AI & Advanced Python/SQL for Data Professionals

Artificial Intelligence, Advanced Python & SQL for Data Professionals

This program takes working professionals from competent data users to confident practitioners who can write production-grade Python, reason about complex SQL at scale, and apply modern machine learning and LLM techniques to real business problems. The emphasis is balanced and integrated: rather than teaching three separate subjects, each module connects Python, SQL, and AI as they appear together in real workflows.

The course is hands-on throughout. Every module ends with a lab tied to a realistic scenario, and the program culminates in a portfolio-ready capstone.

Who It’s For:

• Data analysts, business analysts, and BI professionals
• Engineers and scientists who use data but want stronger software and AI skills
• Anyone comfortable querying data who wants to move beyond dashboards

Course Content:

Module 1: Foundations & Modern Python Workflow

• Professional environment: virtual environments, dependency management, Git for data work
• Notebooks vs. scripts vs. modules when to use each
• Code readability, type hints, and linting as a habit
• Lab: Set up a reproducible project repo and refactor a messy script into a clean module

Module 2: Advanced Python Language Features

• Comprehensions, generators, and iterators for memory-efficient data work
• Decorators, context managers, and *args/**kwargs patterns
• Data classes and lightweight OOP for modeling data
• Error handling and defensive coding
• Lab: Build a small reusable data-utility library with tests

Module 3: Python for Data at Scale

• Pandas mastery: indexing, groupby, merge, reshaping, vectorization
• Numpy fundamentals and why vectorization matters
• Performance: when pandas breaks down, intro to polars/DuckDB
• Lab: Clean and analyze a messy multi-file dataset; profile and speed up a slow transformation

Module 4: Advanced SQL I Querying with Confidence

• Join types deep dive, set operations, subqueries
• Common Table Expressions (CTEs) and readable query structure
• Aggregation patterns and conditional logic
• Lab: Answer escalating business questions against a normalized sample database

Module 5: Advanced SQL II Windows & Performance

• Window functions: ranking, running totals, lag/lead, partitioning
• Indexes, query plans (EXPLAIN), and reasoning about performance
• Avoiding common anti-patterns
• Lab: Rewrite slow queries for performance and solve problems that require window functions

Module 6: Bridging Python and SQL

• SQLAlchemy Core and connection management
• Parameterized queries, safe interpolation, and avoiding injection
• Reading/writing between databases and Data Frames cleanly
• Building a simple, repeatable ETL pipeline
• Lab: Build a pipeline that pulls from a database, transforms in Python, and writes results back

Module 7: Data Wrangling & Feature Engineering

• Handling missing data, outliers, categorical encoding, scaling
• Dates, text, and joins for feature creation
• Reproducible preprocessing and leakage avoidance
• Lab: Engineer a feature set from raw data in preparation for modeling

Module 8: Machine Learning Foundations

• The ML workflow: train/validation/test, fitting, prediction
• Core algorithms with scikit-learn: regression, trees, ensembles
• Pipelines and avoiding common mistakes
• Lab: Build and tune a supervised model on the Module 7 features

Module 9: Evaluation, Interpretation & Unsupervised Learning

• Metrics that matter (and how they mislead): precision/recall, ROC/AUC, regression metrics
• Cross-validation, overfitting, and model interpretability basics
• Clustering and dimensionality reduction
• Lab: Evaluate competing models rigorously and present which to ship and why

Module 10: Modern AI LLMs, Embeddings & RAG

• How LLMs work at a practical level; tokens, context, limitations
• Calling LLM APIs from Python; structured outputs
• Embeddings and semantic search; building a small vector store
• Retrieval-augmented generation (RAG) end to end
• Lab: Build a question-answering tool over a document set using embeddings and an LLM

Module 11: AI in Practice Prompting, Integration & Responsibility

• Effective prompt design and evaluation
• Integrating AI into existing workflows natural-language-to-SQL, automated summaries
• Cost, latency, reliability, and when not to use an LLM
• Ethics, privacy, bias, and data governance for working professionals
• Lab: Add an AI feature to an earlier pipeline and critically assess its reliability

Module 12: Capstone & Presentation

• Dedicated build time with instructor support
• Code review and refinement
• Presentations and peer feedback
• Deliverable: A complete, integrated project see below

Tools & Stack:

Capstone Project:

Participants choose a problem their own work data, anonymized, is encouraged and deliver aproject that integrates all three pillars. Projects are submitted via GitHub and become portfolio pieces.

• SQL: Source and shape data from a real database with non-trivial queries.
• Python: Build a clean, documented, reproducible pipeline.
• AI: Apply a machine learning model and/or an LLM-based component.
• Communication: A short written summary and a live presentation explaining the approach, results, and limitations.

Learning Outcomes:

• Write clean, idiomatic, performant Python using advanced language features and good engineering practice.
• Manipulate and analyze data at scale with pandas, numpy, and the modern Python data stack.
• Author complex SQL window functions, CTEs, set operations and reason about query performance and optimization.
• Build reliable Python ↔ SQL data pipelines and small ETL workflows.
• Train, evaluate, and interpret classical machine learning models.
• Apply modern AI LLMs, embeddings, retrieval-augmented generation to practical tasks using APIs.
• Ship a complete, documented analytical or AI project end to end.

Prerequisites:

• Basic Python variables, loops, functions; can read a simple script
• Basic SQL SELECT, WHERE, simple JOIN
• Comfort working in a code editor or notebook

International Student Fee: 1300 USD

Flexible Class Options

• Corporate Group Training | Fast-Track
• Weekend Classes For Professionals SAT | SUN
• Online Classes-Live Virtual Class(L.V.C) Online Training