This class presents the fundamental probability and statistical concepts used in elementary data analysis. It will be taught at an introductory level for students with junior or senior college-level mathematical training including a working knowledge of calculus. A small amount of linear algebra and programming are useful for the class, but not required.
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Spécialisation Advanced Statistics for Data ScienceUniversité Johns-HopkinsÀ propos de ce cours
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Approx. 13 heures pour terminer
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Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Compétences que vous acquerrez
StatisticsConfidence IntervalStatistical Hypothesis TestingBiostatistics
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Cours 1 sur 4 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Approx. 13 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Italien, Vietnamien, Allemand, Russe, Anglais, Espagnol
Offert par
Université Johns-Hopkins
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Programme du cours : ce que vous apprendrez dans ce cours
4 heures pour terminer
Introduction, Probability, Expectations, and Random Vectors
You are about to undergo an intense and demanding immersion into the world of mathematical biostatistics. Over the next few weeks, you will learn about probability, expectations, conditional probabilities, distributions, confidence intervals, bootstrapping, binomial proportions, and much more. Module 1 covers experiments, probability, variables, mass functions, density functions, cumulative distribution functions, expectations, variations, and vectors.
4 heures pour terminer
13 vidéos (Total 179 min), 2 lectures, 2 quiz
4 heures pour terminer
Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics
This module covers Conditional Probability, Bayes' Rule, Likelihood, Distributions, and Asymptotics. These are the most fundamental core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
4 heures pour terminer
7 vidéos (Total 157 min)
3 heures pour terminer
Confidence Intervals, Bootstrapping, and Plotting
This module covers Confidence Intervals, Bootstrapping, and Plotting. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
3 heures pour terminer
7 vidéos (Total 121 min)
2 heures pour terminer
Binomial Proportions and Logs
This module covers Binomial Proportions and Logs. These are core concepts in mathematical biostatistics and statistics. After this module you should be able to recognize and be functional in these key concepts.
2 heures pour terminer
3 vidéos (Total 71 min)
Avis
Meilleurs avis pour BIOSTATISTIQUE MATHÉMATIQUE COURS INTENSIF 1
par DH4 juin 2017
I knew a lot about probability before starting this course, but I didn't know much of anything about frequentist statistics. This course helped me understand some tricky concepts.
par TL15 janv. 2021
Brian Caffo is the best statistics teacher I have ever had. I like how he breaks down things and he covers the ways to think about statistics far beyond any course I have taken.
par PB11 janv. 2016
Very concise, well-presented course. This was my second time taking it as a refresher. Prof. Caffo does a great job presenting the materials. However, prepare to be challenged.
par JB8 juil. 2017
Great course, though a little difficult in parts, particularly the first week. Worth working through though for a better understanding of probability and statistics.
À propos du Spécialisation Advanced Statistics for Data Science
Fundamental concepts in probability, statistics and linear models are primary building blocks for data science work. Learners aspiring to become biostatisticians and data scientists will benefit from the foundational knowledge being offered in this specialization. It will enable the learner to understand the behind-the-scenes mechanism of key modeling tools in data science, like least squares and linear regression.
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