Data Analytics using R programming

Data Analytics using R programming, available at $19.99, has an average rating of 5, with 151 lectures, based on 1 reviews, and has 13 subscribers.

You will learn about What is data and its types Overview of the R programming language. Installation of R and Rstudio in Ubuntu environment Basic syntax and data structures Operators, control and looping statement in R String handling, vector operator in R Built-in and user defined function in R Vectorization in R Data Structure Data Manipulation, Data Reshaping, Data visualization Data visualization using base R, ggplot2 and other visualization libraries. Reading and importing and handling missing data from different source (CSV, Excel, databases). Different Case studies and practical projects. This course is ideal for individuals who are Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics. or IT professionals seeking to expand their skills into the field of data analytics using R. or Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data. It is particularly useful for Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics. or IT professionals seeking to expand their skills into the field of data analytics using R. or Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.

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Summary

Title: Data Analytics using R programming

Price: $19.99

Average Rating: 5

Number of Lectures: 151

Number of Published Lectures: 151

Number of Curriculum Items: 151

Number of Published Curriculum Objects: 151

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

What is data and its types

Overview of the R programming language.

Installation of R and Rstudio in Ubuntu environment

Basic syntax and data structures

Operators, control and looping statement in R

String handling, vector operator in R

Built-in and user defined function in R

Vectorization in R

Data Structure Data Manipulation, Data Reshaping, Data visualization

Data visualization using base R, ggplot2 and other visualization libraries.

Reading and importing and handling missing data from different source (CSV, Excel, databases).

Different Case studies and practical projects.

Who Should Attend

Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics.

IT professionals seeking to expand their skills into the field of data analytics using R.

Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.

Target Audiences

Students pursuing degrees in fields related to data science, statistics, business, or a related discipline who want to build practical skills in data analytics.

IT professionals seeking to expand their skills into the field of data analytics using R.

Individuals with a general interest in data analytics who want to learn how to use R for analyzing and visualizing data.

Unlock the power of data with our comprehensive “Data Analytics Using R Programming” course. In this immersive learning experience, participants will delve into the world of data analytics, mastering the R programming language to extract valuable insights from complex datasets. Whether you’re a seasoned data professional or a newcomer to the field, this course provides a solid foundation and advanced techniques to elevate your analytical skills.

Key Learning Objectives:

R Programming Fundamentals:

Gain a deep understanding of the R programming language, covering syntax, data structures, and essential functions.

Data Import and Cleaning:

Learn how to import data from various sources and perform data cleaning and preprocessing to ensure accurate analysis.

Exploratory Data Analysis (EDA):

Develop skills in descriptive statistics, data summarization, and advanced visualization techniques using ggplot2.

Real-World Applications:

Apply your newfound knowledge to real-world data analytics challenges, working on hands-on projects that simulate the complexities of professional scenarios.

Course Format:

This course is delivered through a combination of video lectures, hands-on exercises, and real-world projects. Participants will have access to a supportive online community and regular opportunities for live Q&A sessions.

By the end of this course, you will be equipped with the skills to navigate the data analytics landscape confidently, making informed decisions and uncovering hidden patterns in data. Join us on this journey to become a proficient data analyst using the versatile R programming language. Enroll today and harness the power of data!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Prerequisites

Chapter 2: Data Analytics

Lecture 1: What is Data

Lecture 2: Importance of Data

Lecture 3: Type of Data – Categorical

Lecture 4: Type of Data – Numerical

Lecture 5: Analytics and Analysis

Lecture 6: Data Analytics

Lecture 7: Data Analysis

Lecture 8: Classification of Data Analytics

Lecture 9: Process

Chapter 3: Intro to R and R studio

Lecture 1: Introduction to R

Lecture 2: Benefits of R

Chapter 4: R and R studio installation in Ubuntu

Lecture 1: install R in Ubuntu GUI

Lecture 2: install R in Ubuntu terminal

Lecture 3: R studio GUI overview

Lecture 4: How to create and run R file in GUI

Lecture 5: How to save and run R file in Terminal

Lecture 6: Rdata and Rhistory

Chapter 5: R programming Basics

Lecture 1: Variable in R

Lecture 2: DataTypes in R

Lecture 3: Print vs Cat function in R

Lecture 4: ls,rm function in R

Lecture 5: Rules to create variable in R

Lecture 6: Special keywords in R

Lecture 7: Different datatypes in R

Lecture 8: Vectorization in R

Lecture 9: Implicit Cohesion

Lecture 10: ls function in detail

Chapter 6: Operators in R

Lecture 1: Operators in R

Lecture 2: Arithmetic Operators

Lecture 3: Relational Operators

Lecture 4: Logical Operators

Lecture 5: Miscellaneous Operators

Lecture 6: R basics summary

Chapter 7: Control structures in R

Lecture 1: Conditional statement – if, else, else if

Lecture 2: Conditional statement – switch

Lecture 3: Lab exercise

Chapter 8: Looping Statement in R

Lecture 1: For

Lecture 2: While

Lecture 3: Repeat

Chapter 9: String Handling in R

Lecture 1: getting user input and explicit cohersion

Lecture 2: getting user input part 2

Lecture 3: logical check for string – grepl and grep

Lecture 4: print vs cat vs paste method

Lecture 5: String methods – toupper, tolower, substr, format

Chapter 10: Vector operation in R

Lecture 1: Indexing in vector

Lecture 2: Indexing in vector – part 2

Lecture 3: Built-in operation in R

Lecture 4: Repeat operation in R

Lecture 5: Lab exercise

Lecture 6: Lab solution – part 1

Lecture 7: Lab solution – part 2

Chapter 11: Functions in R

Lecture 1: Intro to Function in R

Lecture 2: Built-in function – seq, seq_along

Lecture 3: Built-in function – seq_len

Lecture 4: Built-in function rnorm

Lecture 5: law of large number

Lecture 6: Built-in function rnorm – part 2

Lecture 7: Built-in function – runif

Lecture 8: Built-in function – sample

Lecture 9: Lab exercise

Lecture 10: Lab solution – part 1

Lecture 11: Lab solution – part 2

Lecture 12: Lab solution – part 3

Chapter 12: User defined function in R

Lecture 1: User defined function – part 1

Lecture 2: User defined function – part 2

Lecture 3: User defined function – part 3

Lecture 4: User defined function – part 4

Lecture 5: User defined function – part 5

Lecture 6: User defined function – part 6

Lecture 7: User defined function – part 7

Lecture 8: User defined function – part 8

Lecture 9: Lab exercise

Chapter 13: Vectorization in R

Lecture 1: Vectorized Approach

Lecture 2: Vectorized Function

Chapter 14: Data Structure in R

Lecture 1: Introduction to Data Structure

Lecture 2: List – Part 1

Lecture 3: List – Part 2

Lecture 4: List summary

Lecture 5: Manipulating List

Lecture 6: Converting List to Vector

Lecture 7: Matrix – Part 1

Lecture 8: Matrix – Part 2

Lecture 9: Matrix – Part 3

Lecture 10: Matrix – Part 4

Instructors

Vignesh Muthuvelan
Technical Trainer

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Frequently Asked Questions

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