Skill Path: Probability and Statistics with Python
Probability and statistics are critical in data science. They allow us to gather insights from data and determine whether what we’re seeing is meaningful. This path introduces the basics of statistical analysis using Python, including sampling, working with variables, and understanding frequency distribution tables.
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Learn Probability and Statistics with Python
Here's what you'll learn to do.
Dataquest Skill Paths teach you job-ready skills that can be immediately applied to your current or future data roles and projects.
- How to summarize a distribution's measures of central tendency and variability
- Fundamentals of probability and how to use them for analysis
- How to create and test hypotheses using significance testing
Courses: Probability and Statistics with Python
Learn the fundamentals of Python programming and data science.
Learn important Python data science skills, how to analyze text data, how to work with dates and times, and object-oriented programming.
Learn how vectorized operations make working with data faster, selecting data by value using boolean indexing and using pandas and NumPy methods to analyze data.
Learn how to visualize time series data with line plots, visualizing frequency distributions with bar plots and histograms, and how to speed up your exploratory data visualization workflow using Pandas.
Learn data cleaning and analysis with pandas, how to combine data sets, how to transform and reshape data, and how to clean strings and handle missing data.
Sampling data using simple random sampling, stratified sampling, and cluster sampling, understanding what variables are in statistics, and how they're measured, and building, visualizing, and comparing frequency distribution tables.
Summarizing a distribution using the mean, the weighted mean, the median, or the mode. Measuring the variability of a distribution using the variance and the standard deviation. Learning how to locate and compare values using z-scores.
Learn to assign probabilities based on conditions, assign probabilities based on event independence, assign probabilities based on prior knowledge, and create spam filters using multinomial Naive Bayes.
Learn how to perform a permutation test, perform significance testing to better understand an outcome's importance, and about regular and multi-category chi-square tests.
Learn to estimate theoretical and empirical probabilities, use the fundamental rules of probability, work with combinations and permutations.
