Why learn R?

All in all R is a programming language and free software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. Furthermore the R language is widely used among statisticians and data miners for developing statistical software and data analysis.. At School of it we offer:

• An intro python/programming in R course (for the fundamentals),
• An advanced python course (more intermediate and advanced python).
• A full stack web development course with python or R.
• A Software Development course in python (which includes intro, intermediate and advanced python concepts).
• Software Engineering course in python and 1 other programming language (which includes intro, intermediate and advanced of the 2 programming languages).
• A Data Science course which makes use of python or R programming.

Learn R anytime, anywhere!

Overall learn R programming anytime and anywhere. We have 3 training options and we have award-winning coding courses. In turn identify and implement client side and integration technologies. Thus learn how to create and manage your own stats and graphs using RStudio.

Part-Time

Fast forward your career in the IT industry with a part-time Programming in R courses at School of IT. All in all the Part-time courses allow working professionals to transition into a new skill set while working. In turn at School of IT we are agile and can customize a Programming in R course to the individual. Start anytime and choose your hours!

Full Time

Are you ready to start a career in IT? In turn learn to code in R as a full time student at School of IT. Thus beginning your career as a data analyst or data scientist. Start anytime and manage your own contact hours with your mentor!

High School

In addition prepare for the future by learn to code in R while you’re still in high school. Have the option to get internationally accredited and recognized before you even finish school!

Corporate

All in all upskill yourself or your company by learning to code while you’re working. Thus no matter where you are, you can upskill yourself and get internationally accredited and recognized in under 6 months!

Overall by the end of the intro R programming course students will have usable knowledge of the following:

• Understand R syntax, R and available GUIs.
• Using RStudio and Related software documentation.
• Learn R and statistics.
• Decision Making.
• Create functions and features.
• Using R commands, case sensitivity, etc.
• Execute commands input and output to a file.
• Datatypes together with operators.
• Variable declaration including initialization.
• Objects, their modes and attributes.
• Lists and data frames.
• Basics of Loops.
• Data manipulation.
• Statistical analysis in R.
• Automated and interactive reporting.
• Matrices and Arrays.

By the end of the Full Stack course students will have usable knowledge of the following:

• Understand the Fundamentals of web design
• Be able to understand classes coupled with Objects.
• Understand of datatypes together with operators.
• Understand variable declaration including initialization.
• Learn Methods, functions coupled with sequential code.
• Decision making: If statements not to mention switch cases.
• Learn about Loops, namely a do while, for loop and while loop.
• How to use Django and Programming in R Fundamentals.
• Create Websites using Django Framework and best development principals & standards
• IDE’s, applets coupled with publishing applications.
• Including HTML, CSS, JavaScript and Web Services.

By the end of the Data Science course students will have usable knowledge of the following:

• Apply statistical methods to data
• Be able to understand Programming in R to explore and transform data.
• Create,validate machine learning models with Azure Machine Learning.
• Thoroughly Understand R and RStudio to code to build machine learning models.
• Apply data science techniques to common scenarios.
• Implement a machine learning solution for a given data problem.
• Learn advanced R programming and Data Analysis to implement computer science algorithms.
• Implement a BioInformatics and natural language processing.
• Acquire the skills needed to find structure in data and how to use the R libraries.
• Dplyr.
• Lubridate.
• Knitr.
• Shiny.
• BioConductor.

Job Opportunities

The career prospects for Data Scientists and Python programmers are excellent and high in demand. Data is everywhere: on all platforms and devices and in all countries around the world!

• Junior Programmer.
• Data Analysts.
• Full Stack Web Developer.
• Data Scientist.
• Software Developer.
• Software Engineer.
• App Developer.
• Systems Architect.