What you'll get
- 73+ Hours
- 19 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Learn to apply R for practical forecasting and time series analysis.
- One-year access to course materials.
- Suitable for anyone eager to master Time Series Analysis and Forecasting with R.
- Requires basic knowledge of R programming and statistics.
- Receive a Course Completion Certificate for each of the 19 courses, including project work.
- Certificates are verifiable via unique links, ideal for resumes or LinkedIn.
- Self-paced video-based learning.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Data Science with R | 6h 2m | ✔ | View Curriculum |
| Business Analytics using R - Hands-on! | 16h 21m | ✔ | View Curriculum |
| Machine Learning with R | 20h 25m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| 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 |
| Telecom Customer Churn Prediction | 1h 27m | ✔ | View Curriculum |
| Machine Learning Project-Churn Prediction | 1h 22m | ✔ | View Curriculum |
| Decision Tree Case Study Using R- Bank Loan Default Prediction | 1h 47m | ✔ | View Curriculum |
| Business Analytics - Forecasting using R | 4h 34m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Logistic Regression with R | 4h 14m | ✔ | View Curriculum |
| Decision Tree Modeling Using R | 1h 4m | ✔ | View Curriculum |
| Market Basket Analysis in R | 37m | ✔ | View Curriculum |
| Hypothesis Testing using R | 3h 6m | ✔ | View Curriculum |
| ggplot2 Project | 2h 07m | ✔ | View Curriculum |
| HR Attrition Using R Project | 2h 08m | ✔ | View Curriculum |
| Machine Learning Project in Python | 1h 58m | ✔ | View Curriculum |
| Project on K-Means Clustering | 43m | ✔ | View Curriculum |
Description
Time series analysis and forecasting involve predicting future outcomes by examining patterns in sequential data. This approach estimates potential results based on historical data, helping to anticipate trends and make informed decisions. The R programming language provides a practical framework for applying these techniques at the application level, bridging data science concepts with real-world implementation. By leveraging past data, R allows users to develop models that assess probabilities and forecast future events. With a solid understanding of R, learners can efficiently design and implement the logic needed to apply time-series analysis and forecasting effectively.
Sample Certificate
Requirements
- The course assumes familiarity with topics indirectly related to R that support Time Series Analysis and Forecasting.
- Trainees should have a foundational understanding of data science, as the course builds on several concepts.
- Knowledge of machine learning is beneficial, making it easier to tackle units and projects that rely on ML principles.
- Prior experience with these technologies helps accelerate learning, but beginners can still succeed, as the course provides necessary guidance on these concepts.
Target Audience
- The course targets individuals eager to master Time Series Analysis and Forecasting techniques using R.
- Professionals, students, and trainers benefit from the course by gaining practical implementation skills.
- Developers experienced in other programming languages can learn advanced R concepts and apply them to Time Series Analysis and Forecasting.
- Students who want to enhance their R skills can begin with fundamental concepts and advance to more complex applications.
- Educators teaching R programming can acquire the expertise to implement these concepts from the ground up.
