R Programming for Data Science

4.1 ★★★★ ★

R Programming for Data Science

Name: R Programming for Data Science | Online Course | tutorialspoint
Rating: 4.1 (101 reviews)
Author: Mohammed Barakat

Basics of R programming as a preliminary step to become a Data Scientist

updated on icon Updated on Jul, 2024

language icon Language - English

person icon Mohammed Barakat

English [CC]

category icon Data Science,R Programming,Data Analysis,Development

Lectures -133

Resources -25

Duration -6 hours

4.1 ★★★★ ★

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Course Description

This course provides an overview of Data Science and how R language with its methods and functions constitutes a primary tool to become a Data Scientist. The course presents an introduction to Data Science and Big Data. Then, it gives an overview of R and RStudio software and how to set them up. Lastly, it explains in detail, with the aid of numerous exercises, how to use various R methods and functions to analyze data.

The course demonstrates the importance and advantages of R language as a start, then it presents topics on R data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames and lists. Besides, it includes topics on operators, conditionals, loops, functions, and packages. It also covers regular expressions, getting and cleaning data, plotting, and data manipulation using the dplyr package.

The ever-increasing size of data globally coupled with the prominent need to extract insightful information out of it necessitate learning analytical programming languages such as R. This course paves the road for beginners to start using R in real life analysis tasks and research projects to enable a fact-based decision-making process.

Goals

Having completed this course, you will be able to:

Describe Data Science and Big Data
Recognize the importance of Data Science
Explain the Data Science process
Identify main tools used in Data Science
Explain the steps of a Data Science project
Recognize the main environment and files of RStudio
Complete installing R and R Studio on own machine
Solve arithmetic calculations in R
Distinguish between different data types in R
Solve data problems using vectors, matrices, factors, data frames, and lists in R
Formulate controlled-flow data problems using Operators, Conditional Statements, and Loops
Recognize base R functions and user-defined functions in R
Analyze data using base mathematical functions, R Packages, and Apply function family
Modify data using Regular Expressions and Dates & Times functions
Integrate and cleanse external data in R
Plot data in R
Evaluate datasets in R using dplyr package

Prerequisites

No prior knowledge is mandatory to this course.
Passion towards learning programming and statistics is essential

Curriculum

Check out the detailed breakdown of what’s inside the course

Data Science Overview
7 Lectures

About this section
Introduction to Data Science 00:35 00:35
Data Science: Career of the Future 03:42 03:42
What is Data Science? 02:00 02:00
Data Science as a Process 02:14 02:14
Data Science Toolbox 03:25 03:25
Data Science Process Explained 05:21 05:21

R and RStudio
4 Lectures

Introduction to Basics
4 Lectures

Vectors Data Structure
6 Lectures

Matrices Data Structure
7 Lectures

Factors Data Structure
5 Lectures

Data Frames Structure
6 Lectures

Lists Data Structure
5 Lectures

Relational Operators
5 Lectures

Logical Operators
5 Lectures

Conditional Statements
4 Lectures

Loop Structures
10 Lectures

Functions in R
10 Lectures

Packages in R
4 Lectures

The apply Family – lapply
5 Lectures

The apply family – sapply & vapply
7 Lectures

Useful Functions in R
3 Lectures

Regular Expressions
5 Lectures

Dates and Times
6 Lectures

Getting and Cleaning Data
5 Lectures

Data Manipulation with dplyr
13 Lectures

Plotting Data in R
6 Lectures

COVID19 Analysis: Case Study
1 Lectures

Instructor Details

Mohammed Barakat

Barakat holds a B.Sc. degree in Industrial Engineering from the University of Jordan and has 21 years of experience in diverse industries.

Throughout his career Mohammed focused on Process Management and Improvement. He leveraged the concepts of Lean and Six Sigma in the workplace. And recently, he has been involved in the field of Data Science and Machine Learning.

In his capacity as a process improvement professional he has managed and implemented a multitude of improvement projects using Six Sigma, Lean, Theory of Constraints, and Kaizen.

As a programmer and data analyst, he started his journey in early 2000’s with programming by developing Excel-VBA macros to provide quick solutions in his work. He managed to develop robust applications in MS Access that cut down on labor cost and made processes more efficient.

He kept pace with advancement of data analysis, and he entered Data Science field in 2013. Barakat has developed several R scripts and reproducible researches handling statistical and machine learning algorithms.

Mohammed is a Jordan Consultant Engineer (JCE), and he holds several certifications in Project Management (PMP and PMI-RMP) and Six Sigma (ASQ Six Sigma Black Belt and Green Belt). He is also a Microsoft Certified Trainer and Technology Specialist from Microsoft.

Course Certificate

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