Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Informações sobre o curso
4.2
3,035 classificações
O que você vai aprender
Describe variability, distributions, limits, and confidence intervals
Make informed data analysis decisions
Understand the process of drawing conclusions about populations or scientific truths from data
Use p-values, confidence intervals, and permutation tests
Habilidades que você terá
StatisticsStatistical InferenceStatistical Hypothesis Testing
Programa - O que você aprenderá com este curso
Semana
118 horas para concluir
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 vídeos (total de (Total 64 mín.) min), 11 leituras, 6 testes
10 videos
02 01 Introduction to probability6min
02 02 Probability mass functions7min
02 03 Probability density functions13min
03 01 Conditional Probability3min
03 02 Bayes' rule7min
03 03 Independence3min
04 01 Expected values5min
04 02 Expected values, simple examples2min
04 03 Expected values for PDFs7min
11 leituras
Welcome to Statistical Inference10min
Some introductory comments10min
Pre-Course Survey10min
Syllabus10min
Course Book: Statistical Inference for Data Science10min
Data Science Specialization Community Site10min
Homework Problems10min
Probability10min
Conditional probability10min
Expected values10min
Practical R Exercises in swirl 110min
1 exercício prático
Quiz 112min
Semana
211 horas para concluir
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 vídeos (total de (Total 76 mín.) min), 4 leituras, 4 testes
10 videos
05 02 Variance simulation examples2min
05 03 Standard error of the mean7min
05 04 Variance data example3min
06 01 Binomial distrubtion3min
06 02 Normal distribution15min
06 03 Poisson6min
07 01 Asymptotics and LLN4min
07 02 Asymptotics and the CLT8min
07 03 Asymptotics and confidence intervals20min
4 leituras
Variability10min
Distributions10min
Asymptotics10min
Practical R Exercises in swirl Part 210min
1 exercício prático
Quiz 216min
Semana
311 horas para concluir
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 vídeos (total de (Total 83 mín.) min), 5 leituras, 4 testes
11 videos
08 02 T confidence intervals example4min
08 03 Independent group T intervals14min
08 04 A note on unequal variance3min
09 01 Hypothesis testing4min
09 02 Example of choosing a rejection region5min
09 03 T tests7min
09 04 Two group testing17min
10 01 Pvalues7min
10 02 Pvalue further examples5min
Just enough knitr to do the project3min
5 leituras
Confidence intervals10min
Hypothesis testing10min
P-values10min
Knitr10min
Practical R Exercises in swirl Part 310min
1 exercício prático
Quiz 314min
Semana
413 horas para concluir
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 vídeos (total de (Total 86 mín.) min), 4 leituras, 5 testes
9 videos
11 01 Power4min
11 02 Calculating Power12min
11 03 Notes on power4min
11 04 T test power8min
12 01 Multiple Comparisons25min
13 01 Bootstrapping7min
13 02 Bootstrapping example3min
13 03 Notes on the bootstrap10min
13 04 Permutation tests9min
4 leituras
Power10min
Resampling10min
Practical R Exercises in swirl Part 410min
Post-Course Survey10min
1 exercício prático
Quiz 418min
40%
comecei uma nova carreira após concluir estes cursos
36%
consegui um benefício significativo de carreira com este curso
Melhores avaliações
por JA•Oct 26th 2018
Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .
por AP•Mar 22nd 2017
The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.
Instrutores
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Sobre Universidade 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.
Sobre o Programa de cursos integrados Ciência de Dados
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Perguntas Frequentes – FAQ
Quando terei acesso às palestras e às tarefas?
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O que recebo ao me inscrever nesta Especialização?
Quando você se inscreve no curso, tem acesso a todos os cursos na Especialização e pode obter um certificado quando concluir o trabalho. Seu Certificado eletrônico será adicionado à sua página de Participações e você poderá imprimi-lo ou adicioná-lo ao seu perfil no LinkedIn. Se quiser apenas ler e assistir o conteúdo do curso, você poderá frequentá-lo como ouvinte sem custo.
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