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.
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Course Description
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.
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Why Analyze Survey Data & When to Apply Statistical Tools
FreeWhat 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.
Introducing Survey Data Analysis50 xpLooking at levels of measurements50 xpCrosstabulation100 xpDescriptive and Inferential Statistics50 xpDescriptive statistics100 xpInferential statistics100 xpStatistical Modeling Techniques50 xpScatter plot inspection100 xpChoose a statistical method50 xpSampling technique match100 xp - 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.
Random sampling50 xpRandom sample of employees100 xpReproducible random sampling100 xpStratified Random Sampling50 xpSpread of yes's and no's100 xpStratified sampling100 xpWeighted sampling50 xpBlog survey100 xpWeighted sampling on handedness100 xpCluster Sampling50 xpGroup clusters100 xpChoosing clusters100 xpCluster sampling analysis100 xp - 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.
Descriptive statistics in survey analysis50 xpFrequency distribution100 xpMeasures of variability100 xpMeasures of central tendency100 xpInferential statistics in survey analysis50 xpVisualize data: histogram100 xpFind the z-score100 xpCorrelations50 xpAnalyze variables with .corr()100 xpAre employees happy?50 xpFair and square100 xp - 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.
Regression analysis50 xpFitting a linear regression model100 xpVisualizing survey data100 xpSafety precautions needed?100 xpTwo sample t-test50 xpAre women more extroverted?100 xpTwo sample t-test on extraversion50 xpChi-square test50 xpTo chi-square or not to chi-square?100 xpMental health in tech survey100 xpMental health vs. remote work100 xpCongratulations50 xp
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prerequisites
Hypothesis Testing in Python
EbunOluwa AndrewData Scientist
Ebun Andrew is a petroleum engineer and data scientist who discovered her passion and interest for data science and analytics while taking a DataCamp course! Her main interests are blogging, visiting new cities, and dabbling in all sorts of exquisite cuisines.
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