’ll learn the purpose of analyzing survey data and when it is appropriate to apply statistical tools that are descriptive and inferential in nature.
Get Familiar with Key Statistical Survey Analysis Tools
Building on topics covered in Hypothesis Testing in Python, this hands-on course allows you to become familiar with using Python to analyze all sorts of survey data.
You will learn to apply various sampling methods, ensuring that you accurately represent the population in a study and can infer their effects on the conclusion from your analysis.
As you visualize your survey results, you’ll also qualitatively interpret the variables and results associated with modeling tests such as linear regression, the two-sample t-test, and the chi-square test, as it pertains to the type of survey you’re analyzing.
先决条件
Hypothesis Testing in Python1
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.
2
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.
3
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.
4
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.