What you'll get
- 114+ Hours
- 36 Courses
- Mock Tests
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Complete access to a collection of 36 comprehensive courses, along with a dedicated projects bundle.
- Over 114 hours of detailed video-based learning content.
- In-depth coverage of key areas, including:
- R Programming Fundamentals
- Machine Learning with R
- Business Analytics using R
- Data Visualization in R
- Customer and Marketing Analytics using R
- One-year access to all course materials.
- Suitable for anyone committed to building a career in R programming and analytics.
- Basic familiarity with R is helpful but not mandatory.
- Includes Certificates of Completion for all 36 courses and project work.
- Certificates are verifiable online with unique links for resumes and LinkedIn profiles.
- Fully self-paced training delivered through high-quality video modules.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| R Programming - Practical Data Science Using R | 4h 13m | ✔ | View Curriculum |
| Decision Tree Modeling Using R | 1h 4m | ✔ | View Curriculum |
| Decision Tree Case Study Using R- Bank Loan Default Prediction | 1h 47m | ✔ | View Curriculum |
| Logistic Regression with R | 4h 14m | ✔ | View Curriculum |
| Machine Learning Project-Churn Prediction | 1h 22m | ✔ | View Curriculum |
| R Programming for Data Science | A Complete Courses to Learn | 5h 7m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Machine Learning with R 2022 | 3h 05m | ✔ | View Curriculum |
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Business Analytics - Forecasting using R | 4h 34m | ✔ | View Curriculum |
| Fraud Analytics using R & Microsoft Excel | 2h 34m | ✔ | View Curriculum |
| Marketing Analytics using R and Microsoft Excel | 2h 9m | ✔ | View Curriculum |
| Customer Analytics using R and Tableau | 2h 7m | ✔ | View Curriculum |
| Pricing Analytics using R and Tableau | 2h 39m | ✔ | View Curriculum |
| Project on K-Means Clustering | 43m | ✔ | View Curriculum |
| Machine Learning Project in Python | 1h 58m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Business Analytics using R - Hands-on! | 16h 21m | ✔ | View Curriculum |
| Data Science with R | 6h 2m | ✔ | View Curriculum |
| Comprehensive Course on R | 3h 54m | ✔ | View Curriculum |
| Market Basket Analysis in R | 37m | ✔ | View Curriculum |
| Hypothesis Testing using R | 3h 6m | ✔ | View Curriculum |
| Data Visualization with R Shiny - The Fundamentals | 39m | ✔ | View Curriculum |
| R Studio Anova Techniques Course | 2h 18m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| ggplot2 Project | 2h 07m | ✔ | View Curriculum |
| HR Attrition Using R Project | 2h 08m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using R | 3h 2m | ✔ | View Curriculum |
| Card Purchase Prediction using R | 2h 28m | ✔ | View Curriculum |
| Employee Attrition Prediction Using Random Forest Technique | 1h 6m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using Logistic Regression | 1h 38m | ✔ | View Curriculum |
| Machine Learning Project in Python | 1h 58m | ✔ | View Curriculum |
| Project on K-Means Clustering | 43m | ✔ | View Curriculum |
| Telecom Customer Churn Prediction | 1h 27m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Financial Analytics in R | 3h 45m | ✔ | View Curriculum |
| Quantitative Analysis Using R | 2h 25m | ✔ | View Curriculum |
| Introduction to R for Finance | 2h 17m | ✔ | View Curriculum |
| Financial Analytics in R Intermediate Level | 1h 28m | ✔ | View Curriculum |
| Financial Analytics in R Advanced Level | 1h 35m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| No courses found in this category. | |||
Description
The R Programming and Machine Learning Mastery Program is a well-structured, practical learning pathway designed to help learners build strong expertise in data analysis and machine learning with R. The course is ideal for individuals at beginner and intermediate levels who wish to develop industry-relevant analytical skills.
The program begins by introducing the fundamentals of R programming, RStudio, and essential data handling techniques. Learners steadily progress to advanced topics, exploring supervised learning methods including linear regression, logistic regression, decision trees, and support vector machines. Every concept is supported with hands-on exercises and real-world case studies to ensure practical understanding.
Participants also explore advanced domains like time-series analysis, customer behavior modeling, financial analytics, and predictive analytics. These modules enable learners to apply R across multiple industries, including finance, marketing, operations, and business intelligence.
The course emphasizes experiential learning through capstone projects, practical assignments, quizzes, and mock tests. These activities help learners strengthen their analytical thinking and build a professional portfolio of projects.
By the end of the program, learners gain the ability to work confidently with large datasets, develop predictive models, create meaningful visualizations, and generate valuable business insights using R. The course provides a comprehensive learning solution for anyone aspiring to grow in data science and machine learning.
Sample Certificate
Goals
- Build strong expertise in R programming from basic to advanced levels.
- Develop practical knowledge of machine learning techniques.
- Enable learners to perform real-world data analysis.
- Strengthen decision-making skills through data-driven insights.
- Prepare participants for professional roles in analytics and data science.
Objectives
- Understand core concepts of R programming and data manipulation.
- Learn to build and evaluate predictive models.
- Gain proficiency in data visualization using R.
- Apply machine learning algorithms to business problems.
- Work with real datasets to solve analytical challenges.
- Develop a portfolio of hands-on analytics projects.
- Interpret and communicate analytical results effectively.
Highlights
- Access to 36 well-designed courses in a single program.
- More than 114 hours of expert-led video training.
- Practical learning through real-life datasets and projects.
- Coverage of both basic programming and advanced analytics.
- Practical exposure to building machine learning models and developing data-driven predictions.
- Verifiable certificates for every course completed.
- Self-paced learning with lifetime skill development.
- Capstone projects to build professional credibility.
Requirements
- A fundamental understanding of mathematics and statistics can be helpful.
- No prior programming background is required.
- Basic computer literacy and familiarity with spreadsheets.
- Interest in learning analytics and working with data.
Target Audience
- Individuals new to R programming and data analytics.
- Aspiring data analysts and data scientists.
- Business and domain professionals seeking analytical skills.
- Students and researchers are involved in data-driven projects.
- Marketing, finance, and operations professionals.
- Anyone interested in learning machine learning using R.
FAQ
Q1. Do I need prior coding experience to enroll?
No, the course is designed for beginners and does not require any previous programming knowledge.
Q2. How long will I have access to the course?
Learners receive full access to all materials for one year.
Q3. Will I receive a certificate?
Yes, a verifiable Certificate of Completion is provided for each course.
Q4. Is the course practical or theory-based?
The program is highly practical and project-oriented, with a focus on real-world applications.
Q5. Can the certificates be shared online?
Yes, each certificate includes a unique verification link that learners can add to their resumes and LinkedIn profiles.
Career Benefits
- Develop in-demand skills in R programming and machine learning.
- Become job-ready for roles such as Data Analyst and Data Scientist.
- Gain hands-on project experience valued by employers.
- Improve analytical decision-making capabilities.
- Strengthen professional profiles with verifiable certifications.
- Unlock opportunities in analytics, finance, marketing, and research.
- Build a strong portfolio to showcase practical expertise.
