Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
14 Hour(s) 17 Minute(s)
Language
English
Taught by
KGP Talkie | Laxmi Kant
Certificate
- Available
- *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
Course Overview
Learn Latest R 4 with R-Studio & Jupyter. DataFrame, Vectors, Matrix, DateTime, GGplot2, Tidyverse, Plotly, etc.
Take your first step towards becoming a data science expert with our comprehensive R programming course. This course is designed for beginners with little or no programming experience, as well as experienced R developers looking to expand their skill set.
You'll start with the basics of R programming and work your way up to advanced techniques used in data science. Along the way, you'll gain hands-on experience with popular R libraries such as dplyr, ggplot2, and tidyr.
You will learn how to import, clean and manipulate data, create visualizations and statistical models to gain insights and make predictions. You will also learn data wrangling techniques and how to use R for data visualization.
By the end of the course, you'll have a solid understanding of R programming and be able to apply your new skills to a wide range of data science projects. You'll also learn how to use R in Jupyter notebook, so that you can easily share your work and collaborate with others.
So, if you're ready to take your first step towards becoming a data science expert, this is the course for you! With our hands-on approach and interactive quizzes, you'll be able to put your new skills into practice right away.
In this course, you learn:
How to install R-Packages
How to work with R-data types
What is R DataFrame, Matrices, Vectors, etc?
How to work with DataFrames
How to perform join and merge operations on DataFrames
How to plot data using ggplot2 in R 4
Analysis of real-life dataset Covid-19
How this course will help you?
This course will give you a very solid foundation in machine learning. You will be able to use the concepts of this course in other machine learning models. If you are a business manager or an executive or a student who wants to learn and excel in machine learning, this is the perfect course for you.
Course Content
- 8 section(s)
- 126 lecture(s)
- Section 1 Introduction
- Section 2 R Programming Fundamentals
- Section 3 Fundamentals of DataFrames in R Programming
- Section 4 Jupyter Notebook Introduction for R Programming
- Section 5 Fundamentals of Data Visualization with GGPlot2
- Section 6 Data Preprocessing and Analysis with tidyverse and dplyr
- Section 7 Plotly | Covid-19 Data Analysis
- Section 8 Linear Regression and Data Analysis on Boston Housing Dataset
What You’ll Learn
- Learn to write a program in R 4.0
- Learn fundamentals of R programming
- How to use R-Studio
- How to analyze the data
- How to plot beautiful plots
- Real exercise for data analysis
- Use for Machine Learning programming
- Write code for Linear Regression and Logistic Regression Analysis
- Data visualization on real dataset | Covid-19, Boston Housing Price and Titanic dataset
- Learn Plotly for Covid-19 Data Analysis
- Advanced Plotly in R
- Linear Regression in R
- Non-Linear and Polynomial Regression
- Multiple Simple Linear Regression in R on Boston Housing Price Prediction
Skills covered in this course
Reviews
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SSasha Kariba
The course is well paced and good for beginners of R like myself. As I have coding experience in C++ and SQL it was quite informative. Although, I would say the instructor might want to update the course more often and include more troubleshooting help. Most concepts were explained well and in a jovial manner which I appreciate for these kinds of topics, but more work is needed from the instructor in explaining why certain actions are performed or why we use certain functions instead of others e.g. pmax(). A few mistakes here and there in the instruction too. I found concepts like matrices, matrix operations, arrays, inner, left and right joins were not explained well enough and need to be reworked. I had to fill in the gaps through other sources. If I did not have prior coding experience, I would've struggled with the materials given. Otherwise, good course overall!
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JJozef Tarrant
Good overall, but the Jupyter Notebook part is out of date. I had to research my own solutions and workarounds to get it installed and working.
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NNDACYAYISENGA Jean Claude
It was amazing. I am happy to explore Jupyter notebook. it was my first time to see it
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DDaniel Macias
Very detailed explanation
