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Probability & Statistics courses

Probability and statistics courses explore mathematical concepts for analyzing random events and interpreting data through models and inference. Use tools such as Python, R, Excel and Google Sheets to apply your theoretical knowledge in statistics.

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Recommended for Probability & Statistics beginners

Build your Probability & Statistics skills with interactive courses, curated by real-world experts

Kurs

Introduction to Statistics in R

MedelnivåKunskapsnivå
4.7+
2 022 recensioner
4 tim
Grow your statistical skills and learn how to collect, analyze, and draw accurate conclusions from data.

Lärstig

Statistiker i R

4.5+
7 recensioner
52 tim
En statistiker samlar in och analyserar data och hjälper företag att förstå kvantitativa data, inklusive att upptäcka trender och göra förutsägelser.

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Utforska kurser och inlärningsvägar inom Probability & Statistics

Kurs

Anomaly Detection in Python

MedelnivåKunskapsnivå
4.8+
174 recensioner
4 tim
Detect anomalies in your data analysis and expand your Python statistical toolkit in this four-hour course.

Kurs

Factor Analysis in R

AvanceradKunskapsnivå
4.7+
145 recensioner
4 tim
Explore latent variables, such as personality, using exploratory and confirmatory factor analyses.

Kurs

Bayesian Data Analysis in Python

MedelnivåKunskapsnivå
4.7+
252 recensioner
4 tim
Learn all about the advantages of Bayesian data analysis, and apply it to a variety of real-world use cases!

Kurs

Introduction to Network Analysis in Python

MedelnivåKunskapsnivå
4.7+
208 recensioner
4 tim
This course will equip you with the skills to analyze, visualize, and make sense of networks using the NetworkX library.

Kurs

Analyzing Survey Data in R

MedelnivåKunskapsnivå
4.8+
209 recensioner
4 tim
Learn survey design using common design structures followed by visualizing and analyzing survey results.

Kurs

Introduction to Linear Modeling in Python

MedelnivåKunskapsnivå
4.7+
213 recensioner
4 tim
Explore the concepts and applications of linear models with python and build models to describe, predict, and extract insight from data patterns.

Kurs

Statistical Thinking in Python (Part 2)

MedelnivåKunskapsnivå
4.8+
245 recensioner
4 tim
Learn to perform the two key tasks in statistical inference: parameter estimation and hypothesis testing.

Kurs

A/B Testing in R

MedelnivåKunskapsnivå
4.8+
89 recensioner
4 tim
Learn the basics of A/B testing in R, including how to design experiments, analyze data, predict outcomes, and present results through visualizations.

Kurs

Inference for Numerical Data in R

AvanceradKunskapsnivå
4.8+
100 recensioner
4 tim
In this course youll learn techniques for performing statistical inference on numerical data.

Kurs

Survival Analysis in R

MedelnivåKunskapsnivå
4.7+
186 recensioner
4 tim
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!

Kurs

Network Analysis in R

MedelnivåKunskapsnivå
4.7+
120 recensioner
4 tim
Learn to analyze and visualize network data with the igraph package and create interactive network plots with threejs.

Kurs

Inference for Categorical Data in R

AvanceradKunskapsnivå
4.8+
107 recensioner
4 tim
In this course youll learn how to leverage statistical techniques for working with categorical data.

Kurs

Advanced Probability: Uncertainty in Data

AvanceradKunskapsnivå
4.8+
146 recensioner
2 tim
Develop a better intuition for advanced probability, risk assessment, and simulation techniques to make data-driven business decisions with confidence.

Kurs

Generalized Linear Models in Python

AvanceradKunskapsnivå
4.7+
143 recensioner
5 tim
Extend your regression toolbox with the logistic and Poisson models and learn to train, understand, and validate them, as well as to make predictions.

Kurs

Foundations of Inference in Python

AvanceradKunskapsnivå
4.8+
217 recensioner
4 tim
Get hands-on experience making sound conclusions based on data in this four-hour course on statistical inference in Python.

Kurs

Error and Uncertainty in Google Sheets

MedelnivåKunskapsnivå
4.7+
138 recensioner
4 tim
Learn to distinguish real differences from random noise, and explore psychological crutches we use that interfere with our rational decision making.

Kurs

Statistical Simulation in Python

MedelnivåKunskapsnivå
4.8+
28 recensioner
4 tim
Learn to solve increasingly complex problems using simulations to generate and analyze data.

Kurs

Survival Analysis in Python

AvanceradKunskapsnivå
4.7+
71 recensioner
4 tim
Use survival analysis to work with time-to-event data and predict survival time.

Kurs

Discrete Event Simulation in Python

AvanceradKunskapsnivå
4.7+
68 recensioner
4 tim
Discover the power of discrete-event simulation in optimizing your business processes. Learn to develop digital twins using Pythons SimPy package.

Kurs

Case Studies in Statistical Thinking

MedelnivåKunskapsnivå
4.9+
79 recensioner
4 tim
Take vital steps towards mastery as you apply your statistical thinking skills to real-world data sets and extract actionable insights from them.

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Frequently asked questions

How does probability and statistics related to data science?

Probability and statistics are foundational to data science, offering the tools and frameworks necessary for analyzing data, making predictions, and deriving meaningful insights. They enable data scientists to understand patterns, assess uncertainties, and make informed decisions based on data analysis.

Why is it important to develop knowledge in probability and statistics?

Developing knowledge in probability and statistics is crucial for effectively interpreting data and making reliable predictions. This understanding forms the basis for designing experiments, analyzing results, and validating conclusions in various fields, ensuring decisions are data-driven and evidence-based.

What careers can I pursue with knowledge in probability and statistics?

With knowledge in probability and statistics, you can pursue a wide array of careers such as data scientist, market researcher, machine learning engineer, statistical analyst, and risk manager. These roles span various industries including finance, healthcare, technology, and government, where interpreting data and making evidence-based decisions are key.

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