Big Data Programming and Hadoop Analysis

Course SynopsisBig Data Programming and Hadoop Analytics introduces learners to the core concepts of Big Data, Hadoop ecosystem, and distributed data processing. The course focuses on hands-on experience with HDFS, YARN, MapReduce, Hive, Pig, and Apache Spark. Students will learn to build scalable data pipelines, manage Hadoop clusters, perform large-scale data analytics, and develop end-to-end Big Data solutions using real-world datasets in a hybrid learning environment.Required TextbooksTom White, Hadoop: The Definitive Guide, O’Reilly Media.Holden Karau et al., Learning Spark: Lightning-Fast Big Data Analytics, O’Reilly Media.Recommended Resources:Hadoop and Spark official documentation, GitHub lab repositories, cloud platform free-tier resources, and selected research papers.Completion CriteriaAfter fulfilling all of the following criteria, the student will be deemed to have finished the Module:Has attended 90% of all classes heldHas received an average grade of 80% on all assignmentsHas received an average of 60% in assessmentsThe tutor believes the student has grasped all core concepts and is ready to proceed to the next modulePrerequisitesBasic understanding of the Linux command lineFundamental programming knowledge (Python or Java preferred)Familiarity with SQL conceptsBasic understanding of data structures

Agentic AI with Python

HOW TO LEARN WITH AI

Course Synopsis - How to Learn with AI is a 40-day (60-hour) practical, project-based course designed for beginners, especially SEE-appeared students. It helps students use AI tools for studying, note-taking, content creation, and problem-solving through hands-on learning and real tasks. Evaluation is based on attendance, assignments, projects, and final performance. Required Tools / ResourcesAI Tools: ChatGPT, Google Gemini, ImageFX, Canva, CapCutPersonal laptop with internet accessOptional: Google AI Studio, ElevenLabs(No prior experience required) Completion Criteria90% attendance required80% average in assignments60% minimum in final project/evaluationActive participation in practical tasksInstructor approval for course completion PrerequisitesBasic computer and internet knowledgeInterest in learning AI toolsWillingness to practice 5–10 hours weeklyNo prior AI or design knowledge required Note: Best suited for students interested in practical learning and hands-on AI use.