Byju Chalikandy

Years of Experience in Data Science Classes

19

Years of Experience in Computer Classes

5

Teaching Experience in detail in Computer Classes

I have over 5 years of experience teaching and mentoring students and professionals in diverse areas of Computer Science and Technology. My teaching spans both foundational computer skills and advanced specializations aligned with real-world industry applications. I specialize in AI & Machine Learning, Generative AI & LLMs, and Data Science & Analytics, where I guide learners through concepts ranging from Python programming and statistics to deep learning frameworks, prompt engineering, and RAG systems. I also provide practical, project-oriented training in Big Data & Data Engineering (Hadoop, Spark, Kafka, Snowflake, Databricks, etc.), enabling learners to build scalable data pipelines and analytics platforms. In addition to technical depth, I emphasize software design, architecture, and development practices, covering .NET, Java, Python, and modern web frameworks. I coach learners on cloud computing (Azure, AWS, GCP) and DevOps & Infrastructure Automation (CI/CD pipelines, Kubernetes, Terraform, GitHub Actions), helping them understand how modern software is developed and deployed at scale. Beyond technology, I bring strong expertise in Agile & Scaled Agile coaching, Project/Program/Portfolio management, and product strategy. I mentor aspiring professionals to understand how technology projects are delivered in enterprises, including practices like PI Planning, OKRs, and SAFe frameworks. I also offer specialized guidance in Capital Markets & Business Analysis, bridging finance and technology, which is highly valuable for learners aspiring to enter BFSI/FinTech domains. My teaching style is practical, interactive, and outcome-oriented. I focus on simplifying complex concepts with examples, case studies, and hands-on assignments so that learners not only understand theory but can also apply it confidently in their careers.

Years of Experience in Generative AI Classes

4

Teaching Experience in detail in Generative AI Classes

I have several years of experience teaching and mentoring students and professionals in Generative AI and Large Language Models (LLMs). My focus is on making complex concepts simple, practical, and directly applicable to real-world use cases. I cover topics such as prompt engineering, LLM fine-tuning, retrieval-augmented generation (RAG), embeddings and vector databases (FAISS, Pinecone, Chroma), and building GenAI applications using LangChain, LlamaIndex, and Hugging Face Transformers. Learners also gain hands-on exposure to deployment with FastAPI, Streamlit, and cloud platforms. Along with technical depth, I emphasize responsible AI practices—including bias detection, explainability, and compliance—which are essential for professional adoption. My sessions include real-world case studies from BFSI, EdTech, and Capital Markets, enabling learners to connect theory with industry applications. My teaching style is interactive, project-based, and outcome-oriented. By the end of the course, students not only understand the foundations of Generative AI but are also equipped to build prototypes, integrate models into applications, and apply these skills in academic, research, or professional settings.

Years of Experience in Big Data Training

2

Teaching Experience in detail in Big Data Training

Teaching Experience (Big Data & Data Engineering) Experienced in teaching Big Data and Data Engineering with a focus on practical, industry-ready skills. Cover the end-to-end data lifecycle: ingestion, storage, processing, streaming, and analytics. Train learners on Hadoop & Apache Spark for large-scale distributed data processing. Teach Apache Kafka & Flink for real-time streaming and event-driven pipelines. Provide hands-on exposure to modern data platforms: Snowflake, Databricks, and Azure Synapse. Guide students through data lakehouse architectures, ETL/ELT pipelines, and CDC workflows. Emphasize project-based learning, e.g., fraud detection pipelines, IoT streaming analytics, or data warehouse modernization. Teach performance optimization, scalability, and data governance best practices. Include real-world use cases from BFSI, Retail, and EdTech to connect technology with business impact. Teaching style is interactive, practical, and outcome-focused, ensuring learners gain confidence to build Big Data solutions.

Years of Experience in Agile Training

8

Teaching Experience in detail in Agile Training

Teach Agile fundamentals including Scrum, Kanban, and XP with practical examples. Coach on scaling frameworks such as SAFe, LeSS, and Disciplined Agile for enterprise delivery. Guide learners in Agile Program & Portfolio Management, Lean Portfolio concepts, and OKR alignment. Conduct hands-on workshops and simulations (PI Planning, Sprint Planning, Retrospectives). Train on Agile metrics and dashboards (velocity, burn-down, flow efficiency, value delivery). Share real-world case studies from EdTech, BFSI, and SaaS to show Agile in action. Focus on interactive, outcome-driven teaching tailored for both beginners and leaders. Enable learners to apply Agile in teams, drive continuous improvement, and deliver customer value.

Years of Experience in .Net Training

5

Teaching Experience in detail in .Net Training

Teach C# programming fundamentals (OOP, LINQ, async programming, collections). Train on ASP.NET Core & MVC for building scalable enterprise web applications. Cover Web API development, REST principles, and secure integrations. Guide learners on Entity Framework Core & SQL Server for database design and ORM. Include front-end integration with Blazor, Angular, and React for full-stack development. Emphasize design patterns, SOLID principles, and clean coding practices. Provide training in microservices with .NET Core, Azure cloud deployment, and DevOps pipelines. Focus on hands-on projects (e-commerce apps, LMS modules, secured APIs) with real-world case studies.

Years of Experience in DevOps Training

5

Teaching Experience in detail in DevOps Training

Teach DevOps fundamentals: collaboration, CI/CD, automation, and shift-left practices. Train on Git & GitHub/GitLab for version control and collaborative workflows. Cover CI/CD pipelines using GitHub Actions, Jenkins, and Azure DevOps. Provide hands-on guidance in Docker & Kubernetes (AKS, EKS, GKE) for containerized deployments. Introduce Infrastructure as Code (IaC) with Terraform and Ansible. Emphasize monitoring & observability with Prometheus, Grafana, and ELK/EFK stacks. Teach DevSecOps practices: integrating security tools like Vault, SonarQube, and Snyk into pipelines. Focus on real-world projects: automated deployments, GitOps workflows, and cloud-native CI/CD pipelines.