Этот курс входит в специализацию ''Специализация Data Science'
R Programming
от партнера
Об этом курсе
4.6
Оценки: 12,282
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.
Чему вы научитесь
- Collect detailed information using R profiler
- Configure statistical programming software
- Make use of R loop functions and debugging tools
- Understand critical programming language concepts
Приобретаемые навыки
Data AnalysisDebuggingR ProgrammingRstudio
Программа курса: что вы изучите
1
Раздел
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.
28 видео (всего 129 мин.), 9 материалов для самостоятельного изучения, 8 тестов
28 видео
Installing R on Windows3мин
Installing R Studio (Mac)1мин
Writing Code / Setting Your Working Directory (Windows)7мин
Writing Code / Setting Your Working Directory (Mac)7мин
Introduction1мин
Overview and History of R16мин
Getting Help13мин
R Console Input and Evaluation4мин
Data Types - R Objects and Attributes4мин
Data Types - Vectors and Lists6мин
Data Types - Matrices3мин
Data Types - Factors4мин
Data Types - Missing Values2мин
Data Types - Data Frames2мин
Data Types - Names Attribute1мин
Data Types - Summaryмин
Reading Tabular Data5мин
Reading Large Tables7мин
Textual Data Formats4мин
Connections: Interfaces to the Outside World4мин
Subsetting - Basics4мин
Subsetting - Lists4мин
Subsetting - Matrices2мин
Subsetting - Partial Matching1мин
Subsetting - Removing Missing Values3мин
Vectorized Operations3мин
Introduction to swirl1мин
9 материала для самостоятельного изучения
Welcome to R Programming10мин
About the Instructor10мин
Pre-Course Survey10мин
Syllabus10мин
Course Textbook10мин
Course Supplement: The Art of Data Science10мин
Data Science Podcast: Not So Standard Deviations10мин
Getting Started and R Nuts and Bolts10мин
Practical R Exercises in swirl Part 110мин
1 практическое упражнение
Week 1 Quiz40мин
2
Раздел
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.
13 видео (всего 91 мин.), 3 материалов для самостоятельного изучения, 5 тестов
13 видео
Control Structures - If-else1мин
Control Structures - For loops4мин
Control Structures - While loops3мин
Control Structures - Repeat, Next, Break4мин
Your First R Function10мин
Functions (part 1)9мин
Functions (part 2)7мин
Scoping Rules - Symbol Binding10мин
Scoping Rules - R Scoping Rules8мин
Scoping Rules - Optimization Example (OPTIONAL)9мин
Coding Standards8мин
Dates and Times10мин
3 материала для самостоятельного изучения
Week 2: Programming with R10мин
Practical R Exercises in swirl Part 210мин
Programming Assignment 1 INSTRUCTIONS: Air Pollution10мин
2 практического упражнения
Week 2 Quiz20мин
Programming Assignment 1: Quiz20мин
3
Раздел
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.
8 видео (всего 61 мин.), 2 материалов для самостоятельного изучения, 4 тестов
8 видео
Loop Functions - apply7мин
Loop Functions - mapply4мин
Loop Functions - tapply3мин
Loop Functions - split9мин
Debugging Tools - Diagnosing the Problem12мин
Debugging Tools - Basic Tools6мин
Debugging Tools - Using the Tools8мин
2 материала для самостоятельного изучения
Week 3: Loop Functions and Debugging10мин
Practical R Exercises in swirl Part 310мин
1 практическое упражнение
Week 3 Quiz10мин
4
Раздел
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.
6 видео (всего 42 мин.), 4 материалов для самостоятельного изучения, 5 тестов
6 видео
The str Function6мин
Simulation - Generating Random Numbers7мин
Simulation - Simulating a Linear Model4мин
Simulation - Random Sampling2мин
R Profiler (part 1)10мин
R Profiler (part 2)10мин
4 материала для самостоятельного изучения
Week 4: Simulation & Profiling10мин
Practical R Exercises in swirl Part 410мин
Programming Assignment 3 INSTRUCTIONS: Hospital Quality10мин
Post-Course Survey10мин
2 практического упражнения
Week 4 Quiz20мин
Programming Assignment 3: Quiz20мин
4.6
28%
83%
11%
Лучшие рецензии
автор: EJ•Jul 12th 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!
автор: WH•Feb 3rd 2016
"R Programming" forces you to dive in deep.\n\nThese skills serve as a strong basis for the rest of the data science specialization.\n\nMaterial is in depth, but presented clearly. Highly recommended!
Преподавателя
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
О Johns Hopkins University
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.
О специализации ''Data Science'
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Часто задаваемые вопросы
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
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