Об этом курсе: This course provides a rigorous introduction to the R programming language, with a particular focus on using R for software development in a data science setting. Whether you are part of a data science team or working individually within a community of developers, this course will give you the knowledge of R needed to make useful contributions in those settings. As the first course in the Specialization, the course provides the essential foundation of R needed for the following courses. We cover basic R concepts and language fundamentals, key concepts like tidy data and related "tidyverse" tools, processing and manipulation of complex and large datasets, handling textual data, and basic data science tasks. Upon completing this course, learners will have fluency at the R console and will be able to create tidy datasets from a wide range of possible data sources.

Для кого этот курс: This course is aimed at learners who have some experience programming computers but who are not familiar with the R environment.

Автор: Johns Hopkins University

Преподаватели: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Преподаватели: Brooke Anderson, Assistant Professor, Environmental & Radiological Health Sciences
Colorado State University

Основные сведения	Курс 1 из 5 — Mastering Software Development in R Specialization
Уровень	Учащийся среднего уровня
Язык	English
Как пройти курс	Чтобы пройти курс, выполните все оцениваемые задания.
Оценки пользователей	4.4 звезды Средняя оценка пользователей: 4.4Посмотрите, что пишут учащиеся

Программа курса

НЕДЕЛЯ 1

Basic R Language

In this module, you'll learn the basics of R, including syntax, some tidy data principles and processes, and how to read data into R.

1 видео, 27 материала для самостоятельного изучения

Reading: Course Textbook: Mastering Software Development in R
Reading: Syllabus
Reading: Swirl Assignments
Reading: Datasets
Reading: Lesson Introduction
Reading: Evaluation
Reading: Objects
Reading: Numbers
Reading: Creating Vectors
Reading: Mixing Objects
Reading: Explicit Coercion
Reading: Matrices
Reading: Lists
Reading: Factors
Reading: Missing Values
Reading: Data Frames
Reading: Names
Reading: Attributes
Reading: Summary
Reading: The Importance of Tidy Data
Reading: The “Tidyverse”
Reading: Reading Tabular Data with the readr Package
Reading: Reading Web-Based Data
Reading: Flat files online
Reading: Requesting data through a web API
Reading: Scraping web data
Reading: Parsing JSON, XML, or HTML data

Оцениваемый: Swirl Lessons

НЕДЕЛЯ 2

Data Manipulation

During this module, you'll learn to summarize, filter, merge, and otherwise manipulate data in R, including working through the challenges of dates and times.

11 материала для самостоятельного изучения

Reading: Basic Data Manipulation
Reading: Piping
Reading: Summarizing data
Reading: Selecting and filtering data
Reading: Adding, changing, or renaming columns
Reading: Spreading and gathering data
Reading: Merging datasets
Reading: Working with Dates, Times, Time Zones
Reading: Converting to a date or date-time class
Reading: Pulling out date and time elements
Reading: Working with time zones

Оцениваемый: Swirl Lessons

НЕДЕЛЯ 3

Text Processing, Regular Expression, & Physical Memory

During this module, you'll learn to use R tools and packages to deal with text and regular expressions. You'll also learn how to manage and get the most from your computer's physical memory when working in R.

9 материала для самостоятельного изучения

Reading: Text Processing and Regular Expressions
Reading: Text Manipulation Functions in R
Reading: Regular Expressions
Reading: RegEx Functions in R
Reading: The stringr Package
Reading: Summary
Reading: The Role of Physical Memory
Reading: Back of the Envelope Calculations
Reading: Internal Memory Management in R

Оцениваемый: Swirl Lessons

НЕДЕЛЯ 4

Large Datasets

In this final module, you'll learn how to overcome the challenges of working with large datasets both in memory and out as well as how to diagnose problems and find help.

7 материала для самостоятельного изучения

Reading: Working with Large Datasets
Reading: In-memory strategies
Reading: Out-of-memory strategies
Reading: Diagnosing Problems
Reading: How to Google Your Way Out of a Jam
Reading: Asking for Help
Reading: Quiz Instructions

Оцениваемый: Reading and Summarizing Data

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Как это работает

Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from Your Peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Авторы

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.

Стоимость

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Получить доступ к материалам курса	Доступен
Получить доступ к оцениваемым материалам курса	Доступен
Получить итоговую оценку	Доступен
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Рейтинги и отзывы

Оценка 4.4 из 5 по 689 отзывам

Great overview of R with a focus on Hadley Wickham's tidyverse (very useful set of packages for data scientists).

Great class!!

Recorded whiteboard classes or videos from the instructors would significantly increase the value of the course. There should be more interaction with the instructors. Some weeks tasks/assignments were extremely more difficult compared to others. Otherwise, I liked it and learned a lot.