À propos de ce cours : 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.
Créé par : Université Johns-Hopkins
Enseigné par : Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Enseigné par : Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Informations de base | Cours 6 de 10 dans Data Science Specialization |
| Langue | English |
| Comment réussir | Réussissez tous les devoirs notés pour terminer le cours. |
| Notes des utilisateurs |
Programme de cours
SEMAINE 1
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 vidéos, 11 lectures
- Reading: Welcome to Statistical Inference
- Reading: Some introductory comments
- Reading: Pre-Course Survey
- Reading: Syllabus
- Reading: Course Book: Statistical Inference for Data Science
- Reading: Data Science Specialization Community Site
- Reading: Homework Problems
- Reading: Probability
- Vidéo: 02 01 Introduction to probability
- Vidéo: 02 02 Probability mass functions
- Vidéo: 02 03 Probability density functions
- Reading: Conditional probability
- Vidéo: 03 01 Conditional Probability
- Vidéo: 03 02 Bayes' rule
- Vidéo: 03 03 Independence
- Reading: Expected values
- Vidéo: 04 01 Expected values
- Vidéo: 04 02 Expected values, simple examples
- Vidéo: 04 03 Expected values for PDFs
- Reading: Practical R Exercises in swirl 1
- Ungraded Programming: swirl Lesson 1: Introduction
- Ungraded Programming: swirl Lesson 2: Probability1
- Ungraded Programming: swirl Lesson 3: Probability2
- Ungraded Programming: swirl Lesson 4: ConditionalProbability
- Ungraded Programming: swirl Lesson 5: Expectations
Noté: Quiz 1
SEMAINE 2
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 vidéos, 4 lectures
- Reading: Variability
- Vidéo: 05 02 Variance simulation examples
- Vidéo: 05 03 Standard error of the mean
- Vidéo: 05 04 Variance data example
- Reading: Distributions
- Vidéo: 06 01 Binomial distrubtion
- Vidéo: 06 02 Normal distribution
- Vidéo: 06 03 Poisson
- Reading: Asymptotics
- Vidéo: 07 01 Asymptotics and LLN
- Vidéo: 07 02 Asymptotics and the CLT
- Vidéo: 07 03 Asymptotics and confidence intervals
- Reading: Practical R Exercises in swirl Part 2
- Ungraded Programming: swirl Lesson 1: Variance
- Ungraded Programming: swirl Lesson 2: CommonDistros
- Ungraded Programming: swirl Lesson 3: Asymptotics
Noté: Quiz 2
SEMAINE 3
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 vidéos, 5 lectures
- Reading: Confidence intervals
- Vidéo: 08 02 T confidence intervals example
- Vidéo: 08 03 Independent group T intervals
- Vidéo: 08 04 A note on unequal variance
- Reading: Hypothesis testing
- Vidéo: 09 01 Hypothesis testing
- Vidéo: 09 02 Example of choosing a rejection region
- Vidéo: 09 03 T tests
- Vidéo: 09 04 Two group testing
- Reading: P-values
- Vidéo: 10 01 Pvalues
- Vidéo: 10 02 Pvalue further examples
- Reading: Knitr
- Vidéo: Just enough knitr to do the project
- Reading: Practical R Exercises in swirl Part 3
- Ungraded Programming: swirl Lesson 1: T Confidence Intervals
- Ungraded Programming: swirl Lesson 2: Hypothesis Testing
- Ungraded Programming: swirl Lesson 3: P Values
Noté: Quiz 3
SEMAINE 4
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 vidéos, 4 lectures
- Reading: Power
- Vidéo: 11 02 Calculating Power
- Vidéo: 11 03 Notes on power
- Vidéo: 11 04 T test power
- Vidéo: 12 01 Multiple Comparisons
- Reading: Resampling
- Vidéo: 13 01 Bootstrapping
- Vidéo: 13 02 Bootstrapping example
- Vidéo: 13 03 Notes on the bootstrap
- Vidéo: 13 04 Permutation tests
- Reading: Practical R Exercises in swirl Part 4
- Ungraded Programming: swirl Lesson 1: Power
- Ungraded Programming: swirl Lesson 2: Multiple Testing
- Ungraded Programming: swirl Lesson 3: Resampling
- Reading: Post-Course Survey
Noté: Quiz 4
Noté: Statistical Inference Course Project
FAQ
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Créateurs
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.
Tarification
| Acheter le cours | |
|---|---|
| Accéder au contenu du cours | Disponible |
| Accéder au contenu noté | Disponible |
| Recevoir une Note Finale | Disponible |
| Obtenir un Certificat de Cours partageable | Disponible |
Notation et examens
Everything absolutely amazing. Sometimes there were some troubles with audio files, but I think it doesn't affect on the course. Because I have no trouble with understanding. Thank you to al masters!
Provide deep knowledge on inference statistics,
Very intensive and demanding course with interesting examples. Students without previous knowledge in statistics will likely need additional resources to complete the course.
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At this point in the specialization, I was really worn out by the effort that I needed to put into this course (I solved the homework questions too). While I have no problem with the math, some topics like power should not even have been discussed or should have just been discussed in passing. Caffo spent a whole week on that. After taking the Applied Data Science in Python Specialization, I have a feeling like this course and Regression Models can just be merged, while logistics regression could just be transferred to the machine learning course.
Fortunately, the final assignment was very easy compared to the previous courses and one could finish it reasonably in a day (Reproducible Research final assignment itself took me almost a week) .