Generative AI for Data Professionals DataWithCSDataWithCS

Name: Generative AI for Data Professionals
Availability: InStock

Generative AI is transforming how businesses analyze information, automate workflows, and build intelligent applications. This practical, project-based program helps learners move beyond basic AI tools and understand how to build real-world solutions using Large Language Models (LLMs). Starting with prompt engineering and LLM fundamentals, learners progress to Python-based AI applications, structured data interaction, embeddings, vector databases, Retrieval-Augmented Generation (RAG), and AI-powered assistants. By the end of the program, learners will have hands-on experience building AI applications that solve practical business and data problems.

Highlights

Practical prompt engineering techniques.
Understanding LLMs and Generative AI architecture.
Building AI applications with Python and APIs.
Using LLMs to work with business data and SQL.
Embeddings, vector databases, and semantic search.
Building Retrieval-Augmented Generation (RAG) applications.
Developing AI assistants for real-world use cases.
Hands-on projects and a final capstone application.

Learning Outcomes

Generative AI Fundamentals
LLM Fundamentals and AI Tools
Prompt Engineering
Python for Generative AI
Building LLM-Powered Applications
Generative AI for Data Analytics
AI for Data Engineering and Business Workflows
Embeddings and Semantic Search
Retrieval-Augmented Generation (RAG)
LangChain and LlamaIndex
AI Chatbots and Intelligent Assistants
AI Agents and Workflow Automation
Application Development and Deployment
Capstone Project and Assessment

Introduction to Artificial Intelligence
applications and evolution of AI

Traditional AI vs. ML vs. Deep Learning vs. Generative AI

What are LLMs? Capabilities, use cases, and limitations

Tokens, tokenization, context windows, and inference

Overview of LLM providers, AI tools, use cases, and quiz

Overview of hosted and open-weight language models

Temperature, output limits, context length, and model settings

Comparing quality, speed, cost, and model capabilities

AI tools for data analytics, coding, research, and productivity

Hallucinations, bias, privacy, security, and responsible usage

Prompt structure, instructions, context, and expected output

Zero-shot, one-shot, and few-shot prompting

Role-based prompting, constraints, and task-specific instructions

Task decomposition, examples, and multi-step workflows

Prompt templates, testing, evaluation, and prompt improvement

Python installation, virtual environments, IDEs, and notebooks

Variables, data types, functions, loops, and conditionals

Lists, dictionaries, JSON, and structured data handling

HTTP requests, responses, authentication, and status codes

Connecting Python to an LLM API and generating responses

Sending prompts, processing responses, and managing parameters

System instructions, conversation history, and multi-turn chat

JSON responses, schema validation, and structured information extraction

Tool definitions, function calling, and connecting external functions

Error handling, retries, rate limits, API security, and cost management

Dataset exploration, data profiling, and exploratory analysis

Missing values, duplicates, data types, and Python transformations

Converting natural-language questions into SQL queries

Python code generation, debugging, statistical analysis, and validation

Generating summaries, identifying trends, and validating business insights

Generative AI applications in ETL and ELT pipelines

Generating SQL transformations and debugging pipeline logic

Generating validation rules, test cases, and data quality checks

Generating code documentation, data dictionaries, and pipeline descriptions

Designing AI-assisted data workflows and validating outputs

Embeddings, vector representations, and semantic meaning

Creating text embeddings and understanding embedding models

Cosine similarity, vector distance, and semantic matching

Vector storage, indexing, metadata, and ChromaDB or FAISS

Building a semantic search application using sample documents

RAG architecture, components, and business use cases

Loading PDF, text, and other supported document formats

Text splitting, chunk size, overlap, metadata, and indexing

Retrieval, context construction, and LLM response generation

Retrieval quality, source citations, hallucination checks, and improvements

LangChain and LlamaIndex architecture and use cases

Reusable prompts, output parsers, and composable workflows

Document loaders, text splitters, retrievers, and vector stores

Implementing a document question-answering application

Retrieval configuration, debugging, testing, and performance tuning

Chatbot components, conversation flow, and application design

Managing chat history, context, and conversation state

Connecting LLMs with APIs, databases, and Python functions

Building an assistant that answers questions from business documents

Testing responses, access control, prompt injection, and guardrails

AI agents, tools, goals, and agent-based workflows

Function calling, tool selection, and executing approved actions

Planning tasks, sequencing operations, and handling intermediate outputs

Integrating AI with data processing and business tasks

Reliability, human approval, failure handling, and execution limits

Introduction to Streamlit and Generative AI application interfaces

Integrating Python, LLM APIs, RAG, and user inputs

API key management, input validation, data privacy, and access control

Functional testing, logging, latency, token usage, and cost monitoring

Preparing configuration, dependencies, environment variables, and deployment

Define the business problem, requirements, architecture, and dataset

Build the document ingestion, embedding, and retrieval pipeline

Integrate the LLM, RAG pipeline, and application interface

Evaluate responses, fix errors, document limitations, and prepare the application

Demonstration, technical presentation, project evaluation, and course review

Aman Raj

Senior Data Science and Gen AI Trainer

Data Science
Generative AI
Machine Learning
Deep Learning
Python
SQL
Natural Language Processing (NLP)
Large Language Models (LLMs)

5+ years experience

Aman Raj is a Senior Data Science & Generative AI Trainer with 5+ years of experience in the AI domain. An IIIT Bangalore alumnus and former Navy Tech Mentor, he brings a strong blend of technical expertise and practical industry-oriented learning. His areas of expertise include Data Science, Machine Learning, Generative AI, Large Language Models, Python, and AI-driven applications. He focuses on helping learners build practical skills and develop a strong foundation for careers in AI and Data Science.

Generative AI is a type of artificial intelligence that can generate text, code, images, and other content. It powers applications such as AI chatbots, coding assistants, and intelligent business tools.

This course is suitable for students, working professionals, Data Analysts, Data Engineers, Data Scientists, and Python developers who want to develop practical Generative AI skills.

No prior AI or Machine Learning experience is required. Basic Python programming knowledge is recommended to get the most out of the hands-on exercises.

You will learn prompt engineering, Large Language Models (LLMs), Python integration, AI APIs, embeddings, vector databases, Retrieval-Augmented Generation (RAG), AI chatbots, and introductory AI agents.

Yes. You will work on practical projects such as an AI SQL Assistant, a document question-answering chatbot, and an AI-powered Business Insights Assistant.

The course may cover Python, LLM APIs, LangChain, LlamaIndex, ChromaDB, FAISS, Streamlit, SQL, and other tools used to develop Generative AI applications.

RAG is a technique that enables an AI application to retrieve relevant information from external documents or knowledge sources before generating a response. It helps create more context-aware applications grounded in provided information.

The course can help you develop practical skills for building AI-powered applications and strengthen your profile for opportunities involving Generative AI, data analytics, Python development, and LLM application development. Career outcomes depend on your existing skills and experience.

Students who meet DataWithCS's course-completion requirements can receive a certificate of completion. Any applicable attendance, assessment, and project requirements will be communicated during enrollment.

Visit the DataWithCS website to explore the course and contact the team for information about upcoming batches, fees, schedules, and enrollment. You can also request a demo session to understand the learning approach before joining.

Ready to start Generative AI for Data Professionals ?

Talk to a counselor and find out which batch fits your schedule and goals.

Enroll Now Back to Courses

Highlights

Practical prompt engineering techniques.
Understanding LLMs and Generative AI architecture.
Building AI applications with Python and APIs.
Using LLMs to work with business data and SQL.
Embeddings, vector databases, and semantic search.
Building Retrieval-Augmented Generation (RAG) applications.
Developing AI assistants for real-world use cases.
Hands-on projects and a final capstone application.

Learning Outcomes

Generative AI Fundamentals
LLM Fundamentals and AI Tools
Prompt Engineering
Python for Generative AI
Building LLM-Powered Applications
Generative AI for Data Analytics
AI for Data Engineering and Business Workflows
Embeddings and Semantic Search
Retrieval-Augmented Generation (RAG)
LangChain and LlamaIndex
AI Chatbots and Intelligent Assistants
AI Agents and Workflow Automation
Application Development and Deployment
Capstone Project and Assessment

Introduction to Artificial Intelligence
applications and evolution of AI

Traditional AI vs. ML vs. Deep Learning vs. Generative AI

What are LLMs? Capabilities, use cases, and limitations

Tokens, tokenization, context windows, and inference

Overview of LLM providers, AI tools, use cases, and quiz

Overview of hosted and open-weight language models

Temperature, output limits, context length, and model settings

Comparing quality, speed, cost, and model capabilities

AI tools for data analytics, coding, research, and productivity

Hallucinations, bias, privacy, security, and responsible usage

Prompt structure, instructions, context, and expected output

Zero-shot, one-shot, and few-shot prompting

Role-based prompting, constraints, and task-specific instructions

Task decomposition, examples, and multi-step workflows

Prompt templates, testing, evaluation, and prompt improvement

Python installation, virtual environments, IDEs, and notebooks

Variables, data types, functions, loops, and conditionals

Lists, dictionaries, JSON, and structured data handling

HTTP requests, responses, authentication, and status codes

Connecting Python to an LLM API and generating responses

Sending prompts, processing responses, and managing parameters

System instructions, conversation history, and multi-turn chat

JSON responses, schema validation, and structured information extraction

Tool definitions, function calling, and connecting external functions

Error handling, retries, rate limits, API security, and cost management

Dataset exploration, data profiling, and exploratory analysis

Missing values, duplicates, data types, and Python transformations

Converting natural-language questions into SQL queries

Python code generation, debugging, statistical analysis, and validation

Generating summaries, identifying trends, and validating business insights

Generative AI applications in ETL and ELT pipelines

Generating SQL transformations and debugging pipeline logic

Generating validation rules, test cases, and data quality checks

Generating code documentation, data dictionaries, and pipeline descriptions

Designing AI-assisted data workflows and validating outputs

Embeddings, vector representations, and semantic meaning

Creating text embeddings and understanding embedding models

Cosine similarity, vector distance, and semantic matching

Vector storage, indexing, metadata, and ChromaDB or FAISS

Building a semantic search application using sample documents

RAG architecture, components, and business use cases

Loading PDF, text, and other supported document formats

Text splitting, chunk size, overlap, metadata, and indexing

Retrieval, context construction, and LLM response generation

Retrieval quality, source citations, hallucination checks, and improvements

LangChain and LlamaIndex architecture and use cases

Reusable prompts, output parsers, and composable workflows

Document loaders, text splitters, retrievers, and vector stores

Implementing a document question-answering application

Retrieval configuration, debugging, testing, and performance tuning

Chatbot components, conversation flow, and application design

Managing chat history, context, and conversation state

Connecting LLMs with APIs, databases, and Python functions

Building an assistant that answers questions from business documents

Testing responses, access control, prompt injection, and guardrails

AI agents, tools, goals, and agent-based workflows

Function calling, tool selection, and executing approved actions

Planning tasks, sequencing operations, and handling intermediate outputs

Integrating AI with data processing and business tasks

Reliability, human approval, failure handling, and execution limits

Introduction to Streamlit and Generative AI application interfaces

Integrating Python, LLM APIs, RAG, and user inputs

API key management, input validation, data privacy, and access control

Functional testing, logging, latency, token usage, and cost monitoring

Preparing configuration, dependencies, environment variables, and deployment

Define the business problem, requirements, architecture, and dataset

Build the document ingestion, embedding, and retrieval pipeline

Integrate the LLM, RAG pipeline, and application interface

Evaluate responses, fix errors, document limitations, and prepare the application

Demonstration, technical presentation, project evaluation, and course review

Aman Raj

Senior Data Science and Gen AI Trainer

Data Science
Generative AI
Machine Learning
Deep Learning
Python
SQL
Natural Language Processing (NLP)
Large Language Models (LLMs)

5+ years experience

Generative AI for Data Professionals

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Meet your instructor

Aman Raj

Frequently Asked Questions

Ready to start Generative AI for Data Professionals ?

Generative AI for Data Professionals

Course Overview

Highlights

Learning Outcomes

Course Curriculum

Meet your instructor

Aman Raj

Frequently Asked Questions

Ready to start Generative AI for Data Professionals ?