Master Statistics with Python
Learn the statistics behind data science, from summary statistics to regression models.
Includes Statistics, Experimental Design, Python, pandas, NumPy, SciPy, matplotlib, and more.
Time to complete4 weeks
Certificate of completionYes
Prerequisites1 courseWe suggest you complete the following courses before you get started with Master Statistics with Python:
- Getting Started with Python for Data Science
About this skill path
Data scientists use statistics to produce analyses, recommendations, and even machine learning models. In this Skill Path, you will use Python to summarize datasets, investigate correlations, run hypothesis tests, and build regression models.
Skills you'll gain
- Summarize and visualize data
- Run A/B tests
- Build linear regression models
Syllabus9 units • 31 lessons • 21 projects • 19 quizzes
Learn about variable types and how to store them in Python.
Summary Statistics for Quantitative Data
Learn how to summarize quantitative data in Python using summary statistics.
Visualizing a Distribution of Quantitative Data
Learn how to visualize and describe a distribution of quantitative data using histograms, box plots, and quantiles/quartiles.
Summary Statistics for Categorical Data
Learn how to summarize categorical variables in Python using numerical summary statistics.
Visualizing Categorical Data
Learn how to visualize and describe categorical data using bar charts and pie charts.
Associations between Variables
Learn how to investigate whether there is an association between two variables.
Learn the fundamentals of probability by investigating random events.
Hands-on learningDon't just watch or read about someone else coding — write your own code live in our online, interactive platform. You'll even get AI-driven recommendations on what you need to review to help keep you on track.
Projects in this skill path
Census VariablesApply your knowledge of variable types to investigate, clean, and begin to analyze a sample of simulated census data.
Central Tendency for Housing DataIn this project, you will use your knowledge of mean, median and mode to make conclusions about three boroughs in New York City: Manhattan, New York City, and Queens.
Variance in WeatherFind the best time to visit London by examining weather data.
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Looking for something else?
Introduction to Regression AnalysisThis article is a brief introduction to the formal theory (otherwise known as Math) behind regression analysis.
Exploratory Data Analysis: Data VisualizationLearn to explore a dataset by visualizing the data.
How to Select a Meaningful VisualizationThis article will guide you through the process of selecting a graph for a visualization.
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What's included in skill paths
Practice ProjectsGuided projects that help you solidify the skills and concepts you're learning.
AssessmentsAuto-graded quizzes and immediate feedback help you reinforce your skills as you learn.
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