R Programming for Data Science

R Programming for Data Science



    The R Programming for Data Science is the best way for you to gain deep insight and knowledge of this topic. You will learn from industry experts and obtain an accredited certificate after completing the course. Enrol now for a limited-time discounted price.

    In this course students will be able to start their career in Data Science by learning and practicing the basics of R Language. Students will learn how to install and configure R and RStudio and how to create various data structures. They will solve simple data problems using various R methods, functions, and packages. Students will also understand and use different data gathering, manipulation, and plotting methods to best extract information from raw data. At the end of the course, students will deploy what they have learned in a COVID-19 analysis task.

    Like all the courses of One Education, this R Programming for Data Science is designed with the utmost attention and thorough research. All the topics are broken down into easy to understand bite-sized modules that help our learners to understand each lesson very easily.

    We don’t just provide courses at One Education; we provide a rich learning experience. After purchasing a course from One Education, you get complete 1-year access with tutor support. 

    Our expert instructors are always available to answer all your questions and make your learning experience exquisite.

    After completing the R Programming for Data Science, you will instantly get an e-certificate that will help you get jobs in the relevant field and will enrich your CV.

    If you want to learn about this topic and achieve certifications, you should consider this R Programming for Data Science from One Education.

    There are no hidden fees or exam charges. We are very upfront and clear about all the costs of the course.

    Course design

    The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.

    You are taught through a combination of

    • Video lessons
    • Online study materials

    Will I receive a certificate of completion?

    Upon successful completion, you will qualify for the UK and internationally-recognised certification and you can choose to make your achievement formal by obtaining your PDF Certificate at a cost of £9 and Hard Copy Certificate for £15.

    Why study this course

    Whether you’re an existing practitioner or aspiring professional, this course will enhance your expertise and boost your CV with key skills and an accredited qualification attesting to your knowledge.

    The R Programming for Data Science is open to all, with no formal entry requirements. All you need is a passion for learning, a good understanding of the English language, numeracy and IT, and to be over the age of 16.

    Course Curriculum

    Unit 01: Data Science Overview
    Introduction to Data Science 00:01:00
    Data Science: Career of the Future 00:04:00
    What is Data Science? 00:02:00
    Data Science as a Process 00:02:00
    Data Science Toolbox 00:03:00
    Data Science Process Explained 00:05:00
    What’s Next? 00:01:00
    Unit 02: R and RStudio
    Engine and coding environment 00:03:00
    Installing R and RStudio 00:04:00
    RStudio: A quick tour 00:04:00
    Unit 03: Introduction to Basics
    Arithmetic with R 00:03:00
    Variable assignment 00:04:00
    Basic data types in R 00:03:00
    Unit 04: Vectors
    Creating a vector 00:05:00
    Naming a vector 00:04:00
    Arithmetic calculations on vectors 00:07:00
    Vector selection 00:06:00
    Selection by comparison 00:04:00
    Unit 05: Matrices
    What’s a Matrix? 00:02:00
    Analyzing Matrices 00:03:00
    Naming a Matrix 00:05:00
    Adding columns and rows to a matrix 00:06:00
    Selection of matrix elements 00:03:00
    Arithmetic with matrices 00:07:00
    Additional Materials 00:00:00
    Unit 06: Factors
    What’s a Factor? 00:02:00
    Categorical Variables and Factor Levels 00:04:00
    Summarizing a Factor 00:01:00
    Ordered Factors 00:05:00
    Unit 07: Data Frames
    What’s a Data Frame? 00:03:00
    Creating Data Frames 00:20:00
    Selection of Data Frame elements 00:03:00
    Conditional selection 00:03:00
    Sorting a Data Frame 00:03:00
    Additional Materials 00:00:00
    Unit 08: Lists
    Why would you need lists? 00:01:00
    Creating a List 00:06:00
    Selecting elements from a list 00:03:00
    Adding more data to the list 00:02:00
    Additional Materials 00:00:00
    Unit 09: Relational Operators
    Equality 00:03:00
    Greater and Less Than 00:03:00
    Compare Vectors 00:03:00
    Compare Matrices 00:02:00
    Additional Materials 00:00:00
    Unit 10: Logical Operators
    AND, OR, NOT Operators 00:04:00
    Logical operators with vectors and matrices 00:04:00
    Reverse the result: (!) 00:01:00
    Relational and Logical Operators together 00:06:00
    Additional Materials 00:00:00
    Unit 11: Conditional Statements
    The IF statement 00:04:00
    IF…ELSE 00:03:00
    The ELSEIF statement 00:05:00
    Full Exercise 00:03:00
    Additional Materials 00:00:00
    Unit 12: Loops
    Write a While loop 00:04:00
    Looping with more conditions 00:04:00
    Break: stop the While Loop 00:04:00
    What’s a For loop? 00:02:00
    Loop over a vector 00:02:00
    Loop over a list 00:03:00
    Loop over a matrix 00:04:00
    For loop with conditionals 00:01:00
    Using Next and Break with For loop 00:03:00
    Additional Materials 00:00:00
    Unit 13: Functions
    What is a Function? 00:02:00
    Arguments matching 00:03:00
    Required and Optional Arguments 00:03:00
    Nested functions 00:02:00
    Writing own functions 00:03:00
    Functions with no arguments 00:02:00
    Defining default arguments in functions 00:04:00
    Function scoping 00:02:00
    Control flow in functions 00:03:00
    Additional Materials 00:00:00
    Unit 14: R Packages
    Installing R Packages 00:01:00
    Loading R Packages 00:04:00
    Different ways to load a package 00:02:00
    Additional Materials 00:00:00
    Unit 15: The Apply Family - lapply
    What is lapply and when is used? 00:04:00
    Use lapply with user-defined functions 00:03:00
    lapply and anonymous functions 00:01:00
    Use lapply with additional arguments 00:04:00
    Additional Materials 00:00:00
    Unit 16: The apply Family – sapply & vapply
    What is sapply? 00:02:00
    How to use sapply 00:02:00
    sapply with your own function 00:02:00
    sapply with a function returning a vector 00:02:00
    When can’t sapply simplify? 00:02:00
    What is vapply and why is it used? 00:04:00
    Additional Materials 00:00:00
    Unit 17: Useful Functions
    Mathematical functions 00:05:00
    Data Utilities 00:08:00
    Additional Materials 00:00:00
    Unit 18: Regular Expressions
    grepl & grep 00:04:00
    Metacharacters 00:05:00
    sub & gsub 00:02:00
    More metacharacters 00:04:00
    Additional Materials 00:00:00
    Unit 19: Dates and Times
    Today and Now 00:02:00
    Create and format dates 00:06:00
    Create and format times 00:03:00
    Calculations with Dates 00:03:00
    Calculations with Times 00:07:00
    Additional Materials 00:00:00
    Unit 20: Getting and Cleaning Data
    Get and set current directory 00:04:00
    Get data from the web 00:04:00
    Loading flat files 00:03:00
    Loading Excel files 00:05:00
    Additional Materials 00:00:00
    Unit 21: Plotting Data in R
    Base plotting system 00:03:00
    Base plots: Histograms 00:03:00
    Base plots: Scatterplots 00:05:00
    Base plots: Regression Line 00:03:00
    Base plots: Boxplot 00:03:00
    Unit 22: Data Manipulation with dplyr
    Introduction to dplyr package 00:04:00
    Using the pipe operator (%>%) 00:02:00
    Columns component: select() 00:05:00
    Columns component: rename() and rename_with() 00:02:00
    Columns component: mutate() 00:02:00
    Columns component: relocate() 00:02:00
    Rows component: filter() 00:01:00
    Rows component: slice() 00:04:00
    Rows component: arrange() 00:01:00
    Rows component: rowwise() 00:02:00
    Grouping of rows: summarise() 00:03:00
    Grouping of rows: across() 00:02:00
    COVID-19 Analysis Task 00:08:00
    Additional Materials 00:00:00



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