What you'll get
- 73+ Hours
- 19 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- This program is designed to equip learners with practical skills in using the R programming language for real-world forecasting and time-series analysis.
- Learners receive one year of course access.
- Suitable for individuals who are committed to gaining expertise in Time Series Analysis and Forecasting with R.
- Foundational understanding of R programming and basic statistical concepts is recommended.
- A certificate of completion for each of the 19 included courses, along with hands-on project experience.
- Each certificate includes a unique verification link that can be shared on resumes or professional networking platforms.
- 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
This course introduces learners to the principles and practical applications of time-series analysis and forecasting in R. Time-series techniques analyze data collected over time to identify trends, patterns, and future probabilities. By applying R at an application level, learners gain the ability to translate theoretical data science concepts into actionable forecasting models. The curriculum is structured to ensure clarity, making it approachable for individuals who already possess a working knowledge of R. Through guided instruction and real-world examples, participants develop the capability to design logic that interprets historical data and predicts likely future outcomes with confidence.
Sample Certificate
Goals
- Enable learners to apply R for accurate time-series modeling and forecasting.
- Build confidence in handling chronological datasets.
- Strengthen practical data science and analytical skills.
- Provide industry-relevant project exposure.
Objectives
- Understand the fundamentals of time-series data and its components.
- Develop forecasting models using R libraries and tools.
- Interpret analytical outputs to support decision-making.
- Apply statistical and machine learning concepts in predictive scenarios.
- Complete practical assignments and projects for real-world experience.
Highlights
- 19 structured courses within one comprehensive program.
- Hands-on projects for applied learning.
- One-year unlimited access.
- Verifiable certificates with unique shareable links.
- Self-paced video modules suitable for flexible schedules.
- Includes a balanced focus on core concepts as well as hands-on, real-world application.
Requirements
- Basic knowledge of R programming.
- Introductory understanding of statistics.
- Familiarity with data science concepts is beneficial.
- Awareness of machine learning fundamentals is helpful but not mandatory.
Target Audience
- Students aspiring to specialize in analytics or data science.
- Working professionals seeking forecasting and analytical skills.
- Developers from other programming backgrounds who wish to learn R.
- Trainers and educators who teach programming or analytics.
- Beginners interested in structured, guided entry into time-series analysis.
FAQ
Q1. Is prior R experience required?
A basic understanding of R is recommended, though essential concepts are also introduced within the course.
Q2. How long is the course accessible?
Participants receive one year of access from the date of enrollment.
Q3. Are certificates provided?
Yes, a completion certificate is awarded for each course included in the program.
Q4. Can certificates be verified online?
Yes, every certificate includes a unique verification link suitable for resumes and professional profiles.
Q5. Is the training live or recorded?
The program follows a self-paced, recorded video format.
Q6. Are practical projects included?
Yes, learners complete multiple projects to reinforce real-world application.
Career Benefits
- Enhances employability in data analytics and forecasting roles.
- Strengthens technical credibility through verifiable certifications.
- Builds practical R programming and predictive modeling expertise.
- Supports career transitions into data science and business intelligence.
- Improves decision-making and analytical problem-solving skills valued across industries.
