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.
提供：
このコースについて
324,520 最近の表示
受講生の就業成果
33%
コース終了後に新しいキャリアをスタートした
30%
コースが具体的なキャリアアップにつながった
11%
昇給や昇進につながった
習得するスキル
StatisticsR ProgrammingRstudioExploratory Data Analysis
受講生の就業成果
33%
コース終了後に新しいキャリアをスタートした
30%
コースが具体的なキャリアアップにつながった
11%
昇給や昇進につながった
100%オンライン
自分のスケジュールですぐに学習を始めてください。
次における5の1コース
柔軟性のある期限
スケジュールに従って期限をリセットします。
初級レベル
約22時間で修了
英語
字幕：英語, 韓国語
提供：
デューク大学（Duke University）
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.
シラバス - 本コースの学習内容
12分で修了
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分で修了
1件のビデオ （合計2分）, 1 reading
1件のビデオ
1件の学習用教材
More about Introduction to Probability and Data10 分
1時間で修了
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時間で修了
6件のビデオ （合計28分）, 2 readings, 2 quizzes
6件のビデオ
Introduction3 分
Data Basics5 分
Observational Studies & Experiments4 分
Sampling and sources of bias8 分
Experimental Design2 分
(Spotlight) Random Sample Assignment3 分
2件の学習用教材
Lesson Learning Objectives10 分
Suggested Readings and Practice10 分
2の練習問題
Week 1 Practice Quiz10 分
Week 1 Quiz14 分
1時間で修了
Introduction to Data Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1時間で修了
2 readings
2件の学習用教材
About Lab Choices (Read Before Selection)10 分
Week 1 Lab Instructions (RStudio)10 分
1の練習問題
Week 1 Lab: Introduction to R and RStudio16 分
2時間で修了
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時間で修了
7件のビデオ （合計46分）, 3 readings, 2 quizzes
7件のビデオ
Measures of Center4 分
Measures of Spread6 分
Robust Statistics1 分
Transforming Data3 分
Exploring Categorical Variables8 分
Introduction to Inference12 分
3件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Suggested Readings and Practice10 分
2の練習問題
Week 2 Practice Quiz10 分
Week 2 Quiz12 分
1時間で修了
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時間で修了
2 readings
2件の学習用教材
Week 2 Lab Instructions (RStudio)10 分
Week 2 Lab Instructions (RStudio Cloud)10 分
1の練習問題
Week 2 Lab: Introduction to Data20 分
2時間で修了
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時間で修了
9件のビデオ （合計82分）, 3 readings, 2 quizzes
9件のビデオ
Introduction5 分
Disjoint Events + General Addition Rule9 分
Independence9 分
Probability Examples9 分
(Spotlight) Disjoint vs. Independent2 分
Conditional Probability12 分
Probability Trees10 分
Bayesian Inference14 分
Examples of Bayesian Inference7 分
3件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Suggested Readings and Practice10 分
2の練習問題
Week 3 Practice Quiz6 分
Week 3 Quiz10 分
1時間で修了
Introduction to Probability Project
To complete this assignment you will use R and RStudio installed on your local computer or through RStudio Cloud.
1時間で修了
2 readings
2件の学習用教材
Week 3 Lab Instructions (RStudio)10 分
Week 3 Lab Instructions (RStudio Cloud)10 分
1の練習問題
Week 3 Lab: Probability10 分
2時間で修了
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時間で修了
6件のビデオ （合計67分）, 4 readings, 2 quizzes
6件のビデオ
Evaluating the Normal Distribution2 分
Working with the Normal Distribution5 分
Binomial Distribution17 分
Normal Approximation to Binomial14 分
Working with the Binomial Distribution9 分
4件の学習用教材
Lesson Learning Objectives10 分
Lesson Learning Objectives10 分
Suggested Readings and Practice10 分
Data Analysis Project Example10 分
2の練習問題
Week 4 Practice Quiz14 分
Week 4 Quiz14 分
4.7
804件のレビューIntroduction to Probability and Data からの人気レビュー
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.
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.
Statistics with R専門講座について
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.
よくある質問
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この専門講座をサブスクライブすると何を行うことができるようになりますか？
コースに登録する際、専門講座のすべてのコースにアクセスできます。コースの完了時には修了証を取得できます。電子修了証が成果のページに追加され、そこから修了証を印刷したり、LinkedInのプロフィールに追加したりできます。コースの内容の閲覧のみを希望する場合は、無料でコースを聴講できます。
返金ポリシーについて教えてください。
学資援助はありますか？
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.
さらに質問がある場合は、受講者向けヘルプセンターにアクセスしてください。
