Almost everything we do creates data, and knowing how to investigate and communicate it is key. Our Curriculum Developers show us how to summarize data and run hypothesis tests following our Skill Path: Master Statistics with Python.

Master Stats with Python

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Alex will discuss experimental design and sample size determination in the context of an A/B test in the final session of our live series on stats. Check out the full course here: https://j.mp/3xkPV5x

More details:
In this session, we'll talk about experimental design and sample size determination in the context of an A/B test. A/B testing is often used by marketers to compare two versions of a website or product to determine if one is better for business.

Check out the code used in this session here: https://j.mp/3tMGKso

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Codecademy Live Stats #8: A/B Testing: Planning and Implementing an A/B test

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Jamie will focus on hypothesis tests that can be used to evaluate an association between two variables. Check out the full course here: https://j.mp/3eqhh1r

More details:
In this session, we'll turn our attention to hypothesis tests that can be used to evaluate an association between two variables among a population that we can't observe. If we have time, we'll even simulate our own two-sample t-test.

Check out the code used in this session here: https://j.mp/3tMGKso

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Codecademy Live Stats #7: Hypothesis Testing for an Association

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Alex will discuss problems that can arise when hypothesis testing is misused. Check out the full course here: https://j.mp/3vpTu95

More details:
In this session, we'll discuss some of the problems that can arise when hypothesis testing is misused. We'll cover error types and investigate some of the problems that can arise when a single study involves multiple tests.

Check out the code used in this session here: https://j.mp/3tMGKso


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Codecademy Live Stats #6: Hypothesis Testing: Significance Thresholds and Multiple Hypothesis Tests

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Alex will implement a hypothesis test by writing their own simulation-based function in Python. Check out the full course here: https://j.mp/3tKzmOr

More details:
In this session, we'll implement our first hypothesis test, but we'll do it by writing our own simulation-based function in Python. The code might get a little tricky here, but the lessons learned will be invaluable for any statistician, data scientist, or data analyst.

Link to code used in this session: https://j.mp/3tMGKso


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Codecademy Live Stats #5: Hypothesis Testing: Simulating a Binomial Test

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Jamie will introduce inferential statistics and hypothesis testing by exploring the central limit theorem (CLT). Check out the full course here: https://j.mp/3atxPoc

More details:
In this session, we'll introduce inferential statistics and hypothesis testing by learning about the central limit theorem (CLT). The CLT is the mathematical theory behind a number of commonly-used hypothesis tests, and we will demonstrate it using simulation (no math-y formulas)!

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Codecademy Live Stats #4: Introduction to Hypothesis Testing: The Central Limit Theorem

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Jamie will cover ways to assess a relationship between two variables, using summary statistics and visualizations. Check out the full course here: https://j.mp/3axEhKK

More details:
In this session, we'll cover ways of assessing a relationship between two variables, using both summary statistics and data visualizations. For example, how could we use clinical trial data to get a sense of whether a vaccine appears to work?


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Codecademy Live Stats #3: Associations Between Variables

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Alex will show us how to investigate data using summary statistics and data visualizations. Check out the full course here: https://j.mp/32Ei6ON

More details:
In this session, we'll continue to investigate our data with summary statistics and some basic data visualizations, using the Python libraries NumPy, pandas, matplotlib, and Seaborn. We'll also discuss how to choose an appropriate summary statistic to answer a particular question.

You can download our code for this session here: https://j.mp/3tMGKso


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Codecademy Live Stats #2: Summary Statistics and Visualizations Part II

Welcome to mastering statistics with Codecademy!
Curriculum Developers Sophie and Alex will walk us through our Master Statistics with Python Skill Path. Check out the full course here: https://j.mp/3dHq6VH

More details:
In this session, we'll walk through the process of loading a tabular dataset into Python, inspecting the data, and taking a first look at some of the variables. We'll also cover basic Python and pandas syntax and discuss some of the different kinds of data you might encounter.

The code used in this video can be found here: https://j.mp/3tMGKso

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Codecademy Live Stats #1: Summary Statistics and Visualizations Part I

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Interview Prep

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Master Stats with Python

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