Об этом курсе: 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.
Автор: Johns Hopkins University
Преподаватели: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
Преподаватели: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Преподаватели: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Основные сведения | Курс 6 из 10 — Data Science Specialization |
| Язык | English |
| Как пройти курс | Чтобы пройти курс, выполните все оцениваемые задания. |
| Оценки пользователей |
Программа курса
НЕДЕЛЯ 1
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 видео, 11 материала для самостоятельного изучения
- 阅读: Welcome to Statistical Inference
- 阅读: Some introductory comments
- 阅读: Pre-Course Survey
- 阅读: Syllabus
- 阅读: Course Book: Statistical Inference for Data Science
- 阅读: Data Science Specialization Community Site
- 阅读: Homework Problems
- 阅读: Probability
- Видео: 02 01 Introduction to probability
- Видео: 02 02 Probability mass functions
- Видео: 02 03 Probability density functions
- 阅读: Conditional probability
- Видео: 03 01 Conditional Probability
- Видео: 03 02 Bayes' rule
- Видео: 03 03 Independence
- 阅读: Expected values
- Видео: 04 01 Expected values
- Видео: 04 02 Expected values, simple examples
- Видео: 04 03 Expected values for PDFs
- 阅读: Practical R Exercises in swirl 1
- 未分级程序开发: swirl Lesson 1: Introduction
- 未分级程序开发: swirl Lesson 2: Probability1
- 未分级程序开发: swirl Lesson 3: Probability2
- 未分级程序开发: swirl Lesson 4: ConditionalProbability
- 未分级程序开发: swirl Lesson 5: Expectations
Оцениваемый: Quiz 1
НЕДЕЛЯ 2
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 видео, 4 материала для самостоятельного изучения
- 阅读: Variability
- Видео: 05 02 Variance simulation examples
- Видео: 05 03 Standard error of the mean
- Видео: 05 04 Variance data example
- 阅读: Distributions
- Видео: 06 01 Binomial distrubtion
- Видео: 06 02 Normal distribution
- Видео: 06 03 Poisson
- 阅读: Asymptotics
- Видео: 07 01 Asymptotics and LLN
- Видео: 07 02 Asymptotics and the CLT
- Видео: 07 03 Asymptotics and confidence intervals
- 阅读: Practical R Exercises in swirl Part 2
- 未分级程序开发: swirl Lesson 1: Variance
- 未分级程序开发: swirl Lesson 2: CommonDistros
- 未分级程序开发: swirl Lesson 3: Asymptotics
Оцениваемый: Quiz 2
НЕДЕЛЯ 3
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 видео, 5 материала для самостоятельного изучения
- 阅读: Confidence intervals
- Видео: 08 02 T confidence intervals example
- Видео: 08 03 Independent group T intervals
- Видео: 08 04 A note on unequal variance
- 阅读: Hypothesis testing
- Видео: 09 01 Hypothesis testing
- Видео: 09 02 Example of choosing a rejection region
- Видео: 09 03 T tests
- Видео: 09 04 Two group testing
- 阅读: P-values
- Видео: 10 01 Pvalues
- Видео: 10 02 Pvalue further examples
- 阅读: Knitr
- Видео: Just enough knitr to do the project
- 阅读: Practical R Exercises in swirl Part 3
- 未分级程序开发: swirl Lesson 1: T Confidence Intervals
- 未分级程序开发: swirl Lesson 2: Hypothesis Testing
- 未分级程序开发: swirl Lesson 3: P Values
Оцениваемый: Quiz 3
НЕДЕЛЯ 4
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 видео, 4 материала для самостоятельного изучения
- 阅读: Power
- Видео: 11 02 Calculating Power
- Видео: 11 03 Notes on power
- Видео: 11 04 T test power
- Видео: 12 01 Multiple Comparisons
- 阅读: Resampling
- Видео: 13 01 Bootstrapping
- Видео: 13 02 Bootstrapping example
- Видео: 13 03 Notes on the bootstrap
- Видео: 13 04 Permutation tests
- 阅读: Practical R Exercises in swirl Part 4
- 未分级程序开发: swirl Lesson 1: Power
- 未分级程序开发: swirl Lesson 2: Multiple Testing
- 未分级程序开发: swirl Lesson 3: Resampling
- 阅读: Post-Course Survey
Оцениваемый: Quiz 4
Оцениваемый: Statistical Inference Course Project
Часто задаваемые вопросы
Как это работает
课程作业
每门课程都像是一本互动的教科书，具有预先录制的视频、测验和项目。
来自同学的帮助
与其他成千上万的学生相联系，对想法进行辩论，讨论课程材料，并寻求帮助来掌握概念。
证书
获得正式认证的作业，并与朋友、同事和雇主分享您的成功。
Авторы
Johns Hopkins University
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.
Стоимость
| Приобрести курс | |
|---|---|
| Получить доступ к материалам курса | Доступен |
| Получить доступ к оцениваемым материалам курса | Доступен |
| Получить итоговую оценку | Доступен |
| Получите сертификат о прохождении курса, ссылкой на который можно делиться с другими людьми | Доступен |
Рейтинги и отзывы
I loved this class
This class is extremely useful as it gives me a solid basis of the statistical concepts that will be used in the course later.
Good Course for Data science.
Coursera делает лучшее в мире образование доступным каждому, предлагая онлайн-курсы от ведущих университетов и организаций.
© Coursera Inc., 2018 Все права защищены.
KK
If you have taken Statistics before it may help you move through this course and meet deadlines, or you will need to set aside a large amount of time daily to learn. The forums are a necessary supplement to understanding the details of the project assignments and quizzes. Rigorous and rewarding when you put the work in.