WHAT IF I MISS A CLASS?

Each of our live sessions are recorded. In case if you miss any, you can request us to share the link of that particular session.

HOW DO I JOIN THE LIVE SESSIONS?

Once you get registered, our back-end team will share you the details to join the session live over online portal which can be accessed through a browser.

WHAT IF I HAVE QUERIES AFTER COMPLETION OF THE CLASS?

You can contact our support team, or just drop an email to online@akswave.com with your queries.

HOW WILL I EXECUTE THE PRACTICAL?

For practical execution, our trainer/technical team will provide server access details to the student.

IS THE COURSE MATERIAL ACCESSIBLE AFTER COMPLETION OF THE COURSE?

The course material and recorded videos which are provided during the course period. You can download it anytime.

WILL YOU HELP US IN BUILDING OUR OWN LAB SETUP IN OUR LOCAL DESKTOP / LAPTOP?

Yes. We do provide the step-by-step document which you can follow and if required our technical team will assist you.

ANY OFFER/DISCOUNT THAT I CAN AVAIL?

Visit our website regularly to check discount offers time to time. However, we provide discount for single participant & special discount for 2 or more participants.

WHAT IS THE DIFFERENCE BETWEEN LIVE ONLINE AND PRE-RECORDED VIDEOS TRAINING?

Live-Online training is where you can have live session with the trainer and clarify queries parallelly. Pre-recorded sessions are the recorded videos that will be provided to you so that you can see, listen and learn anytime at your feasible place. For doubts in the videos you can mail the trainer regarding the same.

WILL I GET THE REFUND IF I CANCEL MY ENROLLMENT?

If request for cancelation is made within 2 days of enrolment for class, 100% refunded. If request made after 2 days, then Refund is made after deduction of administration fee.

Data Science Gen AI and Agentic AI Training Institute [Top-Rated]

COURSE CURRICULUM

49+ Course Topics • Hands-on Labs • Real-Time Practice

MODULE 1: FOUNDATIONS

01 Introduction to Data Analytics Topics

What is Data Analytics vs Data Science vs Business Intelligence
Roles and responsibilities: Business Analyst, Data Analyst, BI Developer
Analytics workflow: Data collection → Analysis → Visualization → Insights → Action
Real-world case studies from industry (finance, retail, e-commerce, healthcare)
Career paths and salary benchmarks in analytics

02 Python Environment Setup for Analytics Topics

Installing Python, Jupyter Notebook, VS Code
Package managers: pip, conda
Setting up virtual environments
Introduction to IDE best practices

03 Python Fundamentals for Analytics Topics

Language fundamentals: variables, data types, operators
Control flow: if-else, loops (for, while)
String manipulation and formatting
Lists, tuples, dictionaries for data handling
Functions: defining, parameters, return values

MODULE 2: DATA MANIPULATION & EXPLORATION

04 NumPy Essentials Topics

NumPy arrays vs Python lists: performance benefits
Array creation, indexing, slicing
Mathematical operations on arrays
Broadcasting concepts
Statistical functions (mean, median, std, percentile)
Practical: Working with multi-dimensional datasets

05 Pandas for Data Analysis (Core Module) Topics

Series and DataFrame fundamentals
Creating DataFrames from CSV, Excel, SQL databases
Indexing and selecting data
Viewing and inspecting data (head, tail, info, describe)

06 Data Cleaning and Transformation Topics

Handling missing values (dropna, fillna, interpolation)
Removing duplicates
Data type conversions
Handling outliers

07 Data Manipulation Topics

Filtering and sorting
Groupby operations and aggregations
Pivot tables and crosstabs
Merging, joining, concatenating DataFrames

08 Time Series Basics Topics

DateTime handling
Resampling and rolling windows
Trend analysis

09 Exploratory Data Analysis (EDA) Theory Topics

Understanding data distribution
Identifying patterns and relationships
Statistical thinking for analysts
Types of analysis: univariate, bivariate, multivariate
Hypothesis formation from data exploration

MODULE 3: DATA VISUALIZATION & STORYTELLING

10 Matplotlib Fundamentals Topics

Figure and axis objects
Line plots, bar charts, histograms, scatter plots
Customization: colors, markers, line styles
Legend, labels, titles, annotations
Grid and axis formatting
Multiple plots: subplots and figure layouts

11 Advanced Visualization with Seaborn Topics

Statistical visualization concepts
Categorical plots: bar, box, violin, strip plots
Distribution plots: histograms, KDE plots
Relationship plots: scatter, regression plots, heatmaps
Color palettes and styling
FacetGrid for multi-dimensional analysis

12 Interactive Dashboards with Plotly & Streamlit Topics

Plotly basics: interactive charts and hover information
Creating dashboards with Streamlit
Deploying Streamlit apps to cloud (Streamlit Cloud, Heroku)
Real-time data updates
Dashboard best practices

13 Data Storytelling Topics

Principles of data visualization
Choosing the right chart for the message
Narrative structure: context, conflict, resolution
Presenting insights to non-technical stakeholders
Creating compelling business reports

MODULE 4: STATISTICS FOR BUSINESS DECISIONS

14 Descriptive Statistics Topics

Central tendency: mean, median, mode
Spread: range, variance, standard deviation, IQR
Skewness and kurtosis
Outlier detection and handling (IQR method, Z-score)
Weighted statistics

15 . Probability Basics Topics

Fundamentals: sample space, events, probability rules
Conditional probability and Bayes' theorem
Independence and dependence
Real-world application scenarios

16 Inferential Statistics Topics

Sampling concepts and distributions
Confidence intervals: for mean and proportion
Hypothesis testing: null vs alternative hypotheses
T-tests, Z-tests, chi-square tests
P-values and significance levels
Type I and Type II errors

MODULE 5: SQL FOR DATA ANALYSTS

17 SQL Fundamentals Topics

SELECT, WHERE, ORDER BY, LIMIT
Filtering with AND, OR, NOT, IN, BETWEEN
DISTINCT and COUNT

18 Data Aggregation & Joins Topics

GROUP BY and aggregation functions (SUM, AVG, MAX, MIN, COUNT)
HAVING clause
INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN
Self-joins and multiple joins

MODULE 6: FOUNDATIONS & LLM BASICS

19 Introduction to Generative AI Topics

What is Generative AI vs Traditional ML
GenAI applications across industries (content, code, analytics, customer service)
The GenAI landscape: LLMs, Diffusion models, Multimodal models
Business use cases and ROI analysis
Ethical considerations and responsible A

20 Fundamentals of Large Language Models Topics

History and Evolution

From word embeddings (Word2Vec) to transformers
GPT-3 vs GPT-4 vs Claude vs Llama evolution
Understanding the transformer architecture (conceptual)

How LLMs Work

Tokens and tokenization
Attention mechanisms (high-level)
Training process: next token prediction
Context windows and token limits
Temperature, top-k, top-p sampling explained

21 Shared Foundations (Python, NumPy, Pandas, Statistics) Topics

Python fundamentals for LLM applications
NumPy for numerical operations
Pandas for handling structured data
Statistics essentials for model evaluation
Setting up ML development environment (Jupyter, VS Code, Colab)

MODULE 7: WORKING WITH LLMs APIs & MODELS

22 LLM APIs and Platforms Topics

Cloud-Based LLM APIs

OpenAI API (GPT-4, GPT-3.5): authentication, rate limiting, cost optimization
Google Cloud Vertex AI: Gemini models, deployment options
Anthropic Claude API: working with long context windows
Azure OpenAI: enterprise deployment

Open Source Models

Hugging Face ecosystem: model hub, model cards, licensing
Llama 2, Mistral, CodeLlama: deployment and usage
Running models locally: HuggingFace Transformers, Ollama
Quantization: reducing model size for edge deployment

23 Prompt Engineering Fundamentals Topics

Prompt Structure

System prompts vs user prompts
Role-based prompting
Few-shot vs zero-shot prompting
Chain-of-thought prompting for complex reasoning

Advanced Techniques

Prompt injection and security
Template-based prompts for scalability
Evaluating prompt quality
Iterative prompt optimization

24 LangChain Fundamentals Topics

Core Concepts

LLMs, Chains, Agents, Memory
Prompt templates and formatting
Output parsing and validation
Sequential chains and branching logic

Working with LangChain

Integrating with OpenAI, HuggingFace, local models
Building chatbots with conversation memory
Document loaders and text splitters
Chaining multiple LLM call

MODULE 8: RETRIEVAL-AUGMENTED GENERATION (RAG)

25 RAG Architecture & Concepts Topics

Why RAG?

Hallucination problem in LLMs
Keeping models current without retraining
Domain-specific knowledge integration
Cost optimization vs accuracy tradeoff

RAG Workflow

Data ingestion pipeline
Chunking and preprocessing strategies
Embedding generation and semantic search
Retrieval ranking and reranking
LLM generation with context

26 Vector Databases Topics

Introduction to Vector Databases

Why embeddings and vector similarity matter
Similarity metrics: cosine, L2, dot product
Vector database landscape: Pinecone, Weaviate, Qdrant, Milvus, Chroma

Using Vector Databases

Creating and managing vector indexes
Embedding models: OpenAI embeddings, open-source models
Storing and retrieving vectors
Metadata filtering and hybrid search
Scaling vector databases

27 Building Production RAG Systems Topics

End-to-End RAG Pipeline

Document ingestion: PDF, web, database sources
Text preprocessing: cleaning, chunking, overlap strategies
Embedding storage and indexing
Query expansion and multi-step retrieval
Context ranking and relevance filtering

LangChain + Vector DB Integration

Retrievers in LangChain
Prompt engineering for RAG
Answer generation with source attribution
Handling multi-document retrieval

Evaluation & Optimization

Retrieval quality metrics: precision, recall, MRR, NDCG
Generation quality: semantic similarity, BLEU, ROUGE, LLM-as-judge
Latency and cost optimization
A/B testing retrieval strategies

MODULE 9: CAPSTONE PROJECT

28 End-to-End GenAI Application Topics

Project Options:

1. RAG-based Q&A System: Build chatbot for company knowledge base

2. Fine-Tuned Customer Support Bot: Specialize model for domain-specific queries

3. Content Generation Platform: Multi-document synthesis with custom guidelines

4. Code Assistant: Fine-tuned model for specific programming context

MODULE 10: AGENTIC AI FOUNDATIONS

29 Introduction to Agentic AI Topics

Paradigm Shift: RAG to Agents

Limitations of static RAG systems
Why autonomous agents matter in 2026
Agent vs Chatbot vs Automation: fundamental differences
Business applications: autonomous research, customer support, operations, data analysis

The Agent Architecture

Perception (sensing inputs and context)
Planning (deciding what to do)
Action (executing tasks)
Reflection (learning and improvement)
Multi-agent collaboration patterns

30 Agentic AI Landscape Topics

Frameworks Overview

Crew AI: orchestration and specialization
Langraph: control flow and state management
AutoGen: multi-agent conversation framework
n8n: low-code agent workflows
Comparison and use case matrix

Agent Capabilities

Tool calling and function execution
Long-horizon reasoning and planning
Error recovery and retry logic
Learning from interactions

31 Foundational Concepts Topics

LLM Foundations (Advanced)

Transformer architecture deep dive
Context windows and memory constraints
Token budgeting for agent conversations
Function calling (OpenAI, Anthropic, open-source models)
Tool use best practices

Python for Agents

Async programming for concurrent agent tasks
Design patterns: factory, observer, state machine
Event-driven architecture
Testing and mocking agent behaviors

MODULE 11: BUILDING AUTONOMOUS AGENTS

32 Single Agent Development Topics

Agent Components

System prompt design for agent behavior
State management: memory, knowledge, goals
Tool definition and schema specification
Response parsing and validation

Tool Ecosystem

Creating custom tools for agent use
Tool documentation standards
Grounding agents in reality (APIs, databases, web search)
Tool chaining and composition
Safety constraints and permission systems

33 LangGraph Deep Dive Topics

Core Concepts

Nodes and edges: defining agent behavior graph
State management: persistent and ephemeral
Branching and conditional flows
Recursion and looping in graphs

Building Agents with LangGraph

Define tool schemas and LLM function calling
Implement agent loops with LangGraph
Handle hallucinations and invalid tool calls
Streaming and progressive output
Debugging agent behavior

Advanced Patterns

Sub-agents and hierarchical control
Interrupt and human-in-the-loop workflows
Cross-cutting concerns: logging, monitoring, tracing

34 Crew AI for Agent Orchestration Topics

Crew AI Architecture

Crew, Agents, Tasks, Tools concepts
Agent roles and specialization
Task dependencies and workflows
Context passing between agents

Building Crews

Defining specialized agents (analyst, executor, validator)
Creating sequential and parallel task flows
Agent memory: short-term and long-term
Crew callbacks for monitoring
Output formatting and validation

Multi-Agent Patterns

Manager pattern: centralized decision-making
Consensus pattern: voting and agreement
Debate pattern: adversarial collaboration
Pipeline pattern: sequential processing

35 Real-Time Tools Integration Topics

API Integration

REST APIs: rate limiting, authentication, error handling
Database connections: SQL queries, data updates
Web scraping: dynamic and static content
Third-party integrations: Slack, Gmail, Salesforce

External Data Sources

Real-time data feeds and market data
Internal data lakes and data warehouses
Streaming data: WebSockets, Server-Sent Events
Data freshness and cache invalidation

MODULE 12: MULTI-AGENT SYSTEMS & COLLABORATION

36 Multi-Agent Architecture Design Topics

Conversation Patterns

Sequential conversations: strict ordering
Parallel conversations: concurrent agent execution
Star topology: central coordinator
Mesh topology: direct agent-to-agent communication
Dynamic topologies: agents join/leave at runtime

37 Agent Planning and Coordination Topics

Planning Strategies

Reactive planning: immediate response
Deliberative planning: forward search
Hierarchical planning: decomposing complex tasks
Planning with constraints and resources

38 Multi-Agent in Production (n8n Workflows) Topics

n8n Introduction

Workflow automation fundamentals
Nodes and connections
Triggers and conditions
Data transformation nodes

Building Agent Workflows in n8n

Calling external LLM agents from n8n
Parallel and conditional execution
Error handling and retry strategies
Webhook integration for external events
Scheduling and recurring workflows

Scaling Workflows

Resource management and concurrency limits
Database storage of workflow state
Audit logging and workflow history
Workflow versioning and rollback

MODULE 13: REASONING, PLANNING & PROBLEM-SOLVING

39 Advanced Reasoning Patterns Topics

Chain-of-Thought and Tree-of-Thought

Structured reasoning: step-by-step decomposition
Branching reasoning: exploring multiple paths
Combining human reasoning with LLM capabilities

Retrieval-Augmented Agents (RAG + Agents)

Information retrieval in agent loops
Dynamic knowledge updates
Context management in long conversations
Knowledge graph reasoning with agents

40 Planning Algorithms Topics

Task Planning

Goal decomposition and sub-goals
Dependency graphs and critical path analysis
Resource constraints and scheduling
Plan validation and contingency handling

Search Strategies

Breadth-first and depth-first search
Best-first search and heuristics
Monte Carlo Tree Search for complex decisions
A* and informed search

41 Error Recovery and Resilience Topics

Failure Modes

Tool execution failures
Hallucinations and incorrect reasoning
Resource exhaustion and timeouts
Invalid state and contradiction detection

Recovery Strategies

Retry logic with exponential backoff
Alternative tool invocation
User escalation and human-in-the-loop
Graceful degradation and fallback modes

42 Evaluation Metrics for Agents Topics

Task Success Metrics

Goal achievement rate
Task completion time
Resource efficiency (API calls, tokens, cost)

Quality Metrics

Plan quality and optimality
Reasoning coherence and correctness
Safety and constraint satisfaction
User satisfaction and feedback

MODULE 14: DEPLOYMENT & PRODUCTION SYSTEMS

43 Production Agent Architecture Topics

System Design

44 Monitoring and Observability Topics

Logging and Tracing

Structured logging for agent interactions
Distributed tracing across agent calls
LLM cost tracking and optimization
Performance metrics collection

Monitoring Dashboards

Agent health and availability
Task success rates and latencies
Error rates and recovery performance
Cost analysis and budget controls

45 Cost Optimization Topics

Token and API Optimization

Caching strategies for repeated queries
Prompt compression techniques
Model selection: cost vs performance tradeoff
Batch processing for efficiency

Infrastructure Costs

Compute resource right-sizing
Spot instances and preemption handling
Database indexing for query efficiency

46 Security and Governance Topics

Agent Security

Tool access controls and sandboxing
Input validation and prompt injection prevention
Output filtering and PII detection
Audit trails and compliance logging

Governance

Agent behavior policies and constraints
Model governance and versioning
Data privacy and retention policies
Regulatory compliance (GDPR, SOX, etc.)

MODULE 15: CAPSTONE PROJECT

47 Complex Multi-Agent System Project Topics

Project Scope:

Build an autonomous system that solves a complex business problem involving multiple specialized agents

Project Options:

1. Autonomous Research Agent: Multi-source research synthesis, fact-checking, and report generation

2. Customer Service Crew: Ticket routing, problem-solving, escalation, and feedback integration

3. Data Analysis Team: Data exploration, statistical analysis, visualization, and insight generation

4. Sales Operations Agent: Lead qualification, data enrichment, meeting scheduling, follow-up

5. DevOps Automation Crew: Infrastructure monitoring, incident response, and auto-remediation

Requirements:

Minimum 2 specialized agents with distinct roles
Integration with 3+ external tools/APIs
Sophisticated reasoning and error handling
Production deployment with monitoring
Cost analysis and optimization report

Deliverables:

System architecture diagram (agents, tools, data flows)
Complete source code (GitHub with documentation)
Deployed system (API accessible)
Performance benchmarks and cost analysis
User guide and maintenance documentation
Presentation with business impact metrics

48 TOOLS & TECHNOLOGIES USED Topics

Core Frameworks: Crew AI, LangGraph, AutoGen

Automation Platform: n8n
LLM APIs: OpenAI, Google Vertex AI, Anthropic Claude
Databases: PostgreSQL, MongoDB, Pinecone (vector DB)
Message Queues: RabbitMQ, Kafka
Containerization: Docker, Kubernetes
Cloud Platforms: AWS, GCP, Azure
Monitoring: Prometheus, Grafana, DataDog, Langfuse
Languages: Python 3.10+

49 ASSESSMENT & CERTIFICATIONS Topics

Weekly coding assignments and design challenges

3 mini-projects (single agent, multi-agent crew, n8n workflow)
Capstone project with complete deployment
Portfolio-ready production system
Advanced certification in Agentic AI

HANDS-ON LABS YOU WILL WORK ON

Python & Data Analysis

Data Visualization

Statistics & EDA

SQL Analytics

LLM & Prompt Engineering

RAG & Vector Databases

AI Agent Development

Multi-Agent Systems

24×7 Access to Dedicated Cloud Lab

MEET YOUR TRAINER

Surya Kumar

Data Science Professional

15+ Years of Experience

Senior AI Engineer | Agentic & Generative AI | AWS Cloud | PMP Certified

With 12 years of industry experience, I specialize in deploying AI-powered solutions at scale, driving digital transformation for diverse organizations. My core expertise spans large language models (LLMs), advanced machine learning pipelines, retrieval-augmented generation (RAG), and AI-driven feature engineering.

I bring hands-on experience with full-stack solution development, cloud-native AI deployments on AWS, and proven cross-functional delivery in both startup and enterprise settings. My project portfolio includes designing and implementing state-of-the-art generative AI applications, optimizing ML workflows for production, and leading teams in complex, agentic AI integrations.

As a certified Project Management Professional (PMP), I ensure technical excellence and timely project execution. My training approach blends deep technical knowledge with practical, real-world application—empowering teams to deliver robust and innovative AI systems.

Key Skills:

Generative AI & LLMs (OpenAI, HuggingFace, RAG)
ML Pipeline Design & Feature Engineering
Cloud Deployment (AWS EC2, SageMaker, Lambda, API Gateway)
Full-stack Application Development (Python, JavaScript, REST APIs)
Project Leadership (Agile, Cross-functional Teams, PMP Certified)
AI Solution Architecture & Productionization
Technical Training & Mentorship

Oracle DBA RAC Data Guard ASM Performance Cloud

STUDENT SUCCESS STORIES

Sandhya Student

★★★★★

Good experience so far. The course covers Python, LLMs, prompt engineering, LangChain and RAG along with practical projects. I am getting a better understanding with every session.

Sameer Student

★★★★★

The fine-tuning topics were something I was looking forward to. I got a basic understanding of LoRA, QLoRA and when fine-tuning is useful. Good addition to the course.

Sneha Student

★★★★★

I joined the Generative AI course to understand how LLMs actually work. The practical sessions helped me a lot, especially RAG and LangChain. Overall, a good learning experience.

Suresh Student

★★★★★

My experience with the Generative AI & LLM Engineer training has been positive so far. The curriculum covers a wide range of current GenAI development topics, and I especially appreciate the emphasis on practical projects and application development.

Priyanka Student

★★★★★

I had used different AI models before, but I never really understood the difference between using an API, an open-source model and a local model. The sessions on OpenAI, Gemini, Claude, Hugging Face and local models cleared up a lot of that confusion.

Amit Student

★★★★★

Before joining, I mostly knew how to use ChatGPT and similar tools. This course helped me understand the developer side of GenAI — APIs, prompts, LangChain, RAG and building applications. Quite a different experience from just using AI tools.

Sharad Student

★★★★★

I liked the way the course brings different tools together. Instead of learning Excel, SQL, Python and Power BI as separate things, we could see how they are used during the data analysis process. The project work made that connection clearer for me.

Aditya Student

★★★★★

The course gave me a much better idea of what a data analyst actually does. Earlier I thought it was mostly Excel and charts. Learning SQL, Python, statistics, Power BI and AI together changed that understanding for me.

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DATA Science with Gen AI and Agentic AI

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