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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Приобретаемые навыки
StatisticsR ProgrammingRstudioExploratory Data Analysis
Карьерные результаты учащихся
33%
начал новую карьеру, пройдя эти курсы
30%
получил значимые преимущества в карьере благодаря этому курсу
11%
стал больше зарабатывать или получил повышение
100% онлайн
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Курс 1 из 5 в программе
Гибкие сроки
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Начальный уровень
Прибл. 22 часа на выполнение
Предполагаемая нагрузка: 5 weeks of study, 5-7 hours/week
Английский
Субтитры: Английский, Корейский
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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.
Программа курса: что вы изучите
12 минуты на завершение
About Introduction to Probability and Data
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.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 OpenIntro Statistics, 3rd Edition, https://leanpub.com/openintro-statistics/, (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. 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 resource page (https://www.coursera.org/learn/probability-intro/resources/crMc4) listing useful resources for this course. 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.
12 минуты на завершение
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
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 this module's forum (https://www.coursera.org/learn/probability-intro/module/rQ9Al/discussions?sort=lastActivityAtDesc&page=1) and discuss with your peers! To get started, view the learning objectives (https://www.coursera.org/learn/probability-intro/supplement/rooeY/lesson-learning-objectives) of Lesson 1 in this module.
1 ч. на завершение
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.
1 ч. на завершение
2 материалов для самостоятельного изучения
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
2 ч. на завершение
Exploratory Data Analysis and Introduction to Inference
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.
2 ч. на завершение
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.
1 ч. на завершение
2 материалов для самостоятельного изучения
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
2 ч. на завершение
Introduction to Probability
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. 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.
2 ч. на завершение
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.
1 ч. на завершение
2 материалов для самостоятельного изучения
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
2 ч. на завершение
Probability Distributions
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 no labs for this week. Please don't hesitate to post any questions, discussions and related topics on this week's forum (https://www.coursera.org/learn/probability-intro/module/VdVNg/discussions?sort=lastActivityAtDesc&page=1).
2 ч. на завершение
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
Рецензии
Лучшие отзывы о курсе INTRODUCTION TO PROBABILITY AND DATA
от партнера AAJan 23, 2018
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.
от партнера HDMar 30, 2018
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.
от партнера BBSep 3, 2019
Very clearly explained and the pace is awesome! I really enjoy each deadline and l can already see how it is impacting my day to day work and life. I ook forward to completing the course! Thank you.
от партнера MMJul 5, 2019
Good but questions lacked clarity in what is expected. i.e. "Work out the Boy to Girl ratio" - in what fashion do we do this, as it appeared to be the same as simply working out the "proportion of b
от партнера AAJun 9, 2017
A good introductory course to data analysis, statistics and the R programming language. Recommended to people who are new to data analysis and also those who are experienced and could use a refresh.
от партнера JHMar 26, 2020
The instructions for the final project need to be much clearer. I had a hard time figuring it out, and all of the projects I peer-edited were done poorly. Otherwise, I enjoyed the course very much!
от партнера HWDec 13, 2017
The lectures were very clear and concise and the examples were very relevant. Some of the R instructions left a little something to be desired, but nothing a little time and google couldn't solve.
от партнера RRSep 17, 2016
Really good content and the teacher is one of the best in Coursera. This is for many people a difficult subject that is made easy to digest. Looking forward to more courses from the same Teacher
от партнера SGJun 18, 2019
The contents of the course about statistics are friendly to the beginners and easy to understand, however, the R learning is a little bit hard to those who have no computer or coding background.
от партнера SSJul 26, 2017
Great course! Explained the concepts so clear and crisp and the exercises with R are great. The project reinforces all the concepts. All in all, a great course for beginners in statistics and R.
от партнера HBOct 7, 2018
Great course - great guidance through RStudio coding. Would be great if the instructor could slow down a bit during lectures to make taking notes easier. Otherwise very happy with the course.
от партнера KNJun 3, 2018
The course is pretty nice, I learned some new statistics concepts although the knowledge is not so in-depth. The course also needs more tutorials on R. However, the assignments are quite good
от партнера CDOct 10, 2016
excellent course. lots of material to work off of. I wish there were more tutorials for the R language! I would love to learn more of the capabilities of the program through this online tool.
от партнера SNMar 11, 2019
This course was quite helpful for someone who like me who doesn't have a strong understanding of statistics. The highlight of the course was learning a new data analysis tool - R and RStudio.
от партнера GLJun 16, 2019
The final project is quite challenging but I have learned so much from this course. The lecture is great and the professor explains each concept well with examples. Really worthy of taking!!!
от партнера TCApr 29, 2017
Best statistical course ever taken!\n\nClear explanation with practical examples and background on the basis of the statistics applied. Highly recommend to anyone interested in statistics!
от партнера BRMay 11, 2016
This is a very accessible intro to statistical analysis, light on math but heavy on intuition, and the R programming labs are a superb way to practice and really learn to apply the tools.
от партнера RMJan 15, 2019
This was an excellent course, I found it easy to follow and I learned a lot that pertains directly to my career. I look forward to the other courses in the Statistics with R certificate
от партнера BBAug 20, 2017
It was a great course. The videos were clear in the content and ideas. I personally struggled coming up with research questions for the project. But it was a great learning experience.
от партнера DSSep 16, 2019
The videos of the course should show more R coding.\n\nThe assignments are too long, they take ages to review.\n\nThe explanation of the statistical concepts are excellent! Great job!
Специализация Statistics with R: общие сведения
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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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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