In this course you will learn how to program in R and how to use R for effective data analysis. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language. The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. Topics in statistical data analysis will provide working examples.
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Université Johns-HopkinsÀ propos de ce cours
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Résultats de carrière des étudiants
29%
ont commencé une nouvelle carrière après avoir terminé ce cours
30%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
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Niveau intermédiaire
Familiarity with regression is recommended.
Approx. 57 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Portugais (brésilien), Vietnamien, Allemand, Russe, Anglais, Espagnol, Japonais
Ce que vous allez apprendre
Understand critical programming language concepts
Configure statistical programming software
Make use of R loop functions and debugging tools
Collect detailed information using R profiler
Compétences que vous acquerrez
Data AnalysisDebuggingR ProgrammingRstudio
Résultats de carrière des étudiants
29%
ont commencé une nouvelle carrière après avoir terminé ce cours
30%
ont bénéficié d'un avantage concret dans leur carrières grâce à ce cours
11%
a obtenu une augmentation de salaire ou une promotion
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Familiarity with regression is recommended.
Approx. 57 heures pour terminer
Anglais
Sous-titres : Arabe, Français, Portugais (européen), Chinois (simplifié), Italien, Portugais (brésilien), Vietnamien, Allemand, Russe, Anglais, Espagnol, Japonais
Offert par
Université Johns-Hopkins
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.
Programme du cours : ce que vous apprendrez dans ce cours
25 heures pour terminer
Week 1: Background, Getting Started, and Nuts & Bolts
This week covers the basics to get you started up with R. The Background Materials lesson contains information about course mechanics and some videos on installing R. The Week 1 videos cover the history of R and S, go over the basic data types in R, and describe the functions for reading and writing data. I recommend that you watch the videos in the listed order, but watching the videos out of order isn't going to ruin the story.
25 heures pour terminer
28 vidéos (Total 129 min), 9 lectures, 8 quiz
12 heures pour terminer
Week 2: Programming with R
Welcome to Week 2 of R Programming. This week, we take the gloves off, and the lectures cover key topics like control structures and functions. We also introduce the first programming assignment for the course, which is due at the end of the week.
12 heures pour terminer
13 vidéos (Total 91 min), 3 lectures, 5 quiz
9 heures pour terminer
Week 3: Loop Functions and Debugging
We have now entered the third week of R Programming, which also marks the halfway point. The lectures this week cover loop functions and the debugging tools in R. These aspects of R make R useful for both interactive work and writing longer code, and so they are commonly used in practice.
9 heures pour terminer
8 vidéos (Total 61 min), 2 lectures, 4 quiz
11 heures pour terminer
Week 4: Simulation & Profiling
This week covers how to simulate data in R, which serves as the basis for doing simulation studies. We also cover the profiler in R which lets you collect detailed information on how your R functions are running and to identify bottlenecks that can be addressed. The profiler is a key tool in helping you optimize your programs. Finally, we cover the str function, which I personally believe is the most useful function in R.
11 heures pour terminer
6 vidéos (Total 42 min), 4 lectures, 5 quiz
Avis
Meilleurs avis pour LA PROGRAMMATION EN R
par MR11 mai 2020
Really interesting course. The interactive coding sessions with swirl are especially useful. Would be great, if you provided sample solutions for the programming assignments, in particular for week 4.
par EJ11 juil. 2016
Excellent course! I already knew a lot about R - but this class helped me solidify what I already knew, taught me lots of new tricks, and now I have a certificate that says I know `something' about R!
par WH2 févr. 2016
"R Programming" forces you to dive in deep. These skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
par JM11 août 2019
Very challenging, but good course. I've been programming in R for over a year, but there were still some things for me to pick up in this class. Assignments were a challenge, but satisfying to tackle.
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