This course introduces you to sampling and exploring data, as well as basic probability theory and Bayes' rule. You will examine various types of sampling methods, and discuss how such methods can impact the scope of inference. A variety of exploratory data analysis techniques will be covered, including numeric summary statistics and basic data visualization. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The concepts and techniques in this course will serve as building blocks for the inference and modeling courses in the Specialization.
Этот курс входит в специализацию ''Специализация Statistics with R'
Introduction to Probability and Data
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Об этом курсе
4.7
Оценки: 2,897
Приобретаемые навыки
StatisticsR ProgrammingRstudioExploratory Data Analysis
Программа курса: что вы изучите
Неделя
112 минуты на завершение
About Introduction to Probability and Data
<p>This course introduces you to sampling and exploring data, as well as basic probability theory. You will examine various types of sampling methods and discuss how such methods can impact the utility of a data analysis. The concepts in this module will serve as building blocks for our later courses.<p>Each lesson comes with a set of learning objectives that will be covered in a series of short videos. Supplementary readings and practice problems will also be suggested from <a href="https://leanpub.com/openintro-statistics/" target="_blank">OpenIntro Statistics, 3rd Edition</a> (a free online introductory statistics textbook, that I co-authored). There will be weekly quizzes designed to assess your learning and mastery of the material covered that week in the videos. In addition, each week will also feature a lab assignment, in which you will use R to apply what you are learning to real data. There will also be a data analysis project designed to enable you to answer research questions of your own choosing.<p>Since this is a Coursera course, you are welcome to participate as much or as little as you’d like, though I hope that you will begin by participating fully. One of the most rewarding aspects of a Coursera course is participation in forum discussions about the course materials. Please take advantage of other students' feedback and insight and contribute your own perspective where you see fit to do so. You can also check out the <a href="https://www.coursera.org/learn/probability-intro/resources/crMc4" target="_blank">resource page</a> listing useful resources for this course. <p>Thank you for joining the Introduction to Probability and Data community! Say hello in the Discussion Forums. We are looking forward to your participation in the course.</p>
1 видео ((всего 2 мин.)), 1 материал для самостоятельного изучения
1 видео
1 материал для самостоятельного изучения
More about Introduction to Probability and Data10мин
1 ч. на завершение
Introduction to Data
<p>Welcome to Introduction to Probability and Data! I hope you are just as excited about this course as I am! In the next five weeks, we will learn about designing studies, explore data via numerical summaries and visualizations, and learn about rules of probability and commonly used probability distributions. If you have any questions, feel free to post them on <a href="https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this module's forum</b></a> and discuss with your peers! To get started, view the <a href="https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives" target="_blank"><b>learning objectives</b></a> of Lesson 1 in this module.</p>
6 видео ((всего 28 мин.)), 2 материалов для самостоятельного изучения, 2 тестов
6 видео
Introduction3мин
Data Basics5мин
Observational Studies & Experiments4мин
Sampling and sources of bias8мин
Experimental Design2мин
(Spotlight) Random Sample Assignment3мин
2 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Suggested Readings and Practice10мин
2 практического упражнения
Week 1 Practice Quiz10мин
Week 1 Quiz14мин
1 ч. на завершение
Introduction to Data Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 материалов для самостоятельного изучения, 1 тест
2 материала для самостоятельного изучения
About Lab Choices (Read Before Selection)10мин
Week 1 Lab Instructions (RStudio)10мин
1 практическое упражнение
Week 1 Lab: Introduction to R and RStudio16мин
Неделя
22 ч. на завершение
Exploratory Data Analysis and Introduction to Inference
<p>Welcome to Week 2 of Introduction to Probability and Data! Hope you enjoyed materials from Week 1. This week we will delve into numerical and categorical data in more depth, and introduce inference. </p>
7 видео ((всего 46 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
7 видео
Measures of Center4мин
Measures of Spread6мин
Robust Statistics1мин
Transforming Data3мин
Exploring Categorical Variables8мин
Introduction to Inference12мин
3 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Suggested Readings and Practice10мин
2 практического упражнения
Week 2 Practice Quiz10мин
Week 2 Quiz12мин
1 ч. на завершение
Exploratory Data Analysis and Introduction to Inference Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 материалов для самостоятельного изучения, 1 тест
2 материала для самостоятельного изучения
Week 2 Lab Instructions (RStudio)10мин
Week 2 Lab Instructions (RStudio Cloud)10мин
1 практическое упражнение
Week 2 Lab: Introduction to Data20мин
Неделя
32 ч. на завершение
Introduction to Probability
<p>Welcome to Week 3 of Introduction to Probability and Data! Last week we explored numerical and categorical data. This week we will discuss probability, conditional probability, the Bayes’ theorem, and provide a light introduction to Bayesian inference. </p><p>Thank you for your enthusiasm and participation, and have a great week! I’m looking forward to working with you on the rest of this course. </p>
9 видео ((всего 82 мин.)), 3 материалов для самостоятельного изучения, 2 тестов
9 видео
Introduction5мин
Disjoint Events + General Addition Rule9мин
Independence9мин
Probability Examples9мин
(Spotlight) Disjoint vs. Independent2мин
Conditional Probability12мин
Probability Trees10мин
Bayesian Inference14мин
Examples of Bayesian Inference7мин
3 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Suggested Readings and Practice10мин
2 практического упражнения
Week 3 Practice Quiz6мин
Week 3 Quiz10мин
1 ч. на завершение
Introduction to Probability Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
2 материалов для самостоятельного изучения, 1 тест
2 материала для самостоятельного изучения
Week 3 Lab Instructions (RStudio)10мин
Week 3 Lab Instructions (RStudio Cloud)10мин
1 практическое упражнение
Week 3 Lab: Probability10мин
Неделя
42 ч. на завершение
Probability Distributions
<p>Great work so far! Welcome to Week 4 -- the last content week of Introduction to Probability and Data! This week we will introduce two probability distributions: the normal and the binomial distributions in particular. As usual, you can evaluate your knowledge in this week's quiz. There will be <b>no labs</b> for this week. Please don't hesitate to post any questions, discussions and related topics on <a href="https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1" target="_blank"><b>this week's forum</b></a>.</p>
6 видео ((всего 67 мин.)), 4 материалов для самостоятельного изучения, 2 тестов
6 видео
Normal Distribution17мин
Evaluating the Normal Distribution2мин
Working with the Normal Distribution5мин
Binomial Distribution17мин
Normal Approximation to Binomial14мин
Working with the Binomial Distribution9мин
4 материала для самостоятельного изучения
Lesson Learning Objectives10мин
Lesson Learning Objectives10мин
Suggested Readings and Practice10мин
Data Analysis Project Example10мин
2 практического упражнения
Week 4 Practice Quiz14мин
Week 4 Quiz14мин
34%
начал новую карьеру, пройдя эти курсы
28%
получил значимые преимущества в карьере благодаря этому курсу
Лучшие рецензии
This course literally taught me a lot, the concepts were beautifully explained but the way it was delivered and overall exercises and the difficulty of problems made it more challenging and enjoying.
The tutor makes it really simple. The given examples really helped to understand the concepts and apply it to a wide range of problems. Thank you for this. Wish I could complete the assignments too.
Преподаватели
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
О Университет Дьюка
Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.
О специализации ''Statistics with R'
In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
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Зарегистрировавшись на сертификацию, вы получите доступ ко всем видео, тестам и заданиям по программированию (если они предусмотрены). Задания по взаимной оценке сокурсниками можно сдавать и проверять только после начала сессии. Если вы проходите курс без оплаты, некоторые задания могут быть недоступны.
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Записавшись на курс, вы получите доступ ко всем курсам в специализации, а также возможность получить сертификат о его прохождении. После успешного прохождения курса на странице ваших достижений появится электронный сертификат. Оттуда его можно распечатать или прикрепить к профилю LinkedIn. Просто ознакомиться с содержанием курса можно бесплатно.
Какие правила возврата средств?
Можно ли получить финансовую помощь?
Will I receive a transcript from Duke University for completing this course?
No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.
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