Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.
In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.
Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.
Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.
Master sampling to get more accurate statistics with less data.
Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.
Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.
Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.
Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.
Learn to perform linear and logistic regression with multiple explanatory variables.
In this four-hour course, you’ll learn the basics of analyzing time series data in Python.
Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.
Build the foundation you need to think statistically and to speak the language of your data.
In this course, you'll learn about the concepts of random variables, distributions, and conditioning.
Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.
Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.
Discover different types in data modeling, including for prediction, and learn how to conduct linear regression and model assessment measures in the Tidyverse.
Learn the core techniques necessary to extract meaningful insights from time series data.
In this course you will learn to fit hierarchical models with random effects.
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.
In this course you'll learn about basic experimental design, a crucial part of any data analysis.
Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.
Learn survey design using common design structures followed by visualizing and analyzing survey results.
Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.
Learn to perform linear and logistic regression with multiple explanatory variables.
In this course you'll learn how to perform inference using linear models.
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!
The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.
In this course you'll learn techniques for performing statistical inference on numerical data.
Learn to design and run your own Monte Carlo simulations using Python!
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.
Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.
In this course you'll learn how to leverage statistical techniques for working with categorical data.
Learn about experimental design, and how to explore your data to ask and answer meaningful questions.
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.
Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.
Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.
Learn to solve increasingly complex problems using simulations to generate and analyze data.
GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.
Use pandas and Bayesian statistics to see if left-handed people actually die earlier than righties.
Use logistic regression to determine which treatment procedure is more effective for kidney stone removal.
Analyze health survey data to determine how BMI is associated with physical activity and smoking.
Get ready for Halloween by digging into a FiveThirtyEight dataset with all your favorite candy!
Learn to analyze Twitter data and do a deep dive into a hot trend.
Explore Disney movie data, then build a linear regression model to predict box office success.
Import, clean, and analyze seven years worth of training data tracked on the Runkeeper app.
Apply hierarchical and mixed-effect models to analyze Maryland crime rates.
Use your logistic regression skills to protect people from becoming zombies!
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Scrape news headlines for FB and TSLA then apply sentiment analysis to generate investment insight.
Build a book recommendation system using NLP and the text of books like "On the Origin of Species."
Analyze admissions data from UC Berkeley and find out if the university was biased against women.
Analyze the dialog and IMDB ratings of 287 South Park episodes. Warning: contains explicit language.
Apply text mining to Donald Trump's tweets to confirm if he writes the (angrier) Android half.
Play bank data scientist and use regression discontinuity to see which debts are worth collecting.
Build a machine learning classifier that knows whether President Trump or Prime Minister Trudeau is tweeting!
Examine the relationship between heart rate and heart disease using multiple logistic regression.
Examine the network of connections among local health departments in the United States.
Reanalyse the data behind one of the most important discoveries of modern medicine: handwashing.
Perform hypothesis tests to determine if the adverse effects of a pharmaceutical drug are significant!
Load, clean, and visualize scraped Google Play Store data to gain insights into the Android app market.
Use NLP and clustering on movie plot summaries from IMDb and Wikipedia to quantify movie similarity.
Use Natural Language Processing on NIPS papers to uncover the trendiest topics in machine learning research.