Complete Course on Data Science and Big Data Analytics

• Data Science Fundamentals
• Introduction to data science, its significance, and applications across industries
• Programming Languages
• In-depth training on Python and R programming for data analysis and machine learning
• Statistics and Probability
• Fundamental concepts in statistics and probability for data analysis and insights derivation
• Data Analysis and Visualization
• Techniques for data manipulation, analysis, and visualization using tools like Pandas, NumPy, and Matplotlib
• Machine Learning
• Coverage of supervised and unsupervised learning, decision trees, clustering, regression models, and neural networks
• Big Data and Cloud Computing
• Understanding big data technologies, the Hadoop ecosystem, and cloud platforms for data processing and storage
• Natural Language Processing (NLP)
• Techniques and algorithms for processing and analyzing text data
• Data Mining
• Methods for extracting patterns and knowledge from large datasets
• Business Intelligence and Data Warehousing
• Concepts and tools for business intelligence, data warehousing design, and OLAP
• Data Engineering
• Introduction to data engineering concepts, ETL processes, data modeling, and warehousing
• Ethics and Data Privacy
• Understanding ethical considerations and privacy laws in data science

Who Can Attend:
• Individuals seeking to enhance their skills in data science and big data analytics
• Professionals interested in leveraging data for business intelligence and decision-making
• Students and graduates from STEM fields or related disciplines looking to specialize in data analytics and machine learning

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

2ND BATCH: Tuesday, June 30, 2026 — Friday, July 3, 2026.

3RD BATCH: Tuesday, October 27, 2026 — Friday, October 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.