Are you a programmer that wants to learn R? Learn how to create web apps, REST APIs, machine learning models, and data visualization with the R - the most popular statistical language. Today you’ll learn how to: Load datasets; Scrape Webpages; Build REST APIs; Analyze Data and Show Statistical Summaries; Visualize Data; Train a Machine Learning Model; Develop Simple Web Applications. 6 Essential R Packages for Programmers:

R is a programming language created by Ross Ihaka and Robert Gentleman in 1993. It was designed for analytics, statistics, and data visualizations. Nowadays, R can handle anything from basic programming to machine learning and deep learning. Today we will explore how to approach learning and practicing R for programmers.

As mentioned before, R can do almost anything. It performs the best when applied to anything data related – such as statistics, data science, and machine learning.

The language is most widely used in academia, but many large companies such as Google, Facebook, Uber, and Airbnb use it daily.

Today you’ll learn how to:

- Load datasets
- Scrape Webpages
- Build REST APIs
- Analyze Data and Show Statistical Summaries
- Visualize Data
- Train a Machine Learning Model
- Develop Simple Web Applications

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

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In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

Learn the essential concepts in data science and understand the important packages in R for data science. You will look at some of the widely used data science algorithms such as Linear regression, logistic regression, decision trees, random forest, including time-series analysis. Finally, you will get an idea about the Salary structure, Skills, Jobs, and resume of a data scientist.

Python for Data Science, you will be working on an end-to-end case study to understand different stages in the data science life cycle. This will mostly deal with "data manipulation" with pandas and "data visualization" with seaborn. After this, an ML model will be built on the dataset to get predictions. You will learn about the basics of the sci-kit-learn library to implement the machine learning algorithm.