Master in R Programming with Data Science - 2024

Save Rs. 13,500.00

Price:
Sale priceRs. 2,499.00 Regular priceRs. 15,999.00

Description

R is one of the most widely used programming languages in the industry. It has a very steep learning curve since students get overwhelmed while learning.

You will be moving one step closer to mastering the R programming throughout this course.

R can also be used to analyze own datasets and bring out statistical outputs. The best thing about R is that you can bring up a powerful analysis with R vectors, arrays, matrices and lists. Combining these will bring the best analytic data.

This course will bring you the best knowledge on R programming which can be implemented during a real-time scenario when analyzing the data.

This course will also teach you how to create the best visualization with different datasets by using R programming.

What is R Programming Language?

The R Core Team and the R Foundation for Statistical Computing are two organizations that support the programming language R, which is used for statistical computing and graphics.

Ross Ihaka and Robert Gentleman created R in 1993, which also has time series, statistical inference, machine learning techniques, and more.

R is a widely used for 

  • Data analysis
  • Statistical inference
  • Machine learning methods.

R provides a helpful environment for statistical computing and design and offers a wide range of statistics-related libraries. Additionally, since it's helpful for importing and cleaning data, many quantitative analysts use the R programming language as a programming tool.

Why should you learn R Programming?

Data analysis powerhouse R is a great tool for managing and manipulating data because it was created specifically for statistical computing and analysis.
Widely used in academia and industry R's widespread use and growing popularity across disciplines like data science, bioinformatics, finance, and the social sciences guarantee its continued success and growth.
Rich ecosystem R's flexibility and adaptability come from its extensive package and library ecosystem, which allows it to do a wide variety of specialized tasks.
Data visualization R is a powerful tool for producing professional-grade visualizations, which facilitates the discovery and sharing of insights from data.
Reproducibility and collaboration R scripts facilitate reproducible research by facilitating the verification and replication of your results by other researchers.
Open-source and free Since R is a free and open-source programming language, it can be used by anybody.
Active community support The R community is active and helpful, with plenty of learning materials, discussion boards, and how-to guides available.
Integration with other languages Integrating R with Python, for example, lets you take advantage of both languages' capabilities.
Career opportunities Increase your employment opportunities in data analysis and data science by learning to code in R.

What will you require?

  • A Personal computer
  • A compatible browser
  • A Passion to Learn

Syllabus

Hit the Ground and Running

  • Introduction to R Programming
  • R studio installation in Mac and Windows
  • Basics to datasets
  • Additional resources

Core Programming Principles

  • Variables and its types
  • Using the variables
  • What are Logical variables and operators?
  • What is a “While” loop?
  • How to use the console
  • What is a “For” loop?
  • If statement
  • An Overview of the section
  • Exercise for this section
  • Quiz

Fundamentals of R

  • Definition of Vector
  • Creating some vectors
  • How to use [] brackets
  • What are vectorized operations?
  • Power of Vectorized operations
  • What are the functions in R?
  • What are the packages in R?
  • An Overview of the section
  • Exercise for this section
  • Quiz

Matrices

  • Basketball Trends - Project
  • What are Matrices?
  • How to build your first matrix
  • What are Naming dimensions?
  • What are Colnames() and Rownames()?
  • An overview of Matrix Operations
  • Matplot() visualization
  • What is subsetting?
  • Subset visualization
  • How to create your First Function
  • Insights in Basketball
  • An overview of the section
  • Exercise for this section
  • Matrices quiz

Data Frames

  • Demographic Analysis - Project
  • How to import data in R
  • Dataset exploration
  • How to use the “$” sign
  • Data frame in basic operations
  • How to filter Data frame
  • Qplot introduction
  • Qplot visualization - Part 1
  • How to build Dataframes
  • Merging of Data frames
  • Qplot visualization - Part 2
  • An Overview of the section
  • Exercise for this section
  • Quiz

Advanced Visualization with GGPlot2

  • Movie Ratings - Project
  • Grammar Graphics - GGPlot2
  • Explanation of Factor
  • What are Aesthetics?
  • How to plot with Layers?
  • How to override with Aesthetics?
  • Difference between Mapping and Setting
  • Explanation of Histograms and Density Charts
  • Introduction to Layer Tips
  • What are Statistical Transformations?
  • How to use Facets?
  • What are Coordinates?
  • How to perfect by adding themes?
  • An overview of the section
  • Exercise for this section
  • Quiz

Homework Solutions

  • Law of Large Numbers
  • Financial Statement Analysis
  • Basketball Free Throws
  • World Trends
  • Movie Domestic % Gross (Part 1)
  • Movie Domestic % Gross (Part 2)
  • Thanking the students

    What you’ll learn?

    • R Programming at a good level
    • Core Principles of Programming
    • Creating variables
    • Creating a while() loop and a for() loop in R
    • Basics of matrix(), rbind() and cbind() functions
    • Own Customization of R studio based on preferences
    • Basic understanding of Normal distribution
    • How to work with Financial data in R
    • Using R studio
    • Creating vectors in R
    • Integer, double, logical, character and other data types in R
    • Building and using matrices in R
    • Installing packages in R
    • Law of Large Numbers explanation
    • Practical working with statistical data in R
    • Practical working with sports data in R 

    Who can enroll this course?

    • People who want to learn R programming
    • People who are exhausted by searching R courses that are much more complicated
    • People who want to learn R practically
    • People who are excited about coding challenges
    • People who are able to put extra work as homework in this course

    Course Duration

    • 285 Lectures and  32 Hours of on Demand HD Videos
    • Full lifetime access
    • Access on mobile and TV
    • Certificate of Completion
    • 7900+ students enrolled
    • Complete Practical Training
    • Download access
    • Watch Videos in Android and iOS App

    You may also like

    Recently viewed