I am a person who is better at statistics than any software engineer and better at software engineering than any statistician.

Rohit Kumar Singh

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of this course is to help you learn the most important tool in R that will allow you to do data science. After completing this you will have the tool to tackle a wide variety of data science problems using the best parts of R. Tags: Programming Language, R Progamming, Data Science, Data Science for Beginners, Analytics, Analytics using R

Programming

English

R Programming: Introductory Course in Data Science

Programming Languages

Sorting

Basics

Competitive Programming

Personal and Skill Development

Develop your coding skills, learn a new language or prepare for campus placements and job interviews, you can do it all with these awesome courses.

R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs.
This introduction to R programming course will help you master the basics of R. In 15 lectures, we will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

Course Overview

In this lesson, we will explore some basic building blocks of the R programming language. I will tell you the links to download the required software. We will explore the work space of R studio, the IDE which we will work upon. We will cover basics like assignment operator, vectors, functions, vector recycling with some time saving tricks which you can apply in your code in R.

Basic Building Blocks

In this lesson, you'll learn how to examine your local workspace in R and begin to explore the relationship between your workspace and the file system of your machine, because different operating systems have different conventions with regards to things like file paths, the outputs of these commands may vary across machines.However we will see that R provides a common API (a common set of commands) for interacting with files, that way your code will work across different kinds of computers.

Workspace and Files

In this lesson, you'll learn how to create sequences of numbers in R. We will see how to generate regular sequences. How to increment them, how one can check the length of the sequence. We will also see how can you replicate the seq which will be a part of every data guy's work.

Sequences and Numbers

In this lesson, you'll learn about the simplest and most common data structure in R, i.e, vectors. In previous lessons, we dealt entirely with numeric vectors, which are one type of atomic vector. Other types of atomic vectors include logical, character, integer, and complex. In this lesson, we'll take a closer look at logical and character vectors.

Vectors

In this lesson, you'll learn that missing values play an important role in statistics and data analysis. Often, missing values must not be ignored, but rather they should be carefully studied to see if there's an underlying pattern or cause for their missingness. The lesson will contain NA which stands for Not Available and acts as a placeholder for missing data. We will also look in NaN's which is Not a Number mostly used for undefined operations.

Missing Values

In this lesson, we'll see how to extract elements from a vector based on some conditions that we specify. For example, we may only be interested in the first 20 elements of a vector, or only the elements that are not NA, or only those that are positive or correspond to a specific variable of interest. By the end of this lesson, you'll know how to handle each of these scenarios.

Subsetting Vectors

In this lesson, we'll cover matrices and data frames. Both represent 'rectangular' data types, meaning that they are used to store tabular data, with rows and columns. The main difference, as you'll see, is that matrices can only contain a single class of data, while data frames can consist of many different classes of data.

Matrices and Data Frames

This lesson is meant to be a short introduction to logical operations in R.There are two logical values in R, also called boolean values. They are TRUE and FALSE. In R you can construct logical expressions which will evaluate to either TRUE or FALSE. Many of the questions in this lesson will involve evaluating logical expressions. It may be useful to open up a second R terminal where you can experiment with some of these expressions.

Logic

In this lesson, you'll learn that functions are one of the fundamental building blocks of the R language. They are small pieces of reusable code that can be treated like any other R object. If you've worked through any other part of this course, you've probably used some functions already. Functions are usually characterized by the name of the function followed by parentheses.

Functions

In this lesson, you'll learn how to use lapply() and sapply(), the two most important members of R's *apply family of functions, also known as loop functions.  These powerful functions, along with their close relatives (vapply() and tapply(), among others) offer a concise and convenient means of implementing the Split-Apply-Combine strategy for data analysis.Each of the *apply functions will SPLIT up some data into smaller pieces, APPLY a function to each piece, then COMBINE the results.

Lapply and Sapply

In the last lesson, you learned about the two most fundamental members of R's *apply family of functions: lapply() and sapply(). Both take a| list as input, apply a function to each element of the list, then combine and return the result. lapply() always returns a list, whereas| sapply() attempts to simplify the result.In this lesson, you'll learn how to use vapply() and tapply(), another important functions of the R's *apply family of functions.

Vapply and Tapply

Whenever you're working with a new dataset, the first thing you should do is look at it! What is the format of the data? What are the dimensions? What are the variable names? How are the variables stored? Are there missing data? Are there any flaws in the data?  This lesson will teach you how to answer these questions and more using R's built-in functions.

Looking at Data

In this lesson, you'll learn about one of the great advantages of using a statistical programming language like R is its vast collection of tools for simulating random numbers. This lesson assumes familiarity with a few common probability distributions, but these topics will only be discussed with respect to random number generation. Even if you have no prior experience with these concepts, you should be able to complete the lesson and understand the main ideas.

Simulation

In this lesson, we will see that R has a special way of representing dates and times, which can be helpful if you're working with data that show how something changes over time (i.e. time-series data) or if your data contain some other temporal information, like dates of birth.you'll learn how to wrangle the dates and times in R.Dates are represented by the 'Date' class and times are represented by the 'POSIXct' and 'POSIXlt' classes.

Date and Time in R

In this lesson, you'll learn one of the greatest strengths of R, relative to other programming languages i.e base graphics in R. We do not cover the more advanced portions of graphics in R in this lesson. These include lattice, ggplot2 and ggvis. There is a school of thought that this approach is backwards, that we should teach ggplot2 first.

Base Graphics

This series aims to take your programming to the next level. The series focuses on aspiring programmers who aim to be better at coding.

Hackerearth Inc.

The collection begins with an indispensable searching algorithm, Binary Search and tries to get a basic understanding of where it can be used. The next two lessons solve problems for the important application of Binary Search in Competitive Programming. In these two lessons, one can Binary Search for the answer in a range of values and learn how to exploit the behavior of a variable.

Discovering Faster Methods To Search For The Answer

This collection has an overview of how to understand the sorting of Algorithms with the help of problems. It begins with a talk about Sorting Algorithms and how to study the functioning of Merge Sort. We continue our discussion on Sorting algorithms, study the working of Quicksort and discuss how the runtime of the algorithm varies. The next part of this collection focuses on Quick Sort and also look at a time efficient but memory constrained algorithm, Count Sort. This collection then goes through solving an elementary problem and introduces the inbuilt sort() function in C++. In the last lesson, we solve another problem using the in-built sort function and see how it can be used on non-primitive data types as well. The series focuses on aspiring programmers who aim to be better at coding.


Understand The Sorting Of Algorithms With The Help Of Problems

Data science is an exciting discipline that allows you to turn raw data into understanding, insight, and knowledge. The goal of this course is to help you learn the most important tool in R that will allow you to do data science. After completing this you will have the tool to tackle a wide variety of data science problems using the best parts of R.

R Programming: Introductory Course in Data Science (Hindi)

Computer science background. Passionate about solving real life problems through algorithms. Intern at GeeksforGeeks. Freelancer.

Dhiman Mayank

This course will discuss algorithms and data structures with important interview questions.

(Hindi) Data Structures - Stack, Queue, Linked List, Sorting and Binary Search

Student studying in VIT university, Pursuing undergrad in IT(information tech),
loves to code and teach.

Kusumanchi Vineeth

In this lesson, Kusumanchi has discussed Algorithm. He has covered sorting of a list in ascending order, key points for sorting, advantages, and disadvantages.

Shell Sorting Algorithm - Data Structures

hlo guys!! myself arpitakaushal I live in ayoudhya .

Arpita Kaushal

In this course, the educator is discussing array in c programming for Programming aspirants

Lessons

16 lessons • 2h 20m

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Course Overview

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Basic Building Blocks

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Workspace and Files

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Sequences and Numbers

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Vectors

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Missing Values

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Subsetting Vectors

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Matrices and Data Frames

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Logic

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Functions

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