Statistics for Data Analysis

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In this lesson, we kick off the course with an introduction to data and descriptive statistics.

In this lesson, we establish key distinctions between different types of data, including quantitative, categorical, ordinal, nominal, continuous, and discrete data types.

In this lesson, we get into the calculations and use cases for three popular measures of center: mean, median, and mode.

In this lesson, we demystify the mathematical notation used for random variables, observed values, and aggregations.

In this lesson, we get into the calculations and use cases for several popular measures of spread, including five-number summaries, standard deviation, and variance.

In this lesson, we cover two additional forms of descriptive statistics for data distributions: shape and outliers.

In this lesson, we start off with meeting the instructors and a high-level overview of probability.

In this lesson, you'll gain the basics of probability using coins and dice.

In this lesson, you'll learn about one of the most popular distributions in probability - the Binomial Distribution.

Not all events are independent. In this lesson, you'll learn the probability rules for dependent events.

In this lesson, you'll learn one of the most popular rules in all of statistics - the Bayes Rule.

In this lesson, we begin the course by meeting the instructors and giving a quick introduction to experimentation.

In this lesson, you'll learn the mathematics behind moving from a coin flip to a normal distribution.

In this lesson, you'll learn all about the underpinning of confidence intervals and hypothesis testing - sampling distributions.

In this lesson, you'll learn how to use sampling distributions and bootstrapping to create a confidence interval for any parameter of interest.

In this lesson, you'll learn the necessary skills to create and analyze the results of hypothesis testing.

In this lesson, you'll work through a case study of how A/B testing works in the context of website metrics for an online education company.

In this lesson, we kick off the course with an introduction to machine learning and why linear regression is such an important algorithm.

In this lesson, you'll use Python to fit linear regression models, as well as understand how to interpret the results of linear models.

In this lesson, you'll learn to apply multiple linear regression models in Python. Then you'll learn how to interpret the results and understand if your model fits well.

In this lesson, you'll learn to apply logistic regression models in Python as well as how to interpret logistic regression results.

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