Data Science and Big Data Analytics

• Equip participants with practical knowledge and skills in data science and big data analytics
• Strengthen understanding of data collection, processing, and analysis techniques
• Enable learners to derive insights, make data-driven decisions, and optimize business processes
• Promote best practices in data governance, visualization, and predictive modeling

Course Content

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

DATE:
1ST BATCH:21st–24th Apr ,2026
2ND BATCH:11th–14th Aug , 2026
3RDBATCH:8th–11th Dec ,2026