Data Science Using R Training
Data Science Using R Training
Order this unique E-Learning Data Science Using R Training online, 1 year of 24/7 access to rich interactive videos, progress through reporting and testing.
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Data Science Using R E-Learning
✔️ 1 year 24/7 access to rich interactive videos, voice commands and progress monitoring via reports.
✔️ Test your knowledge immediately with tests per chapter and improve your skills step by step.
Why choose this course?
R is one of the most widely used data science tools worldwide, thanks to the power and flexibility it offers for statistical analysis and data visualization. This E-Learning training is designed to give you an in-depth understanding of R and take your skills as a data scientist to the next level.
This course will teach you essential techniques and workflows needed to be successful in data science, including:
- Data wrangling: Master techniques to clean, transform and prepare raw data for analysis.
- Debugging: Understand how to identify and fix errors in your R code.
- Defensive Programming: Learn techniques to write robust and error-resistant code.
- Domain-specific language integration: Learn how to integrate R with other tools and languages for advanced data analysis.
This training provides a solid foundation for data scientists, with practical examples and exercises that you can immediately apply to your own projects.
Who should participate?
This course is ideal for:
- Beginning data scientists who want to learn how to work with R as their primary tool for data analysis and visualization.
- Statistics or analytics professionals looking to expand their skills with R.
- Academics and researchers who want to analyze and present their datasets using R.
- IT professionals who are involved in data-intensive projects and want to add R to their technical arsenal.
- Those interested in data science who want to understand how R can be used to generate valuable insights from data.
Course content
Data Wrangling in R
Course: 9 Minutes
- Course Introduction
- Introduction to Data Wrangling in R
The dplyr Library
Course: 52 Minutes
- Data Wrangling with dplyr
- Exploring Data with dplyr
- Filtering with dplyr
- Piping with dplyr
- Mutating Data with dplyr
- Summarizing Data with dplyr
- Selecting with dplyr
- Combining Data Sets with dplyr
- Set Operations with dplyr
- Arranging with dplyr
The tidyr Library
Course: 13 Minutes
- Data Manipulation with tidyr
- Gathering with tidyr
- Separating with tidyr
Rectangular Data Extraction
Course: 11 Minutes
- Using readr
- Using readxl
Practice: Using the dplyr Library
Course: 3 Minutes
- Exercise: Combine dplyr Verbs
Debugging
Course: 30 Minutes
- Course Introduction
- Debugging with RStudio
- Using Traceback
- Debugging with Browser
- Displaying Warnings and Messages
- Implementing Handlers
- Benchmarking in R
Defensive Programming in R
Course: 15 Minutes
- Methods of Defensive Programming
- Turning Warnings into Errors
- Programming with Asserts
Memory Management
Course: 9 Minutes
- Using pryr
- Addressing Memory with pryr
Practice: Using Debugging
Course: 2 Minutes
- Exercise: Debug with Browser
Utility Functions in R
Course: 33 Minutes
- Course Introduction
- Using substitute, quote, and deparse
- Using ast
- Using str
- Lazy Evaluation in R
- Using eval
- Using paste Functions
Classes and Operators
Course: 24 Minutes
- Creating Reference Classes
- Using class
- Defining a Binary Operator
- Simulating a Unary Operator
Code Translation
Course: 17 Minutes
- Using SQL to R Translation
- Exporting HTML tables
- Exporting a LaTeX document
- Using list2env
C and C++ Integration
Course: 15 Minutes
- Connecting inline C++ to R
- Calling External C++ with R
- Using cfunction in R
Practice: Code Transformation
Course: 3 Minutes
- Exercise: Output an R Table as LaTeX3
Library Tools
Course: 25 Minutes
- Course Introduction
- Exploring the tidyverse Collection of Libraries
- Manipulating Strings with the stringr Library
- Using the forcats Library
- Using the purrr Library
Web Application Tools
Course: 17 Minutes
- Creating a shiny Application
- Rendering a Plot in shiny
- Displaying a JavaScript DataTable
R Markdown
Course: 25 Minutes
- Using Markdown in R
- Rendering HTML
- Rendering LaTeX
- Passing knitr Parameters
- Referencing with bibtex
Practice: R Markdown Publishing
Course: 3 Minutes
- Exercise: Create and Publish from R Markdown
Start Data Science Using R today!
✔️ Learn at your own pace with comprehensive interactive videos and exercises that guide you through the key concepts of data science with R.✔️ Instantly applicable knowledge to improve your data analysis skills and generate impactful insights.✔️ Become an R expert and grow your career opportunities in data science.
Order your course now and start your journey to data expertise today!
| Language | English |
|---|---|
| Qualifications of the Instructor | Certified |
| Course Format and Length | Teaching videos with subtitles, interactive elements and assignments and tests |
| Lesson duration | 5:04 Hours |
| Progress monitoring | Yes |
| Access to Material | 365 days |
| Technical Requirements | Computer or mobile device, Stable internet connections Web browsersuch as Chrome, Firefox, Safari or Edge. |
| Support or Assistance | Helpdesk and online knowledge base 24/7 |
| Certification | Certificate of participation in PDF format |
| Price and costs | Course price at no extra cost |
| Cancellation policy and money-back guarantee | We assess this on a case-by-case basis |
| Award Winning E-learning | Yes |
| Tip! | Provide a quiet learning environment, time and motivation, audio equipment such as headphones or speakers for audio, account information such as login details to access the e-learning platform. |
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