Data Analysis Techniques, Modeling and Strategies

Introduction to Data Analysis
• Overview of data analysis
• Types of data and data structures
• Data quality and data cleaning

Exploratory Data Analysis (EDA)
• Descriptive statistics
• Data visualization techniques
• Identifying patterns and trends

Statistical Analysis Methods
• Regression analysis
• Hypothesis testing
• Confidence intervals
• Multivariate analysis

Data Modeling Techniques
• Conceptual data modeling
• Logical data modeling
• Physical data modeling
• Entity-Relationship Diagrams (ERD)
• Unified Modeling Language (UML) Class Diagrams

Predictive Modeling
• Machine learning algorithms
• Supervised and unsupervised learning
• Model evaluation and validation
• Overfitting and regularization

Data Analysis Strategies
• Defining business objectives
• Data requirements gathering
• Iterative model development
• Interpreting and communicating results

Case Studies and Best Practices
• Real-world examples of data analysis projects
• Lessons learned and best practices
• Ethical considerations in data analysis

Who Can Attend:
• Data analysts and business analysts
• Data scientists and machine learning engineers
• Business intelligence professionals
• Researchers and academics
• Anyone interested in data-driven decision making

1ST BATCH: Tuesday, February 10, 2026 — Friday, February 13, 2026.

2ND BATCH: Tuesday, June 2, 2026 — Friday, June 5, 2026.

3RD BATCH: Monday, September 28, 2026 — Wednesday, September 30, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.