Udemy R for Data Science: Learn R Programming in 2 Hours

Udemy R for Data Science Learn R Programming in 2 Hours

Enter the world of R Programming: Everything you need to get started with R and Data Science in just 2 HOURS! R for Data Science: Learn R Programming in 2 Hours. This course will not waste your time, Are you tired of watching tutorials that take hours to explain simple concepts? You came to the right place. All this course asks you is 2-3 hours of your life. Are you tired of taking countless courses on Udemy that are 10+ hours and you leave it halfway because they're too darn long? You've come to the right place.

R for Data Science; Learn R programing in 2 hours? Sounds a bit too good to be true? No. This is the class I wish I had when I was trying to learn R Programming. I have a unique way of teaching, as I know how it must be overwhelming to learn a very complex programming language. The best part of this course is No prior programming experience is required. 2-3 hours is all you need to learn the basics of any programming language. Don't believe me? just go check the reviews on my other courses. I've been teaching programming on Udemy for the last 4 years with similar short courses and my students love them. So spend the next 2 hours that you would normally waste on a random Youtube video on this course and get maximum value in the minimum amount of time. This course will introduce you to the concepts of R programming and Data Science in just 2 Hours.

 What People Are Saying About My Programming Courses?

Excellent Course. Worth every Dollar.I always wanted to learn python. A few months back I purchased Ajay's C++ course and I loved it. I was excited to see him release a course on python. The course doesn't deviate from the topic like most courses on Python. This course didn't disappoint at all. I am only halfway in the course, but I am still able to write small programs. Downloadable lecture notes make the learning process a lot easier. If you are a beginner like me and want to write fun programs on Python fast, look no further and enroll this course"

Perfect Course for Beginners at Wonderful Price. Well, I was a little concerned about enrolling in this course as it was just released, but I have to say it beats all the other C++ Courses in the market. The best part is that it’s just 2 hours, the content is straight forward and doesn't waste your time just as it’s said in the promo video. Worth every buck! Will recommend it to all the beginners."

Very Good Course for Beginners.This course covers all the basic concepts of C++ in an easily understandable and interactive way. The instructor Ajay is also very helpful and replies readily to your queries and doubts. Overall I would strongly recommend this course to you if you are looking for basic knowledge of C++."

Excellent Course. I really enjoyed taking this course. I would definitely recommend this course to anyone with an interest in C++. It covers all the basics and good tips are given during the course. Ajay certainly knows the subject he teaches here. Looking forward to his next course."

Good primer .I'm brand new to Python, so this course was really just what I needed. I would like it to have been a bit longer and go a bit deeper, but as a brand new Python coder, I really enjoyed it and learned the basics."

Who this course is for?

- Beginners entering the world of Data Science and R.

- Someone with experience in other languages but want to learn R.

- Someone with no programming experience looking to learn their first language.

Requirements

- Basic computer knowledge.

- No prior R or Data Science experience is needed.

- Download and Install R and R Studio.

- Interest to learn.

What you'll learn?

- Master all the basics of R Programming.

- Learn how to learn programming by yourself.

- Pass their exams related to R.

- Learn basic concepts of Data Science.

- Develop problem solving ability.

Course content

- Introduction: Let's GO! Together Let's enter the world of R Programming and Data Science. An Introduction to this course and what to expect from it.

- Setting up the Environment: Install R and R Studio. 

- Data Types in R: Introduction to different Data Types available in R Programming Language.

- Making Use of R Data Types: The Goal of this lecture to make use of the Data types we learned in the previous lesson. Also an introduction to the concept of variables.

- Introduction to Data Structures in R: An Introduction to R Data Structures which will be the discussion topic of our entire next section.

- Introduction to Vectors in R: We begin the section on R Data Structures with an Introduction to Vectors.

- Creating Vectors in R: Learn different ways in which we can create Vectors in R.

- Coding in R Studio: Creating Vectors: We'll implement what we learned about vectors in the previous lectures in R Studio.

- Operators in R: Learn how to perform operations on Vectors using Operators in R.

- Accessing Vector Elements in R: Learn the concept of Indexing to Access Vector Elements in R.

- Creating A Matrix in R: In this lecture we'll learn our second data structure in R - Matrix.

- Accessing Matrix Elements in R: Here we will expand on our knowledge of Indexing to Access Matrix Elements in R.

- Data Frames in R: In this lecture,we'll learn how to create Data Frames in R.

- Accessing Data Frames in R: In this lecture, we'll learn how to access and manipulate Data Frame values.

- Introduction to Arrays in R: In this lecture, we'll learn how to create Arrays in R.

- Accessing Arrays in R: Learn how to access elements of the Arrays in R Studio.

- Lists in R: Finally a clear explanation to Lists!. It's about time don't you think.

- Factors in R: In this lecture, we'll learn about the final data structure of the section, Factors.

- Assignment Operator: Some use = , Some use <-. 99% times Both mean the same thing. Which one do you prefer?.

- Introduction to Practical Data Science: What is the point of learning things if you can't apply them? Every section from now will be implementing something practical using R.

- Data Preparation in R Part 1: Importing Dataset : The first step in Data Science is to get your data into your program. That is exactly what we'll learn in this lecture.

- Data Preparation in R Part 2: Dimensions and Column Names : In this lecture, we'll learn to find the dimensions of our data set and also on how we can add column names to our dataset.

- Data Preparation in R Part 3: Finding Missing Values: Sometimes your dataset might contain missing values, In this lecture we'll learn how to find them.

- Data Preparation in R Part 4: Fixing the Missing Values Problem: In this lecture, we'll talk about different methods you can use to solve the missing values problem.

- Visualization in R Part 1: Setting up the Environment: In this lecture we will install the necessary libraries in R Studio to start visualizing our datasets.

- Visualization in R Part 2: Dot Plots or Scatter Plots: We'll start learning Visualization by learning how to plot scatter plots or box plots on Iris Data Set.

- Visualization in R Part 3: Bar Plots: Time to learn our next Visualization Technique - BAR PLOTS!

- Visualization in R Part 4: Box Plots: Box Plots gives us a lot of statistical information in one place. In this lecture we'll learn how you can plot Box plots in R.

- Visualization in R Part 4: Histograms and More: In this lecture we'll talk about Histograms and then conclude our Visualization section and move on to other things.

- Machine Learning using R Part 1: Introduction to Machine Learning: The goal of this lecture is to introduce you to Machine learning by comparing it with traditional programming.

- Machine Learning using R Part 2: What Will We Make?: In this lecture, we'll talk about our machine learning project that we will be implementing in this section.

- Machine Learning using R Part 3: Linear Regression without too much Math: Simple introduction to one of the most popular machine learning algorithms without getting into too much math.

- OPTIONAL: Machine Learning using R Part 3: Linear Regression with Math: Optional lecture for those with previous Experience in Statistics and Maths to show how linear regression works.

- Machine Learning using R Part 3: Creating the Machine Learning Model in R : In this lecture, we will open up R Studio and create the machine learning model using Linear Regression.

 - Bidding Farewell! and to NEW BEGINNINGS: Summing up everything we learned and Congratulations for reaching this far. To New Beginnings.

Udemy R for Data Science: Learn R Programming in 2 Hours