R Programming for Data Science & Machine Learning

Uplatz offers extensive course on R Programming for Data Science & Machine Learning. It is a self-paced video course. You will be awarded Course Completion Certificate at the end of the course.

R is a high-level programming language for statistical analysis, graphics representation and reporting. R is freely available under the GNU General Public License, and pre-compiled binary versions are provided for various operating systems like Linux, Windows and Mac. R is a powerful language for data analysis, data visualization, machine learning, statistics. Originally developed for statistical programming, it is now one of the most popular languages in data science.

R provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and graphical data representation. It also offers various data manipulation and cleaning tools, as well as a wide range of add-on packages for specialized tasks.

R programming language is a popular language among statisticians, data analysts, and researchers who deal with large amounts of data. Here are some key characteristics of R programming language:

1. Open source: R is free and open-source software, which means that it can be used, modified, and distributed by anyone.

2. Graphics: R provides a wide range of graphical capabilities, including static and interactive visualizations, which can be used to analyze and communicate data effectively.

3. Large community: R has a large community of users and developers, which means that there are many resources available for learning and troubleshooting.

4. Packages: R has over 18,000 packages available on the Comprehensive R Archive Network (CRAN) that extend its functionality and can be used for a wide variety of applications.

To learn R programming language, here are some steps you can follow:

1. Install R: You can download and install R from the Comprehensive R Archive Network (CRAN) website. It is available for Windows, Mac, and Linux.

2. Choose an Integrated Development Environment (IDE): An IDE makes it easier to write and run R code. Some popular IDEs for R are RStudio, Visual Studio Code, and Jupyter Notebook.

3. Learn the basics: Start by learning the basics of R syntax, data types, and data structures. You can find many online resources, such as tutorials, books, and videos.

4. Practice coding: Start coding small programs and practice coding with real-world data sets. This will help you to get familiar with R and its capabilities.

5. Explore packages: R has a vast library of packages that can help you to perform specific tasks. You can explore these packages and find the ones that are relevant to your work.

6. Join the community: Join R user groups, forums, and mailing lists to connect with other R users and get help with your coding challenges.

Overall, learning R programming language can be challenging but rewarding, and it can help you to unlock the full potential of your data analysis projects.

In this R Programming for Beginnerscourse, you'll be learning about the basics of R, and you'll end with the confidence to start writing your own R scripts. But this isn't your typical textbook introduction to R. You're not just learning about R fundamentals, you'll be using R to solve problems related to movies data. Using a concrete example makes the learning painless. You will learn about the fundamentals of R syntax, including assigning variables and doing simple operations with one of R's most important data structures -- vectors! From vectors, you'll then learn about lists, matrix, arrays and data frames. Then you'll jump into conditional statements, functions, classes and debugging. Once you've covered the basics - you'll learn about reading and writing data in R, whether it's a table format (CSV, Excel) or a text file (.txt). Finally, you'll end with some important functions for character strings and dates in R.

• R Programming for Data Science & Machine Learning - all lectures 8:40:24

  • 1: Introduction to R Language Preview 14:35
  • 2: R Installation Preview 08:50
  • 3: R Data Structures 07:10
  • 4: R Data Types - part 1 04:41
  • 5: R Data Types - part 2 19:11
  • 6: R Packages 11:25
  • 7: Simple Calculator using R 22:34
  • 8: R as a Calculator 18:49
  • 9: Condition Statements in R - part 1 28:49
  • 10: Condition Statements in R - part 2 16:16
  • 11: Looping in R 25:02
  • 12: Repeat Statement in R 15:32
  • 13: Sum of N Natural Numbers 07:18
  • 14: Sum Recursion 05:53
  • 15: Switch Statement in R 33:57
  • 16: Data Pre-processing 1:01:56
  • 17: Functions in R 37:44
  • 18: Factors in R 37:02
  • 19: Data Frames in R 37:47
  • 20: Merging Data Frame in R 20:37
  • 21: Data Reshaping - part 1 25:11
  • 22: Data Reshaping - part 2 23:50
  • 23: Data Reshaping - part 3 10:08
  • 24: R Math built-in Functions 14:32
  • 25: Melting and Casting in R 11:35

R Programming for Data Science & Machine Learning - Course Curriculum

1.1. FUNDAMENTALS OF R

• Installation of R & R Studio
• Features of R
• Variables in R
• Constants in R
• Operators in R
• Datatypes and R Objects
• Accepting Input from keyboard
• Important Built-in functions

1.2. VECTORS

• Creating Vectors
• Accessing elements of a Vector
• Operations on Vectors
• Vector Arithmetic

1.3. CONTROL STATEMENTS

• I statement
• if…else statement
• if else() function
• switch() function
• repeat loop
• while loop
• for loop
• break statement
• next statement

1.4. FUNCTIONS IN R

• Formal and Actual arguments
• Named arguments
• Global and local variables
• Argument and lazy evaluation of functions
• Recursive functions

1.5. MATRICES

• Creating matrices
• Accessing elements of a Matrix
• Operations on Matrices
• Matrix transpose

1.6. STRINGS

• Creating strings
• paste() and paste0()
• Formatting numbers and string using format()
• String manipulation

1.7. LISTS

• Creating lists
• Manipulating list elements
• Merging lists
• Converting lists to vectors

1.8. ARRAYS IN R

• Creating arrays
• Accessing array elements
• Calculations across array elements

1.9. R FACTORS

• Understanding factors
• Modifying factors
• Factors in Data frames

1.10. DATA FRAMES IN R

• Creating data frame
• Operations on data frames
• Accessing data frames
• Creating data frames from various sources

1.11. DATA VISUALIZATION IN R

• Need for data visualization
• Bar plot
• Plotting categorical data
• Stacked bar plot
• Histogram
• plot() function and line plot
• pie chart / 3D pie chart
• Scatter plot
• Box plot

1.12. STRINGR PACKAGE

• Important functions in stringr
• Regular expressions

1.13. DPLYR PACKAGE

Everyone

Passion and determination to achieve your goals!

• Software Engineer - R, Python
• R Programmer Analyst
• Data Analyst & Consultant
• Data Scientist
• Data Architect
• Application Developer
• Software Developer & Programmer
• Application Developer
• Business Analyst
• BI & Reporting Developer/Analyst
• IT Consultant
• Technical Lead
• Solution Architect
• Data Science Engineer - Machine Learning/R
• Machine Learning Engineer
• R Programming Application Developer

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Certificate of completion

Digital certificate - Included

Reed courses certificate of completion

Digital certificate - Included