Data Analytics Training in Perungudi

• Advanced SQL (Window functions, CTEs, complex joins)
• Python fundamentals for data (Lists, dictionaries, functions)
• Data manipulation with Pandas and Polars
• Connecting Python to relational databases
• Writing clean, modular, and reusable data scripts

Beginner Build: An automated Python script that extracts, cleans, and exports SQL data to CSV.

• Handling missing values, outliers, and inconsistent data types
• Feature engineering and data transformation techniques
• Univariate, bivariate, and multivariate analysis
• Statistical distributions and hypothesis testing basics
• Automating EDA reports using Python libraries

Beginner to Intermediate Perform end-to-end EDA on a messy retail dataset, cleaning data and identifying key trends.

• Principles of effective data visualization and design
• Deep dive into Power BI / Tableau (Data modeling, DAX/LOD)
• Building interactive, drill-down executive dashboards
• Implementing row-level security and data access controls
• Publishing, scheduling, and sharing reports securely

Intermediate Design and deploy an interactive executive sales dashboard with automated refresh and drill-downs.

• Linear and logistic regression for business forecasting
• Time-series analysis and seasonality decomposition
• Decision trees, random forests, and gradient boosting
• Model evaluation metrics (RMSE, MAE, F1-Score, ROC-AUC)
• Handling imbalanced datasets and feature selection

Intermediate to Advanced Build a time-series forecasting model and a classification model to identify high-value customers.

• Introduction to LLM APIs (OpenAI, Anthropic, local models)
• Prompt engineering specifically for data analysis and SQL generation
• Building secure Text-to-SQL pipelines with schema validation
• Automating data storytelling and insight generation with AI
• Evaluating and preventing LLM hallucinations in data queries

Advanced Develop a Natural Language Query Assistant that generates accurate SQL charts from plain English.

• Understanding embeddings and semantic search
• Setting up and querying Vector Databases (Pinecone, Qdrant)
• Building modern ETL/ELT pipelines with dbt and Airflow
• Data versioning and feature store basics
• Hybrid search (combining vector similarity with keyword search)

Advanced Build an automated ETL pipeline that ingests documents, generates embeddings, and stores them.

• The anatomy of an AI Agent (Perception, Reasoning, Action)
• Deep dive into Model Context Protocol (MCP) architecture
• Building custom MCP servers to expose database schemas to AI
• Tool selection, execution, and error handling loops
• Memory management for long-running data analysis tasks

Advanced Create an MCP-powered agent that autonomously queries SQL databases and updates CRM records.

• Advanced chunking strategies for diverse data types
• Introduction to GraphRAG and knowledge graph integration
• Agentic RAG: Autonomous query routing and self-correction
• Contextual retrieval and handling multi-modal documents
• Evaluating RAG pipelines for faithfulness and relevance

Advanced Build a GraphRAG assistant that analyzes complex financial reports and answers with precise citations.

• Frameworks for multi-agent systems (LangGraph, CrewAI)
• Designing agent personas (Data Analyst, Researcher, Writer)
• State management and shared memory across agents
• Human-in-the-loop (HITL) workflows and approval gates
• Handling agent deadlocks and infinite execution loops

Advanced Orchestrate a multi-agent team to query databases, research markets, and generate BI reports.

• Data privacy regulations (GDPR, DPDP Act) and compliance
• Implementing data masking and PII detection in AI pipelines
• AI Red-teaming and preventing prompt injection in data apps
• Identifying and mitigating algorithmic bias in predictive models
• Creating audit trails and ensuring explainability (XAI)

Intermediate to Advanced Conduct a data privacy and bias audit on a predictive model, implementing automated guardrails.

• Containerizing data apps with Docker and FastAPI
• Observability and tracing for AI data pipelines
• Caching strategies to reduce API and compute costs
• Automated CI/CD testing for data models and prompts
• Monitoring data drift and model performance in production

Advanced Deploy a concurrent AI analytics API using FastAPI, Docker, and real-time observability dashboards.

• System design for scalable, AI-driven analytics architectures
• Writing technical documentation and data dictionaries
• Resume building and portfolio optimization for AI/Data roles
• Mock technical interviews (SQL, Python, System design)
• Navigating the job market and freelance analytics opportunities

Expert Deploy a production-ready AI analytics SaaS platform from scratch with CI/CD, & monitoring.