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  • 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
  • Plot data in R
  • Evaluate datasets in R using dplyr package

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.

  • No prior knowledge is mandatory to this course.
  • Passion towards learning programming and statistics is essential
  • Professionals and academics who aspire to use R Language as part of their Data Analysis and Data Science tasks.
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  • Section 1 : Data Science Overview 6 Lectures 00:00:00

    • Lecture 1 :
    • Introduction to Data Science Preview
    • Lecture 2 :
    • Data Science: Career of the Future
    • Lecture 3 :
    • What is Data Science?
    • Lecture 4 :
    • Data Science as a Process
    • Lecture 5 :
    • Data Science Toolbox
    • Lecture 6 :
    • Data Science Process Explained
  • Section 2 : R and RStudio 3 Lectures 00:00:00

    • Lecture 1 :
    • Engine and Coding Environment
    • Lecture 2 :
    • Installing R and RStudio
    • Lecture 3 :
    • RStudio: A quick tour
  • Section 3 : Introduction to Basics 3 Lectures 00:10:06

    • Lecture 1 :
    • Arithmetic with R
    • Lecture 2 :
    • Variable Assignment
    • Lecture 3 :
    • Basic Data Types in R
  • Section 4 : Vectors Data Structure 5 Lectures 00:25:46

    • Lecture 1 :
    • Creating a Vector
    • Lecture 2 :
    • Naming a Vector
    • Lecture 3 :
    • Arithmetic Calculations on Vectors
    • Lecture 4 :
    • Vector Selection
    • Lecture 5 :
    • Selection by Comparison
  • Section 5 : Matrices Data Structure 6 Lectures 00:25:43

    • Lecture 1 :
    • What is a Matrix
    • Lecture 2 :
    • Analyzing Matrices
    • Lecture 3 :
    • Naming a Matrix
    • Lecture 4 :
    • Adding columns and rows to a matrix
    • Lecture 5 :
    • Selection of matrix elements
    • Lecture 6 :
    • Arithmetic with matrices
  • Section 6 : Factors Data Structure 4 Lectures 00:01:56

    • Lecture 1 :
    • What is a Factor
    • Lecture 2 :
    • Categorical Variables and Factor Levels
    • Lecture 3 :
    • Summarizing a Factor
    • Lecture 4 :
    • Ordered Factors
  • Section 7 : Data Frames Structure 5 Lectures 00:03:19

    • Lecture 1 :
    • What is a Data Frame
    • Lecture 2 :
    • Creating a Data Frame
    • Lecture 3 :
    • Selection of Data Frame elements
    • Lecture 4 :
    • Conditional selection
    • Lecture 5 :
    • Sorting a Data Frame
  • Section 8 : Lists Data Structure 4 Lectures 00:00:00

    • Lecture 1 :
    • Why would you need lists?
    • Lecture 2 :
    • Creating a List
    • Lecture 3 :
    • Selecting elements from a list
    • Lecture 4 :
    • Adding more data to the list
  • Section 9 : Relational Operators 4 Lectures 00:00:00

    • Lecture 1 :
    • Equality
    • Lecture 2 :
    • Greater and Less Than
    • Lecture 3 :
    • Compare Vectors
    • Lecture 4 :
    • Compare Matrices
  • Section 10 : Logical Operators 4 Lectures 00:00:00

    • Lecture 1 :
    • AND, OR, NOT Operators
    • Lecture 2 :
    • Logical operators with vectors and matrices
    • Lecture 3 :
    • Reverse the result: (!)
    • Lecture 4 :
    • Relational and Logical Operators together
  • Section 11 : Conditional Statements 3 Lectures 00:00:00

    • Lecture 1 :
    • The IF statement
    • Lecture 2 :
    • IF…ELSE
    • Lecture 3 :
    • The ELSEIF statement
  • Section 12 : Loop Structures 9 Lectures 00:00:00

    • Lecture 1 :
    • Write a While loop
    • Lecture 2 :
    • Looping with more conditions
    • Lecture 3 :
    • Break: stop the While Loop
    • Lecture 4 :
    • What’s a For loop?
    • Lecture 5 :
    • Loop over a vector
    • Lecture 6 :
    • Loop over a list
    • Lecture 7 :
    • Loop over a matrix
    • Lecture 8 :
    • For loop with conditionals
    • Lecture 9 :
    • Using Next and Break with For loop
  • Section 13 : Functions in R 9 Lectures 00:00:00

    • Lecture 1 :
    • What is a Function?
    • Lecture 2 :
    • Arguments matching
    • Lecture 3 :
    • Required and Optional Arguments
    • Lecture 4 :
    • Nested functions
    • Lecture 5 :
    • Writing own functions
    • Lecture 6 :
    • Functions with no arguments
    • Lecture 7 :
    • Defining default arguments in functions
    • Lecture 8 :
    • Function scoping
    • Lecture 9 :
    • Control flow in functions
  • Section 14 : Packages in R 3 Lectures 00:00:00

    • Lecture 1 :
    • Installing R Packages
    • Lecture 2 :
    • Loading R Packages
    • Lecture 3 :
    • Different ways to load a package
  • Section 15 : The apply family – lapply 4 Lectures 00:00:00

    • Lecture 1 :
    • What is lapply and when is used?
    • Lecture 2 :
    • Use lapply with user-defined functions
    • Lecture 3 :
    • lapply and anonymous functions
    • Lecture 4 :
    • Use lapply with additional arguments
  • Section 16 : The apply family – sapply & vapply 6 Lectures 00:00:00

    • Lecture 1 :
    • What is sapply
    • Lecture 2 :
    • How to use sapply
    • Lecture 3 :
    • sapply with your own function
    • Lecture 4 :
    • sapply with a function returning a vector
    • Lecture 5 :
    • When can not sapply simplify
    • Lecture 6 :
    • What is vapply and why is it used
  • Section 17 : Useful Functions in R 2 Lectures 00:00:00

    • Lecture 1 :
    • Mathematical functions
    • Lecture 2 :
    • Data Utilities
  • Section 18 : Regular Expressions 4 Lectures 00:00:00

    • Lecture 1 :
    • grepl & grep
    • Lecture 2 :
    • Metacharacters
    • Lecture 3 :
    • sub & gsub
    • Lecture 4 :
    • More metacharacters
  • Section 19 : Dates and Times 5 Lectures 00:00:00

    • Lecture 1 :
    • Today and Now
    • Lecture 2 :
    • Create and format dates
    • Lecture 3 :
    • Create and format times
    • Lecture 4 :
    • Calculations with Dates
    • Lecture 5 :
    • Calculations with Times
  • Section 20 : Getting and Cleaning Data 4 Lectures 00:00:00

    • Lecture 1 :
    • Get and set current directory
    • Lecture 2 :
    • Get data from the web
    • Lecture 3 :
    • Loading flat files
    • Lecture 4 :
    • Loading Excel files
  • Section 21 : Data Manipulation with dplyr 12 Lectures 00:00:00

    • Lecture 1 :
    • Introduction to dplyr package
    • Lecture 2 :
    • Using the pipe operator (%>%)
    • Lecture 3 :
    • Columns component: select()
    • Lecture 4 :
    • Columns component: rename() and rename_with()
    • Lecture 5 :
    • Columns component: mutate()
    • Lecture 6 :
    • Columns component: relocate()
    • Lecture 7 :
    • Rows component: filter()
    • Lecture 8 :
    • Rows component: slice()
    • Lecture 9 :
    • Rows component: arrange()
    • Lecture 10 :
    • Rows component: rowwise()
    • Lecture 11 :
    • Grouping of rows: summarise()
    • Lecture 12 :
    • Grouping of rows: across()
  • Section 22 : Plotting Data in R 5 Lectures 00:00:00

    • Lecture 1 :
    • Base plotting system
    • Lecture 2 :
    • Base plots: Histograms
    • Lecture 3 :
    • Base plots: Scatterplots
    • Lecture 4 :
    • Base plots: Regression Line
    • Lecture 5 :
    • Base plots: Boxplot
  • Section 23 : COVID19 Analysis: Case Study 1 Lectures 00:00:00

    • Lecture 1 :
    • COVID19 Analysis: Case Study
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Mohammed Barakat holds a B.Sc. degree in Industrial Engineering and has been working in diverse industries for more than two decades. His area of focus is Process Management and Data Analysis. In his capacity as a process improvement expert, he managed and implemented a multitude of improvement projects using Six Sigma, Lean, Theory of Constraints, and Kaizen. And as a programmer and data scientist, he started his journey with Microsoft Office VBA (Visual Basic) programming to cut down on labor cost and improve processes efficiency. Then, he joined the Data Science field and has developed several R scripts and reproducible research handling statistical and machine learning algorithms. Barakat is a Jordan Consultant Engineer (JCE), 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 (MCT) and a Microsoft Certified Technology Specialist (MCTS). He was named a winner of the ASQ MEA Quality Professionals Award for the year 2018.
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