WHO ARE THE TRAINERS?

Our trainer is an OCP & OCM certified consultant and has significant amount of experience in working with the technology, having 24+ yrs of experience.

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.

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.

Best Data Science with Gen AI Training in Hyderabad & Online

COURSE CURRICULUM

45+ 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 AI

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)

21 How LLMs Work Topics

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

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

*Same as Data Analytics Module 1-2, abbreviated*
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

23 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

24 Open Source Models Topics

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

25 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

26 Advanced Techniques Topics

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

27 LangChain Fundamentals Topics

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

28 Working with LangChain Topics

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

MODULE 8: RETRIEVAL-AUGMENTED GENERATION (RAG)

29 RAG Architecture & Concepts Topics

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

30 RAG Workflow Topics

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

31 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

32 Using Vector Databases Topics

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

33 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

34 LangChain + Vector DB Integration Topics

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

35 Evaluation & Optimization Topics

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: FINE-TUNING & MODEL CUSTOMIZATION

36 Fine-Tuning Fundamentals Topics

When to Fine-Tune?
vs few-shot prompting vs RAG tradeoff analysis
Cost-benefit analysis
Use cases where fine-tuning excels

37 Fine-Tuning Methods Topics

Full fine-tuning: data requirements, compute resources
Parameter-Efficient Fine-Tuning (PEFT):
LoRA (Low-Rank Adaptation)
QLoRA (Quantized LoRA)
Prefix tuning, Prompt tuning
Instruction tuning vs domain adaptation

38 Evaluation Metrics for GenAI Topics

Task-specific metrics: classification, generation, QA
Human evaluation frameworks
Automated metrics: BLEU, ROUGE, METEOR, semantic similarity
LLM-as-Judge evaluation
Cost-quality tradeoff analysis

MODULE 10: BUILDING GENAI APPLICATIONS

39 Chatbots & Conversational AI Topics

Building chatbots with LangChain
Memory management: conversation history, summaries
Handling context limits
Streaming responses
Error handling and fallback strategies

MODULE 11: CAPSTONE PROJECT

40 End-to-End GenAI Application Project Topics

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

41 Deliverables: Topics

Application code (GitHub repository)
Fine-tuned model or RAG pipeline
API documentation
Performance benchmarks and cost analysis
Deployment guide (Docker + cloud platform)
Presentation with business metrics

42 TOOLS & TECHNOLOGIES USED Topics

Languages: Python 3.9+
LLM Frameworks: LangChain, LangGraph, HuggingFace Transformers
APIs: OpenAI, Google Vertex AI, Anthropic Claude, HuggingFace
Vector Databases: Pinecone, Weaviate, Qdrant, Chroma
Deployment: FastAPI, Docker, AWS/GCP/Azure
Notebooks: Jupyter, Google Colab
Monitoring: Langfuse, Arize

43 LEARNING OUTCOMES Topics

1. Understand LLM architecture and capabilities/limitations

2. Prompt engineer effectively for production applications

3. Build RAG systems with vector databases

4. Fine-tune models for specific domains

5. Evaluate GenAI application quality

6. Deploy and scale LLM applications

7. Design cost-effective AI solutions

8. Build production-ready chatbots and content generation systems

44 ASSESSMENT & CERTIFICATIONS Topics

Weekly coding assignments
3 mini-projects (RAG, Fine-tuning, Application)
Capstone project with business analysis
Portfolio-ready projects for LinkedIn/GitHub

45 PREREQUISITES Topics

Solid Python programming skills
Familiarity with ML/Data Science concepts (not required but helpful)
Understanding of basic API usage
No prior GenAI experience required
Displaying DS_GenAI_Syllabus.md.

HANDS-ON LABS YOU WILL WORK ON

Python & Data Analysis

SQL Data Analysis

Data Visualization

Statistics & EDA

LLM & Prompt Engineering

RAG & Vector Databases

Model Fine-Tuning

GenAI Applications

24×7 Access to Dedicated Cloud Lab

MEET YOUR TRAINER

Surya Kumar

Data Science Professional

Experience: 15+ Years

Specialization:

None

Professional Summary

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

Certifications

None

Technical Expertise

None

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.

Hanuman Student

★★★★★

I came from a non-data background, so I was a little unsure before joining. Now I can work with SQL queries, Python libraries and Power BI with much more confidence. Still have a lot to practise, but the course gave me a proper starting point.

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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DATASCIENCE with Gen AI

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