Description
Data Scientist with R, Data Scientist with R course is published by Datacamp Academy. You’ll learn how to use R for data science, from data manipulation to machine learning, while gaining the R skills you need to succeed as a data scientist. As you progress through the courses in this track, you’ll discover how learning data science with R can help you import, clean, manipulate, and visualize data. R is a versatile language for any aspiring data scientist or researcher. By learning the necessary skills, you’ll build a solid foundation for your data science journey.
Through interactive exercises, you will be introduced to some of the most popular R packages, including tidyverse packages such as ggplot2, dplyr, and readr. You’ll work with real-world datasets, write your own functions, and learn fundamental statistical and machine learning techniques. Embark on this path, enhance your R programming and data science skills, and begin your journey to becoming a confident data scientist.
What you will learn
- Principles of R
- Introducing tidyverse
- Manipulating data with dplyr
- Join data with dplyr
- Statistics in R
- Data visualization with ggplot2
- Data manipulation with R
- Data communication concepts
- Data cleaning in R
- Working with date and time in R
- Entering and deleting data with R
- Writing functions in R
- Regression in R
- Sampling in R
- Hypothesis testing in R
- Statistical principles with R
Data Scientist with R course specifications
- Publisher: Datacamp
- teacher : JONATHAN CORNELISSEN
- English language
- Education level: all levels
- Number of courses: 22
- Training duration: 88 hours to complete the course
Chapters of the Data Scientist with R course
Pictures
Sample video
Installation guide
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English subtitle
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Introduction to R
Intermediate R
Introduction to the Tidyverse
Data Manipulation with dplyr
Joining Data with dplyr
Introduction to Statistics in R
Introduction to Data Visualization with ggplot2
Intermediate Data Visualization with ggplot2
Data Communication Concepts
Introduction to Importing Data in R
Cleaning data in R
Working with Dates and Times in R
Introduction to Writing Functions in R
Exploratory Data Analysis in R
Introduction to Regression in R
Intermediate Regression in R
Sampling in R
Hypothesis testing in R
Experimental design in R
Supervised Learning in R Classification
Supervised Learning in R Regression
Unsupervised Learning in R
File(s) password: www.downloadly.ir
Size
1.94 GB