Introduction to Statistics in Python
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
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Course Description
Statistics is the study of how to collect, analyze, and draw conclusions from data. It’s a hugely valuable tool that you can use to bring the future into focus and infer the answer to tons of questions. For example, what is the likelihood of someone purchasing your product, how many calls will your support team receive, and how many jeans sizes should you manufacture to fit 95% of the population? In this course, you'll discover how to answer questions like these as you grow your statistical skills and learn how to calculate averages, use scatterplots to show the relationship between numeric values, and calculate correlation. You'll also tackle probability, the backbone of statistical reasoning, and learn how to use Python to conduct a well-designed study to draw your own conclusions from data.
- 1
Summary Statistics
FreeSummary statistics gives you the tools you need to boil down massive datasets to reveal the highlights. In this chapter, you'll explore summary statistics including mean, median, and standard deviation, and learn how to accurately interpret them. You'll also develop your critical thinking skills, allowing you to choose the best summary statistics for your data.
- 2
Random Numbers and Probability
In this chapter, you'll learn how to generate random samples and measure chance using probability. You'll work with real-world sales data to calculate the probability of a salesperson being successful. Finally, you’ll use the binomial distribution to model events with binary outcomes.
What are the chances?50 xpWith or without replacement?100 xpCalculating probabilities100 xpSampling deals100 xpDiscrete distributions50 xpCreating a probability distribution100 xpIdentifying distributions50 xpExpected value vs. sample mean50 xpContinuous distributions50 xpWhich distribution?100 xpData back-ups100 xpSimulating wait times100 xpThe binomial distribution50 xpSimulating sales deals100 xpCalculating binomial probabilities100 xpHow many sales will be won?100 xp - 3
More Distributions and the Central Limit Theorem
It’s time to explore one of the most important probability distributions in statistics, normal distribution. You’ll create histograms to plot normal distributions and gain an understanding of the central limit theorem, before expanding your knowledge of statistical functions by adding the Poisson, exponential, and t-distributions to your repertoire.
The normal distribution50 xpDistribution of Amir's sales100 xpProbabilities from the normal distribution100 xpSimulating sales under new market conditions100 xpWhich market is better?50 xpThe central limit theorem50 xpVisualizing sampling distributions50 xpThe CLT in action100 xpThe mean of means100 xpThe Poisson distribution50 xpIdentifying lambda100 xpTracking lead responses100 xpMore probability distributions50 xpDistribution dragging and dropping100 xpModeling time between leads100 xpThe t-distribution50 xp - 4
Correlation and Experimental Design
In this chapter, you'll learn how to quantify the strength of a linear relationship between two variables, and explore how confounding variables can affect the relationship between two other variables. You'll also see how a study’s design can influence its results, change how the data should be analyzed, and potentially affect the reliability of your conclusions.
Correlation50 xpGuess the correlation50 xpRelationships between variables100 xpCorrelation caveats50 xpWhat can't correlation measure?100 xpTransforming variables100 xpDoes sugar improve happiness?100 xpConfounders50 xpDesign of experiments50 xpStudy types100 xpLongitudinal vs. cross-sectional studies50 xpCongratulations!50 xp
In the following tracks
Data Analyst with PythonData Scientist with PythonData Scientist Professional with PythonStatistics Fundamentals with PythonCollaborators
Prerequisites
Data Manipulation with pandasMaggie Matsui
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Curriculum Manager at DataCamp
Maggie is a Curriculum Manager at DataCamp. She holds a Bachelor's degree in Statistics and Computer Science from Brown University, where she spent lots of time teaching math, programming, and statistics as a tutor and teaching assistant. She's passionate about teaching all things data-related and making programming accessible to everyone.
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