Descriptions
Data Scientist with R: Learn how to use R for data science, from data manipulation to machine learning, and gain the career-enhancing R skills you need to succeed as a data scientist. As you complete 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 professional or researcher, and by learning the integral skills, you’ll develop a solid foundation for your data science journey. Through interactive exercises, you’ll learn some of the most popular R packages, including tidyverse packages like ggplot2, dplyr, and readr. You’ll work with real-world datasets, write your own functions, and learn basic statistical and machine learning techniques. Take this course, expand your R programming and data science skills, and begin your journey to becoming a confident data scientist.
What you will learn
- introduction to Tidyverse
- Data Manipulation with dplyr
- accession Data with dplyr
- introduction to statistics in R
- introduction for data visualization with ggplot2
- Data Manipulation with R
- Data Communication concepts
- introduction for importing data into R
- cleaning Data in R
- Work with date and time in R
- introduction for writing functions in R
- Exploratory Data analysis in R
- introduction for regression in R
- sampling in R
- hypothesis Testing in R
- experimental Designing in R
Specifications of Data Scientist with R
- Publisher : Data Camp
- Teacher: JONATHAN CORNELISSEN
- Language: English
- Level: All levels
- Number of courses: 22
- Duration: 88 hours to complete the course
Content from Data Scientist with R
Pictures
Sample clip
installation Guide
Extract the files and watch them with your favorite player
Subtitles: English
Quality: 720p
Download links
Introduction to R
Intermediate R
Introduction to the Tidyverse
Data manipulation with dplyr
Connect data with dplyr
Introduction to Statistics in R
Introduction to data visualization with ggplot2
Advanced data visualization with ggplot2
Data communication concepts
Introduction to importing data into R
Cleaning data in R
Working with dates and times in R
Introduction to write functions in R
Exploratory data analysis in R
Introduction to Regression in R
Mean 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
Password file(s): free download software
File size
1.94GB