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Data Science and Machine Learning Personal Development

R Programming for Data Science

Overview: Welcome to "R Programming for Data Science"! This course is your gateway to mastering R, a powerful programming language and environment fo...

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129 Lesson

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6hr 32min

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5 students enrolled

Overview:

Welcome to "R Programming for Data Science"! This course is your gateway to mastering R, a powerful programming language and environment for statistical computing and data analysis. R is widely used by data scientists, statisticians, and researchers for its extensive range of libraries and packages tailored for data manipulation, visualization, and modeling. In this course, you'll learn the fundamentals of R programming and how to leverage its capabilities for data science tasks.
  • Interactive video lectures by industry experts
  • Instant e-certificate
  • Fully online, interactive course with Professional voice-over
  • Developed by qualified first aid professionals
  • Self paced learning and laptop, tablet, smartphone friendly
  • 24/7 Learning Assistance
  • Discounts on bulk purchases

Main Course Features:

  • Comprehensive coverage of R programming fundamentals and syntax
  • Hands-on projects and exercises for practical application of concepts
  • Exploration of key R libraries and packages for data manipulation and analysis (e.g., dplyr, ggplot2)
  • Introduction to statistical analysis techniques using R
  • Implementation of machine learning algorithms for predictive modeling and pattern recognition
  • Real-world case studies and examples demonstrating R's application in data science projects
  • Access to resources and tools for continued learning and practice in R programming
  • Supportive online community for collaboration and assistance throughout the course

Who Should Take This Course:

  • Data scientists, statisticians, and researchers looking to enhance their skills in R programming for data science tasks
  • Analysts and professionals seeking to transition into a career in data science
  • Students studying statistics, data analysis, or related fields interested in learning R for practical applications
  • Anyone interested in leveraging R for data manipulation, visualization, and modeling in their personal or professional projects

Learning Outcomes:

  • Master R programming fundamentals and syntax for data manipulation and analysis
  • Understand key R libraries and packages for statistical computing and data visualization
  • Apply statistical techniques to analyze and interpret data effectively using R
  • Develop machine learning models for predictive modeling tasks using R
  • Gain hands-on experience through projects and exercises in R programming
  • Build a portfolio of data science projects showcasing your proficiency in R
  • Communicate findings and insights effectively through data visualization and storytelling in R
  • Continue learning and exploring advanced topics in R programming and data science beyond the course curriculum.

Certification

Once you’ve successfully completed your course, you will immediately be sent a digital certificate. All of our courses are fully accredited, providing you with up-to-date skills and knowledge and helping you to become more competent and effective in your chosen field. Our certifications have no expiry dates, although we do recommend that you renew them every 12 months.

Assessment

At the end of the Course, there will be an online assessment, which you will need to pass to complete the course. Answers are marked instantly and automatically, allowing you to know straight away whether you have passed. If you haven’t, there’s no limit on the number of times you can take the final exam. All this is included in the one-time fee you paid for the course itself.
Course Content
129 Lectures 6hr 32min

  • ImgIntroduction to Data Science

  • ImgData Science: Career of the Future

  • ImgWhat is Data Science?

  • ImgData Science as a Process

  • ImgData Science Toolbox

  • ImgData Science Process Explained

  • ImgWhat’s Next?

  • ImgEngine and coding environment

  • ImgInstalling R and RStudio

  • ImgRStudio: A quick tour

  • ImgArithmetic with R

  • ImgVariable assignment

  • ImgBasic data types in R

  • ImgCreating a vector

  • ImgNaming a vector

  • ImgArithmetic calculations on vectors

  • ImgVector selection

  • ImgSelection by comparison

  • ImgWhat’s a Matrix?

  • ImgAnalyzing Matrices

  • ImgNaming a Matrix

  • ImgAdding columns and rows to a matrix

  • ImgSelection of matrix elements

  • ImgArithmetic with matrices

  • ImgAdditional Materials

  • ImgWhat’s a Factor?

  • ImgCategorical Variables and Factor Levels

  • ImgSummarizing a Factor

  • ImgOrdered Factors

  • ImgWhat’s a Data Frame?

  • ImgCreating Data Frames

  • ImgSelection of Data Frame elements

  • ImgConditional selection

  • ImgSorting a Data Frame

  • ImgAdditional Materials

  • ImgWhy would you need lists?

  • ImgCreating a List

  • ImgSelecting elements from a list

  • ImgAdding more data to the list

  • ImgAdditional Materials

  • ImgEquality

  • ImgGreater and Less Than

  • ImgCompare Vectors

  • ImgCompare Matrices

  • ImgAdditional Materials

  • ImgAND, OR, NOT Operators

  • ImgLogical operators with vectors and matrices

  • ImgReverse the result: (!)

  • ImgRelational and Logical Operators together

  • ImgAdditional Materials

  • ImgThe IF statement

  • ImgIF…ELSE

  • ImgThe ELSEIF statement

  • ImgFull Exercise

  • ImgAdditional Materials

  • ImgWrite a While loop

  • ImgLooping with more conditions

  • ImgBreak: stop the While Loop

  • ImgWhat’s a For loop?

  • ImgLoop over a vector

  • ImgLoop over a list

  • ImgLoop over a matrix

  • ImgFor loop with conditionals

  • ImgUsing Next and Break with For loop

  • ImgAdditional Materials

  • ImgWhat is a Function?

  • ImgArguments matching

  • ImgRequired and Optional Arguments

  • ImgNested functions

  • ImgWriting own functions

  • ImgFunctions with no arguments

  • ImgDefining default arguments in functions

  • ImgFunction scoping

  • ImgControl flow in functions

  • ImgAdditional Materials

  • ImgInstalling R Packages

  • ImgLoading R Packages

  • ImgDifferent ways to load a package

  • ImgAdditional Materials

  • ImgWhat is lapply and when is used?

  • ImgUse lapply with user-defined functions

  • Imglapply and anonymous functions

  • ImgUse lapply with additional arguments

  • ImgAdditional Materials

  • ImgWhat is sapply?

  • ImgHow to use sapply

  • Imgsapply with your own function

  • Imgsapply with a function returning a vector

  • ImgWhen can’t sapply simplify?

  • ImgWhat is vapply and why is it used?

  • ImgAdditional Materials

  • ImgMathematical functions

  • ImgData Utilities

  • ImgAdditional Materials

  • Imggrepl & grep

  • ImgMetacharacters

  • Imgsub & gsub

  • ImgMore metacharacters

  • ImgAdditional Materials

  • ImgToday and Now

  • ImgCreate and format dates

  • ImgCreate and format times

  • ImgCalculations with Dates

  • ImgCalculations with Times

  • ImgAdditional Materials

  • ImgGet and set current directory

  • ImgGet data from the web

  • ImgLoading flat files

  • ImgLoading Excel files

  • ImgAdditional Materials

  • ImgBase plotting system

  • ImgBase plots: Histograms

  • ImgBase plots: Scatterplots

  • ImgBase plots: Regression Line

  • ImgBase plots: Boxplot

  • ImgIntroduction to dplyr package

  • ImgUsing the pipe operator (%>%)

  • ImgColumns component: select()

  • ImgColumns component: rename() and rename_with()

  • ImgColumns component: mutate()

  • ImgColumns component: relocate()

  • ImgRows component: filter()

  • ImgRows component: slice()

  • ImgRows component: arrange()

  • ImgRows component: rowwise()

  • ImgGrouping of rows: summarise()

  • ImgGrouping of rows: across()

  • ImgCOVID-19 Analysis Task

  • ImgAdditional Materials

course

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Includes

  • Img Enrolled: 5 students
  • Img Duration: 6hr 32min
  • Img Chapters: 129