What you'll get
- 47+ Hours
- 18 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- This course provides a comprehensive overview of forecasting models with Excel, R, and Python.
- Participants have one year of access to all learning materials and can study at their own pace.
- The program is open to anyone interested in developing data forecasting skills.
- A basic understanding of Excel and introductory programming is recommended for an optimal learning experience.
- Learners earn a completion certificate for all 18 courses and hands-on projects included in the program.
- All certificates are verifiable and include a unique link that can be added to resumes or LinkedIn profiles to highlight new expertise.
- Training is delivered through self-paced video lessons, enabling learners to progress independently.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Statistical Tools in Excel | 1h 11m | ✔ | View Curriculum |
| Machine Learning - Statistics Essentials | 8h 23m | ✔ | View Curriculum |
| Statistics for Data Science using Python | 3h 23m | ✔ | View Curriculum |
| Statistics Essentials for Analytics - Beginners | 2h 5m | ✔ | View Curriculum |
| Machine Learning Project #3 - Predicting Prices using Regression | 2h 18m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Business Analytics - Forecasting using R | 4h 34m | ✔ | View Curriculum |
| Telecom Customer Churn Prediction | 1h 27m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using Logistic Regression | 1h 38m | ✔ | View Curriculum |
| House Price Prediction using Linear Regression | 3h 2m | ✔ | View Curriculum |
| Employee Attrition Prediction Using Random Forest Technique | 1h 6m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Card Purchase Prediction using R | 2h 28m | ✔ | View Curriculum |
| Project on Term Deposit Prediction using R | 3h 2m | ✔ | View Curriculum |
| Decision Tree Case Study Using R- Bank Loan Default Prediction | 1h 47m | ✔ | View Curriculum |
| Machine Learning Project-Churn Prediction | 1h 22m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Forecasting the Sales of the Store Using Time Series Analysis | 2h 13m | ✔ | View Curriculum |
| Predictive Modeling with Python | 8h 26m | ✔ | View Curriculum |
| Logistic Regression-Predicting the Survival of Passenger in Titanic | 2h 6m | ✔ | View Curriculum |
| Data Science with Python Project-Predict Diabetes on Diagnostic Measures | 1h 02m | ✔ | View Curriculum |
Description
The Forecasting Models course equips learners to understand and apply predictive techniques in modern data analysis. It is designed for those seeking to anticipate trends and make informed decisions, offering practical instruction in forecasting with Excel, R, and Python.
Participants receive one year of access to learn at their own pace. The course is accessible to those with basic Excel skills and introductory programming knowledge. Learners study key forecasting methods, complete hands-on projects, and gain confidence applying algorithms to real-world data.
Each participant receives verifiable completion certificates for all modules and projects, with unique verification links suitable for resumes, portfolios, and LinkedIn profiles. The self-paced video lessons provide an engaging learning experience for those pursuing data analytics, business intelligence, or data science.
Sample Certificate
Requirements
- No prior experience is required to participate in this forecasting model training.
- Advanced programming skills are not required.
- A basic understanding of R and Python is recommended to help you follow the concepts more effectively.
- In-depth expertise in machine learning or data science isn’t required; however, having a grasp of fundamental algorithms and basic concepts will enhance the overall learning experience.
Target Audience
- Individuals seeking to expand their knowledge of data and analytics.
- Data analysts working with large datasets who want to apply algorithms for trend prediction.
- Technical managers, software engineers, and IT professionals are aiming to strengthen their skills in time-series analysis and forecasting.
- Students and professionals interested in advancing or transitioning their careers into data science.
