What is R Programming: Basic to Advanced about?

This course teaches R programming from basic to advanced level, covering programming foundations, data handling, data analysis, statistics, visualization, research workflows, and real-world analytical applications.

Who should enroll in this R programming basic to advanced course?

This course is suitable for R programming beginners, data science learners, statistics students, research scholars, analytics professionals, biotechnology students, public health learners, business analysts, computer science students, PhD scholars, academicians, and professionals interested in data analysis and statistical computing using R.

Does this course cover data analysis and statistics in R?

Yes. The course covers data analysis, statistical methods, data cleaning, data manipulation, descriptive statistics, analytical workflows, and practical R programming techniques for working with structured datasets.

Will learners understand data visualization and data handling?

Yes. Learners explore data handling, importing datasets, cleaning data, transforming variables, creating visualizations, interpreting charts, and using R to communicate analytical insights clearly.

Is this course useful for research, analytics, and data science careers?

Yes. The course helps learners build foundational and advanced knowledge of R programming, data analysis, statistics, visualization, data handling, and research applications useful for careers in data science, analytics, academic research, biostatistics, business intelligence, public health, and scientific computing.

R Programming: Basic To Advanced

Attribute

Detail

Format

Online (e-LMS)

Level

Advanced

Duration

6 Months

Certification

e-Certification + e-Marksheet

Tools

R, RStudio, Python, TensorFlow

About the R Programming Course

R Programming: Basic to Advanced dives deep into R Programming Basic To.

Gain comprehensive expertise through our structured curriculum and hands-on approach.

Program Highlights

• Comprehensive coverage of R Programming from fundamentals to advanced applications

• Hands-on projects and real-world case studies in Data Science

• Expert-curated curriculum aligned with current industry standards

• Access to recorded lectures and e-LMS platform for flexible, self-paced learning

• e-Certification and e-Marksheet upon successful completion

• Dedicated mentor support and interactive doubt-clearing sessions

• Practical experience with tools: R, RStudio, Python, TensorFlow

• Career-oriented training for academic and professional growth in Data Science

Course Curriculum

Module 1: AI Fundamentals, Mathematics, and R Programming Foundations

Apply mathematical concepts such as linear algebra and calculus to solve problems in R programming
Develop a solid understanding of AI fundamentals, including machine learning and deep learning concepts
Configure R programming environments, including setting up RStudio and installing necessary packages

Module 2: Data Engineering, Preprocessing, and Feature Pipelines

Design and implement data pipelines using R programming, including data ingestion, processing, and storage
Analyze and preprocess datasets to prepare them for modeling, including handling missing values and outliers
Evaluate the quality of datasets and develop strategies for data augmentation and feature engineering

Module 3: Model Architecture, Algorithm Design, and R Programming Methods

Implement machine learning algorithms, including regression, classification, and clustering, using R programming
Develop and evaluate model architectures, including neural networks and decision trees
Optimize model performance using techniques such as cross-validation and hyperparameter tuning

Module 4: Training, Hyperparameter Optimization, and Evaluation

Train and evaluate machine learning models using R programming, including metrics such as accuracy and precision
Configure and optimize hyperparameters using techniques such as grid search and random search
Develop strategies for model selection and ensemble methods, including bagging and boosting

Module 5: Deployment, MLOps, and Production Workflows

Deploy machine learning models using R programming, including model serving and monitoring
Develop and implement MLOps workflows, including continuous integration and deployment
Configure and manage production environments, including containerization and orchestration

Module 6: Ethics, Bias Mitigation, and Responsible AI Practices

Analyze and mitigate bias in machine learning models, including fairness and transparency
Develop and implement responsible AI practices, including explainability and accountability
Evaluate the ethical implications of AI systems, including privacy and security

Module 7: Industry Integration, Business Applications, and Case Studies

Apply machine learning concepts to real-world business problems, including marketing and finance
Develop and implement industry-specific solutions, including healthcare and finance
Evaluate the effectiveness of AI systems in various industries, including case studies and success stories

Tools, Techniques, or Platforms Covered

R
RStudio
Python
TensorFlow

Real-World Applications

Apply R Programming skills directly to academic research, thesis work, and publications
Build a professional portfolio showcasing practical Data Science competencies
Solve industry-relevant problems using R Programming methodologies and tools
Contribute to open-source projects and collaborative research in Data Science
Prepare for competitive examinations, interviews, and professional certifications in Data Science

Who Should Attend & Prerequisites

Designed for Professionals.
Designed for Students.
Working experience with artificial intelligence tools and prior coursework in related topics expected.
Mentorship by industry experts and NSTC faculty.

Prerequisites:

Frequently Asked Questions

1. What is the format of this R Programming: Basic to Advanced course?

This is an Online (e-LMS) course delivered via our e-LMS platform. You will have access to pre-recorded video lectures, reading materials, assignments, quizzes, and hands-on projects that you can complete at your own pace.

2. Will I receive a certificate after completing this course?

Yes! Upon successful completion of all modules, assignments, and assessments, you will receive an e-Certification along with an e-Marksheet from NanoSchool (NSTC) that you can showcase on your CV and LinkedIn profile.

3. What are the prerequisites for this course?

Learners should have a foundational understanding of Data Science concepts. Familiarity with basic tools and programming is recommended.

4. How long will I have access to the course materials?

You will have access to all course materials for the duration of 6 Months. The self-paced format allows you to learn according to your own schedule through our online learning management system.

5. Is mentor support available during the course?

Yes, dedicated mentor support is available throughout the course. You can reach out for doubt-clearing sessions, project guidance, and career advice related to Data Science. Our mentors are industry experts and experienced professionals.

Enroll in R Programming: Basic to Advanced today and take the next step in your professional journey. With expert-curated content, practical projects, and industry-recognized certification, this course is your gateway to mastering Data Science skills that matter.

Format	Online (e-LMS)

Achieve Excellence & Enter the Hall of Fame!

Elevate your research to the next level! Get your groundbreaking work considered for publication in prestigious Open Access Journal (worth USD 1,000) and Opportunity to join esteemed Centre of Excellence. Network with industry leaders, access ongoing learning opportunities, and potentially earn a place in our coveted

Hall of Fame.

Achieve excellence and solidify your reputation among the elite!