Data Science and Big Data Analytics

Introduction to Data Science and Big Data

• Overview of data science concepts and lifecycle
• Big data characteristics: volume, velocity, variety, veracity, and value
• Role of data science in modern organizations
• Data types, sources, and collection methods

Data Processing and Management

• Data cleaning, integration, and transformation
• Database management systems and data warehouses
• Introduction to cloud-based data storage and processing
• Ensuring data quality and integrity

Data Analytics Techniques

• Descriptive, diagnostic, predictive, and prescriptive analytics
• Statistical analysis and hypothesis testing
• Machine learning and AI applications in analytics
• Text, image, and sensor data analysis

Big Data Tools and Technologies

• Hadoop, Spark, and other big data frameworks
• Programming with Python and R for data analysis
• Data visualization tools (Tableau, Power BI, etc.)
• Real-time data processing and streaming

Data Governance and Security

• Data privacy, security, and compliance considerations
• Policies for ethical data use and governance
• Access control and data protection strategies
• Risk management in data analytics projects

1ST BATCH: Tuesday, April 14, 2026 — Friday, April 17, 2026.

2ND BATCH: Tuesday, August 4, 2026 — Friday, August 7, 2026.

3RD BATCH: Tuesday, December 1, 2026 — Friday, December 4, 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.