نظرة عامة على نماذج الانحدار - الأسبوع الأول | Coursera

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نظرة عامة على نماذج الانحدار

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مجموعة أدوات عالم البيانات

Johns Hopkins University

4.2 (5 ratings) | 692 Students Enrolled

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ستتلقى في هذه الدورة التدريبية مقدمة عن الأدوات الرئيسية والأفكار الخاصة بمجموعة أدوات عالم البيانات. تقدم الدورة التدريبية نظرة عامة عن البيانات والاستفسارات والأدوات التي يعمل عليها علماء البيانات ومحللو البيانات. هناك عنصران لهذه الدورة التدريبية. الأول هو مقدمة نظرية عن الأفكار الكامنة وراء تحويل البيانات إلى معلومات قابلة للتطبيق. والثاني هو مقدمة عملية عن الأدوات التي سيتم استخدامها في البرنامج مثل التحكم في النُسَخ ولغة Markdown وGit وGitHub وR وRStudio.

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From the lesson

الأسبوع الأول

ستتعرف خلال الأسبوع الأول على أهداف ومقاصد تخصص علوم البيانات وعلى كل عنصر من عناصره. ستتلقى أيضًا نظرة عامة عن المجال بالإضافة إلى إرشادات حول كيفية تثبيت برنامج R.

دافع التخصص12:03

مجموعة أدوات عالم البيانات5:09

الحصول على مساعدة8:52

العثور على إجابات4:35

نظرة عامة على برمجة R2:12

نظرة عامة على الحصول على البيانات1:34

نظرة عامة على التحليل الاستكشافي للبيانات1:21

نظرة عامة على إعداد بحث قابل للتكرار1:27

نظرة عامة على الاستنتاج الإحصائي1:06

نظرة عامة على نماذج الانحدار1:46

نظرة عامة على التعلم الآلي العملي1:31

نظرة عامة على منتجات بيانات المباني1:19

تثبيت برنامج R على نظام التشغيل Windows‏ {Roger Peng}3:19

تثبيت برنامج R على نظام التشغيل Mac‏ {Roger Peng}2:02

تثبيت برنامج Rstudio‏ {Roger Peng}1:36

تثبيت البرامج الخارجية على نظام التشغيل Mac‏ (نظام التشغيل OS X Mavericks)1:19

Taught By

Jeff Leek, PhD
Associate Professor, Biostatistics
Roger D. Peng, PhD
Associate Professor, Biostatistics
Brian Caffo, PhD
Professor, Biostatistics

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Transcript

One of the most widely used tools for performing any sort of statistical or data science analysis is a regression model. So the next course will be covering those regression models. Some would say it's just one other form of just pre, creating a supervised predictive function. But it's a little bit more than that in that it's, one of the more interpretable and easily used, tools that you can use to sort of explain you analyses. The people that are outside of the data science community. And, as communication is a critical component of data science, that makes regression models a critical component of the tool box. So, this course will cover linear regression and multiple regression, ideas like confounding which we'll see a little bit in this class even, some prediction using linear models. Scatterplots smoothing its splines, and then resampling inference, and maybe weighted regression. So, this will cover also some ideas including ideas that you hear about often when you read articles about statistical analyses in the popular press. Things like regression to the mean. So, why is it that children of tall parents tend to be tall but not as tall as their parents were? So these sorts of fundamental ideas will be explained in the regression class. So we'll also talk a little bit about the basic regression model, there'll be a little bit more mathematics in this class than there are in some of the other classes and sort of deriving and understanding the basic ideas behind a regression model. But calculus and the new algebra are not required. We've worked hard to make it so that, basic understanding of algebra is sufficient to follow this class. We'll also learn about multivariable regression analyses so, sometimes you want to relate one variable to another variable but you want to account for, what happens when you include other variables, adjusting your analysis. You often hear about adjusting the analysis, and that will be covered in this class as well.

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