Career Program · KPHB, Hyderabad & Online

Data Science + GenAI + Agentic AI

From data science fundamentals to generative AI and intelligent agents.

A complete, practical path from your first line of Python to machine learning and deep learning — continuing into generative AI, RAG and agentic AI systems. Built around real datasets and portfolio projects.

Course fee ₹30,000
Batches 9:30 AM – 11:30 AM · 11:30 AM – 1:30 PM
16 modules
102 topics

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Data Science + GenAI + Agentic AI course at The Data School, KPHB, Hyderabad

What you will master

Write Python for data work
Analyse and visualise real datasets
Apply statistics and SQL
Train and evaluate machine learning models
Understand deep learning and NLP foundations

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16Modules
102Topics covered
8Learning outcomes
14Tools & technologies

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Course Overview

What this program covers.

Career relevance

Data science skills are used across industries to forecast demand, understand customers, reduce risk and build intelligent products — and generative and agentic AI are now part of that toolkit. This program prepares you for roles where you turn data into models, AI applications and automated decisions.

Who it's for

Graduates, working professionals and career switchers who want to become data scientists and AI engineers.

Prerequisites

Basic computer literacy and school-level math. No prior coding required.

Learning outcomes

Write Python for data work
Analyse and visualise real datasets
Apply statistics and SQL
Train and evaluate machine learning models
Understand deep learning and NLP foundations
Build applications on top of LLMs
Build and evaluate RAG pipelines
Design AI agents that use tools and run multi-step workflows

Detailed Syllabus

16 modules. One clear path.

Every module, every topic. Expand a module to see exactly what you will learn.

16 modules · 102 topics

Learn the Python fundamentals you will use every day as a data professional.
What you will learn
- Installing Python and Jupyter
- Variables, data types and operators
- Conditionals and loops
- Functions and modules
- Lists, tuples, dictionaries and sets
- File handling and exceptions
- Introduction to OOP
Load, clean and reshape data efficiently with the core Python data libraries.
What you will learn
- NumPy arrays, indexing and broadcasting
- Series and DataFrames
- Loading CSV, Excel and JSON
- Cleaning missing and duplicate data
- Filtering, sorting and groupby
- Merging and joining
- Time series basics
Explore datasets and communicate what you find with clear, honest charts.
What you will learn
- The EDA workflow
- Matplotlib
- Seaborn
- Distributions, correlations and outliers
- Choosing the right chart
- Storytelling with data
Build the statistical reasoning needed to draw reliable conclusions from data.
What you will learn
- Descriptive statistics
- Probability and distributions
- Sampling and the central limit theorem
- Confidence intervals
- Hypothesis testing and p-values
- A/B testing
- Correlation vs causation
Query relational databases confidently to answer real business questions.
What you will learn
- Relational concepts
- SELECT, WHERE and ORDER BY
- Aggregations and GROUP BY
- Joins
- Subqueries and CTEs
- Window functions
- SQL with Python
Train, validate and compare your first supervised and unsupervised models.
What you will learn
- Supervised vs unsupervised learning
- Linear and logistic regression
- Decision trees and k-NN
- Train/test split and cross-validation
- Accuracy, precision, recall and RMSE
- Scikit-learn pipelines
- k-means clustering
Improve model performance with ensembles, better features and careful tuning.
What you will learn
- Random forests and gradient boosting
- Feature engineering and selection
- Hyperparameter tuning
- Handling imbalanced data
- PCA
- Model interpretation
Understand how neural networks learn and how they power modern AI.
What you will learn
- Neurons, layers and activations
- Backpropagation and gradient descent
- Building networks in PyTorch
- Overfitting and regularisation
- Intro to CNNs and sequence models
- How modern AI and LLMs fit in
Turn unstructured text into features, classifications and insights.
What you will learn
- Text preprocessing and tokenisation
- Bag of words and TF-IDF
- Word embeddings
- Text classification and sentiment analysis
- Named entity recognition
- Introduction to transformers
- Pretrained language models
Understand how large language models work and write prompts that produce reliable output.
What you will learn
- What generative AI is
- Transformers and LLMs at a high level
- Tokens, context windows and hallucinations
- Zero-shot and few-shot prompting
- Chain-of-thought
- Structured outputs (JSON)
Integrate LLMs into software and represent meaning as vectors to power search.
What you will learn
- Calling LLM APIs
- System prompts and parameters
- Function / tool calling
- Embeddings and similarity
- Vector databases and chunking
- Semantic and hybrid search
Ground model answers in your own data, and measure the quality of what comes back.
What you will learn
- RAG architecture
- Ingestion pipelines
- Retrieval and re-ranking
- Context construction
- Reducing hallucinations and citing sources
- Evaluating RAG outputs
Understand what makes a system agentic and give agents safe abilities to act.
What you will learn
- Agents vs chatbots
- The perceive-plan-act loop
- Reasoning and planning patterns
- Tool schemas and function calling
- Calling external APIs and databases
- Error handling and validation
Structure multi-step work, manage memory and coordinate specialised agents.
What you will learn
- Workflows vs autonomous agents
- Task decomposition, retries and fallbacks
- Human-in-the-loop
- Short and long-term memory
- RAG as an agent tool
- Supervisor / worker multi-agent designs
Keep generative and agentic systems measurable, safe and cost-controlled.
What you will learn
- Evaluating LLM and agent outputs
- Test sets and success metrics
- Prompt injection
- Input / output guardrails
- Logging and observability
- Cost and loop controls
Bring everything together in end-to-end data science and AI projects you can show employers.
What you will learn
- Problem framing and data collection
- Cleaning, EDA and modelling
- Building a RAG assistant
- Building an AI agent
- Publishing to GitHub
- Presenting your work

Learning Experience

How you will learn.

Instructor-led learning
Structured sessions that explain the why, not just the how.
Hands-on exercises
Practise every concept in code as you learn it.
Assignments
Reinforce each module with focused, reviewed work.
Applied practice
Combine multiple skills to solve realistic problems.
Portfolio development
Package your work so employers can see what you built.
Interview preparation
Practise technical questions and project walkthroughs.

Tools & Technologies

The stack you will use.

Python
NumPy
Pandas
Matplotlib
Seaborn
SQL
Scikit-learn
PyTorch
NLP
LLMs
Embeddings
Vector Databases
RAG
AI Agents

Projects

Projects you'll build.

Portfolio projects that bring the syllabus together — each ends with a GitHub repository you can show employers.

Data Science

Customer Churn Prediction

Businesses lose revenue when customers leave. This project builds a model that predicts which customers are at risk and explains what drives churn.

What you'll build

Data cleaning and exploratory analysis
Feature engineering
Baseline and improved classification models
Evaluation with precision, recall and ROC-AUC
Feature importance summary for business teams

You'll leave with: Trained and evaluated model · Notebook with full analysis · Business-focused summary · GitHub repository

Python
Pandas
Scikit-learn
Matplotlib
Seaborn
SQL

Machine Learning

Recommendation Engine

Users face too many choices. This project builds a system that suggests relevant items based on behaviour and item similarity.

What you'll build

Interaction data preparation
Popularity baseline
Content-based recommender
Collaborative filtering approach
Evaluation of recommendation quality

You'll leave with: Working recommender · Notebook with experiments · Comparison of methods · GitHub repository

Python
Pandas
NumPy
Scikit-learn
Machine Learning

Generative AI

RAG Document Assistant

Teams keep knowledge in PDFs and documents that are slow to search. This project builds an assistant that answers questions from your own documents and shows where each answer came from.

What you'll build

Document ingestion and chunking pipeline
Embedding and vector index
Retriever with metadata filters
Answer generation grounded in retrieved context
Chat interface with source citations

You'll leave with: Working assistant · GitHub repository with README · Evaluation notes · Demo walkthrough

LLMs
Embeddings
Vector Databases
RAG
APIs
Python

Agentic AI

AI Research Agent

Research means searching, reading and summarising many sources. This project builds an agent that plans a research task, gathers sources with tools and writes a structured summary.

What you'll build

Planner that breaks a question into steps
Search and page-reading tools
Memory of findings so far
Report writer with a source list
Guardrails limiting steps and cost

You'll leave with: Working research agent · GitHub repository with README · Sample research reports · Demo walkthrough

AI Agents
Tool Calling
Memory
Workflows
LLMs
Python

Career Outcomes

Roles this program prepares you for.

Relevant roles for the skills you will build. Outcomes depend on individual effort, background and the job market.

Data Scientist
Machine Learning Engineer
Generative AI Engineer
Agentic AI Engineer

FAQ

Questions, answered.

Still unsure? An advisor can walk you through the program.

Where should I start if I am new to Data Science + GenAI + Agentic AI?

Start with Module 01. The program assumes no prior coding and builds from Python fundamentals through machine learning, then into generative AI and agents, so you can follow the modules in order.

Is the program project-based?

Yes. Every module is paired with hands-on exercises, and the program builds towards portfolio projects that you can publish on GitHub and discuss in interviews.

Do I need to know how to code?

No. You will learn Python from the beginning. Comfort with basic computer use and school-level math is enough to start.

What learning modes are available?

Programs are designed around instructor-led learning. Talk to an advisor for the learning modes (online, classroom or hybrid) available for the next batch.

Does the program include interview preparation?

Yes. Interview preparation is part of the learning experience, covering technical questions, project walkthroughs and how to present your portfolio.

Where are Data Science + GenAI + Agentic AI classes held?

Classroom sessions run at The Data School, 4th Floor, MIG 549, Road Number 1, Above HDFC Bank, KPHB Colony, Kukatpally, Hyderabad — close to KPHB Colony Metro station. Live online and hybrid options are also available.

Can I attend a free demo class for Data Science + GenAI + Agentic AI?

Yes. Book a free demo class at our KPHB centre or online — there's no obligation to enrol. The centre is open Monday to Saturday, 9 AM to 9 PM.

What is the course fee, and when do batches start?

The course fee is ₹30,000. Classroom batches run Monday to Saturday, 9:30 AM – 11:30 AM or 11:30 AM – 1:30 PM. You can attend a free demo class first — book one online or call the centre, and an advisor will share the next start date.

Learn Data Science + GenAI + Agentic AI near you

Classroom batches Monday–Saturday (9:30 AM – 11:30 AM or 11:30 AM – 1:30 PM) at Road Number 1, KPHB Colony, Kukatpally — plus live online and hybrid options. Course fee ₹30,000.

Ready to Start Your AI Career?

Course fee ₹30,000. Speak with an advisor about the Data Science + GenAI + Agentic AI program, learning modes and upcoming batch dates.

Apply Now Book a Free Demo Class

Data Science + GenAI + Agentic AI

What this program covers.

Career relevance

Who it's for

Prerequisites

Learning outcomes

16 modules. One clear path.

How you will learn.

Instructor-led learning

Hands-on exercises

Assignments

Applied practice

Portfolio development

Interview preparation

The stack you will use.

Projects you'll build.

Customer Churn Prediction

What you'll build

Recommendation Engine

What you'll build

RAG Document Assistant

What you'll build

AI Research Agent

What you'll build

Roles this program prepares you for.

Questions, answered.

Where should I start if I am new to Data Science + GenAI + Agentic AI?

Is the program project-based?

Do I need to know how to code?

What learning modes are available?

Does the program include interview preparation?

Where are Data Science + GenAI + Agentic AI classes held?

Can I attend a free demo class for Data Science + GenAI + Agentic AI?

What is the course fee, and when do batches start?

Learn Data Science + GenAI + Agentic AI near you

Ready to Start Your AI Career?