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Updated in [April 29th, 2023]

This course, Foundations of R Software, is designed to provide an introduction to the fundamentals of R software. It is intended for undergraduate students of Science and Engineering, as well as students of humanities with basic mathematical background. Working professionals in analytics and anyone involved in programming, mathematical and statistical computations, simulations and data analysis can also benefit from this course.

The course requires a mathematics background up to class 12, although having some preliminary knowledge will be helpful but not necessarily mandatory. Participants will learn how to use R software for mathematical and statistical computing, as well as its own programming language, built-in functions, and graphical capabilities.

This course is beneficial for all industries involved in mathematical and statistical computations, programming and simulations and having R & D set up.

[Applications]
The application of this course can be seen in various industries. It can be used to develop mathematical and statistical models, perform simulations, and analyze data. It can also be used to create graphical representations of data and to develop custom functions. Additionally, it can be used to develop applications for data analysis and to create interactive web applications. Furthermore, it can be used to develop machine learning algorithms and to create predictive models. Finally, it can be used to develop applications for data visualization and to create interactive dashboards.

[Career Paths]
1. Data Scientist: Data Scientists use R software to analyze large datasets and uncover trends and insights. They use statistical methods to develop predictive models and algorithms to help businesses make better decisions. They also use R to create visualizations and reports to communicate their findings. As data becomes increasingly important in the business world, the demand for Data Scientists is expected to continue to grow.

2. Machine Learning Engineer: Machine Learning Engineers use R software to develop and deploy machine learning models. They use R to create algorithms and models that can be used to automate tasks and make predictions. They also use R to analyze data and develop insights that can be used to improve the performance of machine learning models. As machine learning becomes more important in the business world, the demand for Machine Learning Engineers is expected to continue to grow.

3. Business Analyst: Business Analysts use R software to analyze data and develop insights that can be used to improve business operations. They use R to create visualizations and reports to communicate their findings. They also use R to develop predictive models and algorithms to help businesses make better decisions. As businesses become increasingly data-driven, the demand for Business Analysts is expected to continue to grow.

4. Research Scientist: Research Scientists use R software to analyze data and develop insights that can be used to advance scientific research. They use R to create visualizations and reports to communicate their findings. They also use R to develop predictive models and algorithms to help researchers make better decisions. As research becomes increasingly data-driven, the demand for Research Scientists is expected to continue to grow.

[Education Paths]
Degree Paths:
1. Bachelor of Science in Computer Science: This degree path provides students with a comprehensive understanding of computer science fundamentals, including programming, software engineering, and data structures. Students will also learn about the latest trends in computer science, such as artificial intelligence, machine learning, and cloud computing.

2. Master of Science in Data Science: This degree path focuses on the application of data science techniques to solve real-world problems. Students will learn about data mining, machine learning, and predictive analytics, as well as the use of big data and analytics tools.

3. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems and their applications. Students will learn about the fundamentals of artificial intelligence, including machine learning, natural language processing, and robotics.

4. Doctor of Philosophy in Machine Learning: This degree path focuses on the development of advanced machine learning algorithms and their applications. Students will learn about the latest trends in machine learning, such as deep learning, reinforcement learning, and generative models.