## Introduction to Statistics in Python

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

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71 results ## Introduction to Statistics in Python

## Introduction to Statistics

## Introduction to Statistics in R

## Introduction to Regression in R

## Exploring and Analyzing Data in Python

## Exploratory Data Analysis in R

## Introduction to Statistics in Spreadsheets

## Hypothesis Testing in Python

## Introduction to Regression with statsmodels in Python

## Sampling in Python

## Data Storytelling Concepts

## Intermediate Regression in R

## Exploratory Data Analysis in Power BI

## Time Series Analysis in Python

## Hypothesis Testing in R

## Statistical Thinking in Python (Part 1)

## Sampling in R

## Forecasting in R

## Hierarchical and Mixed Effects Models in R

## Intermediate Regression with statsmodels in Python

## Analyzing Survey Data in R

## Statistical Thinking in Python (Part 2)

## Bayesian Data Analysis in Python

## Generalized Linear Models in R

## A/B Testing in Python

## Factor Analysis in R

## Time Series Analysis in R

## Customer Analytics and A/B Testing in Python

## Forecasting Product Demand in R

## Introduction to Linear Modeling in Python

## Fundamentals of Bayesian Data Analysis in R

## ARIMA Models in R

## Foundations of Inference in R

## Foundations of Probability in R

## Experimental Design in R

## Inference for Numerical Data in R

## Introduction to Network Analysis in Python

## Monte Carlo Simulations in Python

## Foundations of Probability in Python

## Linear Algebra for Data Science in R

## Nonlinear Modeling with Generalized Additive Models (GAMs) in R

## Error and Uncertainty in Spreadsheets

## Generalized Linear Models in Python

## Inference for Linear Regression in R

## Anomaly Detection in Python

## Survival Analysis in R

## Network Analysis in R

## Modeling with Data in the Tidyverse

## Performing Experiments in Python

## Structural Equation Modeling with lavaan in R

## Survival Analysis in Python

## Inference for Categorical Data in R

## Practicing Statistics Interview Questions in Python

## Statistical Simulation in Python

## Analyzing Survey Data in Python

## Business Process Analytics in R

## Introduction to A/B Testing in R

## Bayesian Regression Modeling with rstanarm

## Case Studies in Statistical Thinking

## Intermediate Network Analysis in Python

## Multivariate Probability Distributions in R

## Foundations of Inference in Python

## Case Studies: Network Analysis in R

## Choice Modeling for Marketing in R

## Bayesian Modeling with RJAGS

## Predictive Analytics using Networked Data in R

## Building Response Models in R

## Introduction to Anomaly Detection in R

## Practicing Statistics Interview Questions in R

## Mixture Models in R

## Probability Puzzles in R

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data using Python.

4 hoursProbability & StatisticsMaggie Matsuicourses

Learn the fundamentals of statistics, including measures of center and spread, probability distributions, and hypothesis testing with no coding involved!

4 hoursProbability & StatisticsGeorge Boormancourses

Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

4 hoursProbability & StatisticsMaggie Matsuicourses

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis in R.

4 hoursProbability & StatisticsRichie Cottoncourses

Learn how to explore, visualize, and extract insights from data.

4 hoursProbability & StatisticsAllen Downeycourses

Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

4 hoursProbability & StatisticsAndrew Braycourses

Learn how to leverage statistical techniques using spreadsheets to more effectively work with and extract insights from your data.

4 hoursProbability & StatisticsTed Kwartlercourses

Learn how and when to use common hypothesis tests like t-tests, proportion tests, and chi-square tests in Python.

4 hoursProbability & StatisticsJames Chapmancourses

Predict housing prices and ad click-through rate by implementing, analyzing, and interpreting regression analysis with statsmodels in Python.

4 hoursProbability & StatisticsMaarten Van den Broeckcourses

Learn to draw conclusions from limited data using Python and statistics. This course covers everything from random sampling to stratified and cluster sampling.

4 hoursProbability & StatisticsJames Chapmancourses

Elevate your data storytelling skills and discover how to tell great stories that drive change with your audience.

2 hoursProbability & StatisticsJoe Franklincourses

Learn to perform linear and logistic regression with multiple explanatory variables.

4 hoursProbability & StatisticsRichie Cottoncourses

Enhance your reports with Power BI's Exploratory Data Analysis (EDA). Learn what EDA is for Power BI and how it can help you extract insights from your data.

3 hoursProbability & StatisticsMaarten Van den Broeckcourses

In this four-hour course, you’ll learn the basics of analyzing time series data in Python.

4 hoursProbability & StatisticsRob Reidercourses

Learn how and when to use hypothesis testing in R, including t-tests, proportion tests, and chi-square tests.

4 hoursProbability & StatisticsRichie Cottoncourses

Build the foundation you need to think statistically and to speak the language of your data.

3 hoursProbability & StatisticsJustin Boiscourses

Master sampling to get more accurate statistics with less data.

4 hoursProbability & StatisticsRichie Cottoncourses

Learn how to make predictions about the future using time series forecasting in R including ARIMA models and exponential smoothing methods.

5 hoursProbability & StatisticsRob J. Hyndmancourses

In this course you will learn to fit hierarchical models with random effects.

4 hoursProbability & StatisticsRichard Ericksoncourses

Learn to perform linear and logistic regression with multiple explanatory variables.

4 hoursProbability & StatisticsMaarten Van den Broeckcourses

Learn survey design using common design structures followed by visualizing and analyzing survey results.

4 hoursProbability & StatisticsKelly McConvillecourses

Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

4 hoursProbability & StatisticsJustin Boiscourses

Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

4 hoursProbability & StatisticsMichał Oleszakcourses

The Generalized Linear Model course expands your regression toolbox to include logistic and Poisson regression.

4 hoursProbability & StatisticsRichard Ericksoncourses

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.

4 hoursProbability & StatisticsMoe Lotfy, PhDcourses

Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

4 hoursProbability & StatisticsJennifer Brussowcourses

Learn the core techniques necessary to extract meaningful insights from time series data.

4 hoursProbability & StatisticsDavid S. Mattesoncourses

Learn how to use Python to create, run, and analyze A/B tests to make proactive business decisions.

4 hoursProbability & StatisticsRyan Grossmancourses

Learn how to identify important drivers of demand, look at seasonal effects, and predict demand for a hierarchy of products from a real world example.

4 hoursProbability & StatisticsAric LaBarrcourses

Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

4 hoursProbability & StatisticsJason Vestutocourses

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

4 hoursProbability & StatisticsRasmus Bååthcourses

Become an expert in fitting ARIMA (autoregressive integrated moving average) models to time series data using R.

4 hoursProbability & StatisticsDavid Stoffercourses

Learn how to draw conclusions about a population from a sample of data via a process known as statistical inference.

4 hoursProbability & StatisticsJo Hardincourses

In this course, you'll learn about the concepts of random variables, distributions, and conditioning.

4 hoursProbability & StatisticsDavid Robinsoncourses

In this course you'll learn about basic experimental design, a crucial part of any data analysis.

4 hoursProbability & StatisticsJoanne Xiongcourses

In this course you'll learn techniques for performing statistical inference on numerical data.

4 hoursProbability & StatisticsMine Cetinkaya-Rundelcourses

This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

4 hoursProbability & StatisticsEric Macourses

Learn to design and run your own Monte Carlo simulations using Python!

4 hoursProbability & StatisticsIzzy Webercourses

Learn fundamental probability concepts like random variables, mean and variance, probability distributions, and conditional probabilities.

5 hoursProbability & StatisticsAlexander A. Ramírez M.courses

This course is an introduction to linear algebra, one of the most important mathematical topics underpinning data science.

4 hoursProbability & StatisticsEric Eagercourses

GAMs model relationships in data as nonlinear functions that are highly adaptable to different types of data science problems.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

4 hoursProbability & StatisticsEvan Kramercourses

Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

5 hoursProbability & StatisticsIta Cirovic Donevcourses

In this course you'll learn how to perform inference using linear models.

4 hoursProbability & StatisticsJo Hardincourses

Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

4 hoursProbability & StatisticsBex Tuychiyevcourses

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!

4 hoursProbability & StatisticsHeidi Seiboldcourses

Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

4 hoursProbability & StatisticsJAMES CURLEYcourses

Explore Linear Regression in a tidy framework.

Learn about experimental design, and how to explore your data to ask and answer meaningful questions.

4 hoursProbability & StatisticsLuke Haydencourses

Learn how to create and assess measurement models used to confirm the structure of a scale or questionnaire.

4 hoursProbability & StatisticsErin Buchanancourses

Use survival analysis to work with time-to-event data and predict survival time.

4 hoursProbability & StatisticsShae Wangcourses

In this course you'll learn how to leverage statistical techniques for working with categorical data.

4 hoursProbability & StatisticsAndrew Braycourses

Prepare for your next statistics interview by reviewing concepts like conditional probabilities, A/B testing, the bias-variance tradeoff, and more.

4 hoursProbability & StatisticsConor Deweycourses

Learn to solve increasingly complex problems using simulations to generate and analyze data.

4 hoursProbability & StatisticsTushar Shankercourses

Learn how to analyze survey data with Python and discover when it is appropriate to apply statistical tools that are descriptive and inferential in nature.

4 hoursProbability & StatisticsEbunOluwa Andrewcourses

Learn how to analyze business processes in R and extract actionable insights from enormous sets of event data.

4 hoursProbability & StatisticsGert Janssenswillencourses

Learn A/B testing: including hypothesis testing, experimental design, and confounding variables.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Learn how to leverage Bayesian estimation methods to make better inferences about linear regression models.

4 hoursProbability & StatisticsJake Thompsoncourses

Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

4 hoursProbability & StatisticsJustin Boiscourses

Analyze time series graphs, use bipartite graphs, and gain the skills to tackle advanced problems in network analytics.

4 hoursProbability & StatisticsEric Macourses

Learn to analyze, plot, and model multivariate data.

4 hoursProbability & StatisticsSurajit Raycourses

Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

4 hoursProbability & StatisticsPaul Savalacourses

Apply fundamental concepts in network analysis to large real-world datasets in 4 different case studies.

4 hoursProbability & StatisticsTed Hartcourses

Learn to analyze and model customer choice data in R.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

In this course, you'll learn how to implement more advanced Bayesian models using RJAGS.

4 hoursProbability & StatisticsAlicia Johnsoncourses

Learn to predict labels of nodes in networks using network learning and by extracting descriptive features from the network

4 hoursProbability & StatisticsBart Baesenscourses

Learn to build simple models of market response to increase the effectiveness of your marketing plans.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

Learn statistical tests for identifying outliers and how to use sophisticated anomaly scoring algorithms.

4 hoursProbability & StatisticsDataCamp Content Creatorcourses

In this course, you'll prepare for the most frequently covered statistical topics from distributions to hypothesis testing, regression models, and much more.

4 hoursProbability & StatisticsZuzanna Chmielewskacourses

Learn mixture models: a convenient and formal statistical framework for probabilistic clustering and classification.

4 hoursProbability & StatisticsVictor Medinacourses

Learn strategies for answering probability questions in R by solving a variety of probability puzzles.

4 hoursProbability & StatisticsPeter Chicourses