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Learning R Programming for Data Science

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Last updated 5/2022 English
Overview

This course provides an overview of Data Science and how R language with its methods and functions constitutes a primary tool to become a Data Scientist. The course presents the Data Science process. Then, it explains how to use various R methods and functions to analyze data.

It shows how to install R and R Studio as a start, then it presents topics on R data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames and lists. Besides, it includes topics on operators, conditionals, loops, functions, and packages. It also covers regular expressions, getting and cleaning data, plotting, and data manipulation using the dplyr package.

The ever-increasing size of data globally coupled with the prominent need to extract insightful information out of it necessitate learning analytical programming languages such as R. This course paves the road for beginners to start using R in real life analysis tasks and research projects to enable a fact-based decision-making process.

Who this course is for

Professionals and academics who aspire to use R Language as part of their Data Analysis and Data Science tasks.

Testimonials
  1. Easy and Understandable. Slow and step by step explanations for every concept with examples ~ Dhanalakshmi G
  2. The introductory part is already very good. It introduces the topic in such a way that one would want to complete the course. I will complete this course, The God willing ~ Ramon J
What you'll learn
  1. Describe Data Science and Big Data
  2. Recognize the importance of Data Science
  3. Explain the Data Science process
  4. Identify main tools used in Data Science
  5. Explain the steps of a Data Science project
  6. Recognize the main environment and files of RStudio
  7. Complete installing R and R Studio on own machine
  8. Solve arithmetic calculations in R
  9. Distinguish between different data types in R
  10. Solve data problems using vectors, matrices, factors, data frames, and lists in R
  11. Formulate controlled-flow data problems using Operators, Conditional Statements, and Loops
  12. Recognize base R functions and user-defined functions in R
  13. Analyze data using base mathematical functions, R Packages, and Apply function family
  14. Modify data using Regular Expressions and Dates & Times functions
  15. Integrate and cleanse external data in R
  16. Plot data in R
  17. Evaluate datasets in R using dplyr package
Requirements
  1. No prior knowledge is mandatory to this course.
  2. Passion towards learning programming and statistics is essential
Course Content
24 Sections 115 Lectures 6h 14m total length