| Attribute | Detail |
|---|---|
| Format | Online, self-paced course |
| Level | Basic / Beginner |
| Duration | 2–3 Weeks |
| Certification | e-Certification |
| Fee | ₹199 / $20 |
| Tools | Statistical Modeling Data Analysis Regression Probability Data Interpretation |
About the Basics of Statistical Modeling Course
The Basics of Statistical Modeling course is a free, beginner-friendly self-paced program designed to introduce learners to how statistical methods are used to understand data and build simple predictive models.
The course explains how relationships between variables are analyzed, how patterns are identified, and how models are used to make informed decisions. Learners will explore core concepts such as variables, distributions, regression basics, and model interpretation. This course is ideal for beginners who want to build a strong foundation in statistics for data science and analytics.
Program Highlights
• Free beginner-level statistical modeling course
• Online self-paced learning format
• Simple explanation of statistical concepts and modeling
• Covers variables, distributions, and regression basics
• Real-world examples from business and research
• Suitable for students and first-time learners
• e-Certification upon successful completion
Course Curriculum
Module 1: Introduction to Statistical Modeling
- What is Statistical Modeling?
- Role of Statistics in Data Science
- Types of Models and Applications
- Examples from Real-World Data
Module 2: Understanding Data and Variables
- Types of Variables: Numerical and Categorical
- Independent vs Dependent Variables
- Data Distribution Basics
- Importance of Data Quality
Module 3: Basic Statistical Concepts
- Mean, Median, and Variance
- Probability Basics
- Understanding Relationships in Data
- Introduction to Correlation
Module 4: Regression and Modeling Basics
- Introduction to Regression
- Understanding Simple Linear Relationships
- Interpreting Model Outputs
- Basic Prediction Concepts
Module 5: Applications and Next Steps
- Statistical Modeling in Business, Healthcare, and Research
- Using Models for Decision-Making
- Career Pathways in Data Science and Analytics
- Mini Learning Activity / Concept-Based Practice
Tools, Techniques, or Platforms Covered
Statistical Modeling Data Analysis Regression Probability Data Interpretation
Real-World Applications
- Analyzing relationships between variables
- Making predictions using simple models
- Supporting research and data-driven decisions
- Understanding trends in business and healthcare data
- 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 the basics of statistical modeling.
- It is also useful for learners from mathematics, statistics, business, engineering, research, and non-technical backgrounds.
Certification
