| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Predictive Analytics Data Analysis Regression Data Trends Model Evaluation |
About the Data Science for Beginners Course
The Basics of Predictive Analytics course is a free, beginner-friendly self-paced program designed to introduce learners to how data is used to make predictions and support decision-making.
The course explains how patterns in historical data can be analyzed to predict future outcomes. Learners will explore key ideas such as data trends, simple prediction models, regression concepts, and basic evaluation methods. This course is ideal for beginners who want to understand how predictive analytics is used in business, research, healthcare, and technology.
Program Highlights
• Free beginner-level predictive analytics course
• Online self-paced learning format
• Simple explanation of prediction and data analysis concepts
• Covers trends, patterns, and basic prediction models
• Real-world use cases and examples
• Suitable for students and first-time learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Predictive Analytics
- What is Predictive Analytics?
- Importance of Data in Decision-Making
- Difference Between Descriptive, Diagnostic, and Predictive Analytics
- Real-World Applications
Module 2: Understanding Data for Prediction
- Types of Data and Variables
- Historical Data and Trends
- Features and Target Variables
- Data Quality and Preparation Basics
Module 3: Basic Predictive Models
- Introduction to Regression Concepts
- Understanding Relationships in Data
- Simple Prediction Techniques
- Examples of Predictive Use Cases
Module 4: Model Evaluation Basics
- How Predictions Are Evaluated
- Accuracy and Error Concepts
- Overfitting and Underfitting Basics
- Improving Model Performance
Module 5: Applications and Next Steps
- Predictive Analytics in Business, Healthcare, Finance, and Technology
- Using Predictions for Decision-Making
- Career and Learning Pathways in Data Science
- Mini Learning Activity / Concept-Based Practice
Tools, Techniques, or Platforms Covered
Predictive Analytics Data Analysis Regression Data Trends Model Evaluation
Real-World Applications
- Predicting sales, demand, and customer behavior
- Analyzing trends in business and financial data
- Supporting healthcare predictions and risk analysis
- Understanding patterns in research and academic datasets
- Preparing for advanced learning in data science and machine learning
Who Should Attend & Prerequisites
- This course is suitable for students, beginners, freshers, and professionals who want to understand how data is used to make predictions.
- It is also useful for learners from business, management, commerce, engineering, science, and data-related fields.
Certification
