Data Science with Generative AI Course in Hyderabad
Learn data science end to end and extend it with generative AI applications
The Data Science with Generative AI course combines a full data science curriculum with modern generative AI skills. You start with Python, SQL, statistics and exploratory analysis, then study machine learning with scikit-learn: regression, classification, ensembles, clustering, feature engineering, evaluation and tuning. You move on to deep learning with neural networks, natural language processing and computer vision basics, followed by generative AI: large language models, prompt engineering, embeddings, retrieval-augmented generation, agents, evaluation and responsible AI. You also learn data visualisation and dashboards, model deployment with FastAPI and Docker, experiment tracking with MLflow, and an introduction to MLOps and LLMOps. The programme uses real datasets and several capstone-level projects, including a classical machine learning model and a generative AI assistant. It suits graduates and working professionals who want a broad, current data science profile; no programming experience is required. It is longer than the Generative AI with Python course and adds the full data science foundation. Classes run in Ameerpet, Hyderabad, and in live online batches.
Syllabus
Python & Data Science Foundations
- •Python syntax, functions, classes and packages
- •Jupyter, virtual environments and Git
- •The data science workflow from question to deployment
- •Lab: Build small Python utilities and publish them on GitHub
- •Outcome: A working development environment and Python base
Data Analysis with NumPy, pandas & SQL
- •NumPy and pandas for cleaning, joining and aggregating
- •SQL queries, joins and window functions
- •Reading from files, databases and APIs
- •Lab: Build a clean analytics table from three sources
- •Outcome: Prepare data reliably
Statistics & Visualisation
- •Descriptive statistics, distributions and probability
- •Confidence intervals, hypothesis tests and correlation
- •Matplotlib, Seaborn and Power BI basics for storytelling
- •Lab: Run an EDA and statistical test on a business dataset
- •Outcome: Support conclusions with statistics and visuals
Supervised Machine Learning
- •Linear and logistic regression, KNN, SVM and naive Bayes
- •Metrics, cross-validation and avoiding leakage
- •Pipelines with scikit-learn
- •Lab: Build and compare models on a churn dataset
- •Outcome: Build and evaluate predictive models
Trees, Boosting & Model Tuning
- •Decision trees, random forests and gradient boosting
- •Hyperparameter tuning and handling imbalanced data
- •Explainability with SHAP
- •Lab: Tune a boosted model and explain predictions
- •Outcome: Build strong tabular-data models
Unsupervised Learning & Forecasting
- •K-means, hierarchical clustering, DBSCAN and PCA
- •Anomaly detection and recommender basics
- •Time series forecasting with ARIMA, Prophet and ML approaches
- •Lab: Segment customers and forecast demand
- •Outcome: Find structure and forecast trends
Deep Learning Foundations
- •Neural networks, backpropagation and optimisers
- •Training loops with PyTorch; regularisation
- •CNNs for images and LSTMs for sequences
- •Lab: Train an image classifier with transfer learning
- •Outcome: Build and train deep learning models
Natural Language Processing
- •Text cleaning, TF-IDF and classical classifiers
- •Embeddings and transformers with Hugging Face
- •Fine-tuning for classification and named entity recognition
- •Lab: Build a text classifier with a pre-trained transformer
- •Outcome: Work with language data using modern models
Generative AI & Prompt Engineering
- •How LLMs work, tokens, context and sampling
- •Prompt patterns, structured output and API calls
- •Function calling and tool use; model selection
- •Lab: Build an LLM assistant that extracts data and calls tools
- •Outcome: Build reliable LLM features
Embeddings, Vector Search & RAG
- •Embeddings, similarity and vector databases
- •RAG pipelines: chunking, retrieval, re-ranking and citations
- •Evaluating retrieval and answers
- •Lab: Build a document question-answering assistant
- •Outcome: Build grounded assistants over private data
Agents, Fine-Tuning & Evaluation
- •Agents, tools and workflows with LangChain or LangGraph
- •Fine-tuning small models with LoRA at an introductory level
- •Evaluation sets, LLM-as-judge limits and safety testing
- •Lab: Build and evaluate a tool-using agent
- •Outcome: Build and test multi-step AI workflows
Deployment, MLOps & LLMOps Basics
- •Serving models and LLM apps with FastAPI and Docker
- •Experiment tracking with MLflow; CI with GitHub Actions
- •Monitoring, drift and token cost control
- •Lab: Deploy a model API and an LLM app
- •Outcome: Take projects from notebook to service
Responsible AI, Capstones & Interview Preparation
- •Bias, fairness, privacy, security and governance
- •Capstone 1: end-to-end ML project; Capstone 2: generative AI assistant
- •Portfolio, resume and mock interviews
- •Outcome: Two deployed portfolio projects and interview readiness
Tools Covered
Projects Included
- Customer Churn Prediction: Build, tune and explain a boosted model; deliverable is a notebook and business memo.
- Demand Forecasting: Compare statistical and ML forecasts for a retail dataset; deliverable is a forecast report.
- Text Classification with Transformers: Fine-tune a transformer on support tickets; deliverable is a model and evaluation.
- Document Question-Answering Assistant: Build a RAG assistant with citations and an evaluation set; deliverable is a demo and test sheet.
- Capstone Pair: Deploy one classical ML API and one generative AI assistant with monitoring; deliverable is two GitHub repositories and architecture diagrams.
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Call: +91 9908099980Prerequisites & Eligibility
Understand the foundation and skills needed to succeed in the Data Science with Generative AI course.
Course Prerequisites
- Any graduate is eligible for enrollment across all program tracks
- Dedication to work on real-time scenario projects and live client project implementations
- Willingness to engage in aptitude, soft skills and interview readiness sessions
- Commitment to complete full-day training or intensive/internship program schedules
Why Choose GloryTecks
Build practical capabilities through structured learning, expert mentors and hands-on projects.
5+ Years of Excellence
Established training institute in Hyderabad with a proven track record of producing industry-ready professionals.
Expert Faculty
Learn from trainers with real-world industry experience, who teach the way the work is actually done.
Placement Assistance
A dedicated placement team with tie-ups across hiring companies. We support you until you get hired.
Comprehensive Curriculum
Updated syllabus covering the latest technologies and industry best practices, with hands-on projects.
Career Growth Focus
Not just training, but complete career transformation with soft skills and interview preparation.
Online & Classroom
Attend live online batches or classroom training at our Ameerpet centre, whichever suits you.
Flexible Batches
Weekday, weekend and online batch timings to suit working professionals, students and freshers.
Real-time Projects
Work on real-time scenario projects so you finish with practical experience to show employers.
We provide placement support with direct access to hiring companies, along with comprehensive training that helps freshers become job-ready professionals.
Certification
Explore certification and practical learning opportunities with a focus on Data Science with Generative AI.
Certificate of Completion
Data Science with Generative AI
This certifies that
[Your Name]
has successfully completed the
Data Science with Generative AI
Training Program
GloryTecks
Ameerpet, Hyderabad
Certificate ID
GT-YYYY-XXXX
Certificate Includes
- Course completion certificate from GloryTecks
- Project completion certificates
- Internship completion certificate for eligible internship programs
- Digital certificate with a unique verification ID
- Shareable on LinkedIn and other professional platforms
- Valid proof of skill acquisition for employers
- Includes the detailed syllabus covered
Industry Focus
A certificate built around the tools and skills employers ask for, adding credibility to your resume.
Skill Validation
Proves your expertise in the course and the technologies you worked with.
Career Advancement
Strengthens your profile when you apply for jobs and negotiate your salary.
Digital & Physical
Get a digital certificate for online sharing and a physical copy for framing.
Course Certificate
Upon successful completion of the training program
Project Certificate
For major projects completed during training
Course Features & Highlights
Build a strong foundation through a structured Data Science with Generative AI course with practical learning and hands-on exposure.
Live Projects
Work on real-world projects from day one with industry use cases.
Expert Trainers
Learn from industry professionals with real-world experience.
Placement Support
Dedicated placement team with assistance until you get hired.
Industry Certification
Get certified and boost your resume with recognized credentials.
LMS Access
Access to learning materials and recorded sessions.
Mock Interviews
Weekly mock interviews to prepare you for real job scenarios.
Resume Building
Professional resume preparation and LinkedIn profile optimization.
Additional Benefits
- Flexible Payment Plans
- 6-12 Months LMS Access
- Doubt Clearing Sessions
- 24/7 Learning Support
Frequently asked questions
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Best Data Science with Generative AI Training in
Ameerpet, Hyderabad
Located at Ameerpet — Hyderabad’s IT training hub. Easily accessible from all areas of Hyderabad. Just 2 minutes walk from Ameerpet Metro Station.
Placement Support
Career support covers resume and LinkedIn preparation, mock interviews and a portfolio of 5 course projects. Roles that use these skills include Data Scientist (entry level), Machine Learning Engineer (entry level), Generative AI Developer, Data Analyst; support is guidance with the job search, not a guarantee of a job.