4.9★
AI-InfusedData Science with Agentic AI online course
15 MonthsOnline + Classroom
Next batch 9 Nov 2026
Free LMS · 1 yrPlacement supportNo-cost EMI
New ArrivalAI-InfusedAdvanced Data Science
Advanced Data Science Online Course with Gen AI
The Best Data Science Online Course with Gen AI is a well-structured program that connects core data science expertise with cutting-edge developments in Generative AI. It emphasizes key areas such as data analysis, machine learning, and deep learning, while also introducing learners to modern Gen AI technologies like ChatGPT, LLMs, and AI-driven applications. Through practical projects, expert guidance, and real-world case studies, the course equips you with both the skills and confidence to apply your knowledge effectively in professional settings.
Duration12 Months
ModeOnline
Next batch9 Nov 2026
PaymentNo-cost EMI
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Highest package offered ₹15 LPA4.8★Google rating4.9★Justdial rating20,000+ learners trained2,000+ placed
Program highlights
Build Industry-Ready Skills with Practical Training
12 MonthsStructured online training
OnlineLearn from anywhere
1 yearFree BrillicaNext LMS access
15PDP & interview sessions
100%Placement assistance
0%No-cost EMI available
AI-Infused Courses
Build industry-relevant skills with modern AI tools and technologies integrated into your learning experience.
Free LMS for 1 Year
Access class recordings, study notes, quizzes, assignments, and projects on BrillicaNext for 12 months to support continuous learning.
BrillicaNext Mobile App
Stay connected to your learning journey with convenient mobile access to course resources on Android and iOS.
Placement Support
Prepare for career opportunities with resume building, LinkedIn profile guidance, mock interviews, and placement assistance.
No-Cost EMI
Make your learning journey more accessible with convenient monthly instalments through available EMI options, subject to applicable terms.
Curriculum
Explore the Course Curriculum
- Data fundamentals and the pillars of data analytics
- Data Analytics vs Data Science
- Machine Learning, AI and Generative AI explained
- Data science lifecycles and workflows
- Roles and career paths in data science
- DBMS vs RDBMS, MySQL / PostgreSQL setup
- SQL command types: DDL, DML, DQL, DCL, TCL
- Data types, schemas, CRUD and loading data
- SELECT, aliases, LIMIT/OFFSET, DISTINCT
- WHERE, BETWEEN, LIKE, REGEXP and sorting
- Text, date-time, NULL and CAST functions
- GROUP BY, HAVING, WITH ROLLUP
- CASE statements and pivoting with CASE WHEN
- Set operations, constraints, keys and normalization (1NF–3NF)
- ER diagrams; inner, outer, cross, self and anti-joins
- Nested and correlated subqueries, EXISTS / ANY / ALL
- Chaining multiple CTEs
- Window functions: ROW_NUMBER, RANK, LAG, LEAD, running totals
- Views, stored procedures, temp tables, indexes and EXPLAIN
- Exporting results; MySQL with Python (pandas.read_sql)
- Setup, Jupyter Notebook, comments and variables
- Data types, type casting, input and operators
- Importing modules: random, math, datetime
- Strings, lists, tuples, sets and dictionaries
- Indexing, slicing, functions and methods
- Comprehensions and generator expressions
- Conditionals, ternary operator, loops, break/continue
- Functions, *args/**kwargs, scope (LEGB) and recursion
- Lambda and higher-order functions: map, filter, reduce, zip
- User-defined modules, packages and pip
- File handling: text, CSV and JSON
- Exception handling and custom exceptions
- Classes, objects, constructors and self
- Instance, class and static methods
- Encapsulation, inheritance, polymorphism, abstraction
- NumPy array creation, attributes and random module
- Indexing, reshaping, stacking and splitting
- Vectorisation, broadcasting and np.where / np.select
- Series and DataFrames; reading CSV, Excel, JSON and SQL
- Selection with loc / iloc, filtering and sorting
- Missing values, duplicates, string cleaning and apply()
- Binning, outlier treatment (IQR), GroupBy and agg()
- Pivot tables, crosstab, melt, merge and join
- Date-time handling, resampling and rolling windows
- Matplotlib: line, bar, area, pie, scatter and distribution charts
- Customisation, subplots and styles
- Seaborn: categorical, distribution, relational and matrix plots
- Univariate, bivariate and multivariate analysis
- Correlation, outliers and missing-value patterns
- Notation (Σ, Π, sets), functions and graphs
- Exponents, logarithms and power transformations
- Vectors, norms, dot product and cosine similarity
- Matrices, inverse, determinant, rank and vector spaces
- Eigenvalues, eigenvectors, SVD, covariance matrix and PCA
- Limits, derivatives, partial derivatives and gradients
- Chain rule — the foundation of backpropagation
- Loss functions, gradient descent and learning rate
- Entropy, information gain and cross-entropy
- Population vs sample, data and variable types
- Central tendency, dispersion, percentiles, skewness and kurtosis
- Z-scores, normal distribution, Q-Q plots and normality tests
- Log, Box-Cox, Yeo-Johnson and quantile transforms
- Standardisation, min-max and robust scaling
- Counting, conditional probability and Bayes' theorem
- PMF, PDF, CDF and key distributions (Binomial, Poisson, Normal, t, Chi-Square, F)
- Sampling, Law of Large Numbers and Central Limit Theorem
- Estimation, MLE and confidence intervals
- Hypothesis testing, p-values, errors, power and effect size
- Z / t-tests, ANOVA, Chi-Square and non-parametric tests
- Correlation vs causation, bootstrap and Monte Carlo
- A/B testing and experimental design
- SciPy and Statsmodels on real-world datasets
- Garbage in, garbage out and the CRISP-DM workflow
- Features vs target and types of ML problems
- Automated profiling with ydata-profiling and Sweetviz
- Cardinality, constant and correlated features
- Target-driven analysis and feature interactions
- Missingness mechanisms (MCAR, MAR, MNAR) and imputation
- Outlier engineering with Isolation Forest and LOF
- Categorical encoding and high-cardinality handling
- Multicollinearity (VIF), scaling and domain features (RFM)
- Feature selection: filter, wrapper and embedded methods
- Imbalanced data: SMOTE, ADASYN and class weights
- Data leakage, splits, cross-validation and Scikit-learn pipelines
- Types and categories of ML algorithms
- Problem formulation, overfitting and the bias-variance trade-off
- Simple, multiple and polynomial regression
- Ridge, Lasso and Elastic Net; MAE, RMSE, R²
- Logistic regression, KNN, Naive Bayes, SVM and decision trees
- Confusion matrix, precision, recall, F1 and ROC-AUC
- Random Forest, AdaBoost, GBM, XGBoost, LightGBM, CatBoost, stacking
- Cross-validation, Optuna tuning and model selection
- K-Means, hierarchical, DBSCAN and GMM clustering
- PCA, t-SNE, UMAP, anomaly detection and recommenders
- ARIMA, SARIMA, Holt-Winters and Prophet
- SHAP, PDP / ICE and end-to-end pipelines
- Kaggle: Titanic, House Prices, Spaceship Titanic
- DevOps vs MLOps and maturity levels
- Batch vs real-time inference; canary and blue-green deployment
- Modular project structure and environments
- Config, logging, type hints, pytest and data validation
- Git and GitHub workflows for ML
- MLflow experiment tracking and model registry
- DVC data versioning; Pickle, Joblib and ONNX
- Streamlit and Gradio apps
- FastAPI ML APIs with Pydantic
- Docker, Docker Compose and Kubernetes basics
- GitHub Actions, Airflow and Prefect
- Deploying on Render, Hugging Face, AWS, Azure and GCP
- Prediction logging, Prometheus and Grafana
- Data and model drift with Evidently AI
- Perceptron, MLP, forward propagation and activations
- Loss functions and backpropagation from scratch
- Optimisers (SGD, Adam, AdamW) and initialisation
- Schedulers, dropout, batch norm and tuning
- TensorFlow, Keras callbacks and TensorBoard
- PyTorch tensors, autograd and training loops
- CNNs, data augmentation and famous architectures
- Object detection with YOLO and image segmentation (U-Net)
- RNN, LSTM, GRU, Seq2Seq and attention
- Transformer architecture and Vision Transformers
- Autoencoders, VAEs, GANs and diffusion models
- ONNX, quantisation and TensorFlow Lite
- NLP workflow, text collection and regex for NLP
- NLTK, spaCy, TextBlob, Gensim and Hugging Face
- Cleaning, tokenisation, stemming and lemmatisation
- POS tagging, dependency parsing and Hinglish / Indian-language text
- Bag of Words, N-grams, TF-IDF and text similarity
- Text classification, NER and topic modelling (LDA, NMF)
- Word2Vec, GloVe, FastText and Sentence-BERT
- LSTM / BiLSTM text models and Seq2Seq translation
- Subword tokenisation, BERT family, GPT, T5 and BART
- Hugging Face fine-tuning, summarisation and QA systems
- BLEU, ROUGE, METEOR and perplexity
- How LLMs are trained and aligned (SFT, RLHF, DPO)
- Generation parameters and the LLM landscape
- OpenAI, Anthropic and Gemini APIs; structured outputs
- Running open-source LLMs with Ollama; tokens, cost and latency
- Prompt patterns, chain-of-thought and templates
- Managing system prompts, history and memory
- Vector databases: FAISS, ChromaDB, Pinecone, Qdrant
- Chunking, hybrid search, re-ranking and GraphRAG
- RAG evaluation with RAGAS
- LangChain and LlamaIndex
- LoRA / QLoRA fine-tuning; multimodal models, Whisper, Stable Diffusion
- Gen AI-assisted EDA, data cleaning and code generation
- Text-to-SQL and natural-language analytics
- Synthetic data generation and LLM-based data labelling
- Chatbots and copilots with Streamlit and Gradio
- Multimodal apps: vision-language models, speech and image generation
- Cost, latency and quality optimisation for Gen AI apps
- LLM evaluation, guardrails and responsible AI
- LangSmith / Langfuse, FastAPI, Docker and vLLM serving
Tools & technologies
Master Industry Tools Through Hands-On Practice
Pandas
Python
Numpy
SQL
Tensor Flow
AWS
Matplotlib
Scikit Learn
Gen AI
PyTorchProjects
Build Real Projects. Showcase Your Skills
Project 01
Car's Ex-Showroom Price Prediction
Predict car ex-showroom prices using machine learning based on features, specifications, and brand.
Project 02
Kidney Stone Classification Project
Classify kidney stone cases using medical data and machine learning classification techniques.
Project 03
Brain Tumor Detection Using MRI Scan Images
Detect brain tumors from MRI scan images using image processing and deep learning models.
Project 04
Casting Iron Defect Image Classification
Identify casting iron defects from images using computer vision and deep learning techniques.
Project 05
SPAM Detection
Classify messages or emails as spam or legitimate using natural language processing and machine learning.
Project 06
Recommendation System
Recommend relevant products or content based on user preferences, behavior, and historical data.
Certification
Earn an industry-recognised certificate
Showcase your skills and project experience with a Brillica Services certificate. Add it to your resume and LinkedIn profile to highlight your achievements, strengthen your professional credibility, and stand out to potential employers.
- Worldwide RecognitionShowcase your course completion and practical skills with a certificate from Brillica Services to support your professional profile when applying for opportunities.
- Lifetime ValidityYour certificate remains valid for a lifetime, allowing you to highlight your learning achievement throughout your career.
- Project-Based AchievementEarn your certificate by completing course requirements, projects, and assessments, demonstrating your practical learning beyond classroom attendance.
Career outcomes
Our learners work at top companies
Our placement team prepares you for career opportunities across industries with expert guidance, interview preparation, and job-readiness support.
Data ScientistML EngineerAI EngineerData AnalystGenAI DeveloperNLP Engineer
Who should join
Built for beginners and professionals who want a career in Data Science and AI
- Graduates and final-year students (B.Tech, BCA, MCA, B.Sc) who want a first job in data science or AI.
- Software developers and testers moving into data science, machine learning and AI engineering.
- Data and business analysts who want to add Python, machine learning and Gen AI to their skills.
- Working professionals and career switchers who want a structured online path into AI.
- Founders and freelancers who want to use AI and data to automate work and decisions.
Mentors
Learn from Industry Experts
Prashant SinghSr. Training ConsultantEXP 14+ yrs
Alumni
Recently placed Brillica learners
Kanishka GuptaData Analyst
Placed at
Pankaj BhandariSr. Data Analyst
Placed at
Kajal BhandariData Analyst
Placed at
Pawan Lakhera Data Analyst
Placed at
Brillica Job-Readiness Program · included free
Skills open the door. Confidence gets you the job.
Alongside your regular course, every Brillica learner goes through our Job-Readiness Program and Personal Development Program (PDP). Resume, communication, GD, aptitude, interview skills and mock interviews, until you're ready to crack the interview.
15+PDP sessions per program
10Job-readiness modules
1:1Resume & mock interview reviews
₹0Extra cost
Resume building
One-on-one resume reviews with ATS-friendly templates for your target role.
Recruiter-ready resumeLinkedIn & portfolio
Optimised LinkedIn profile, GitHub, Behance or campaign portfolio from your course projects.
Profile that gets foundCommunication skills
Spoken English, email writing, presenting your projects and speaking with confidence.
Clear, confident speakingGroup discussion (GD)
Weekly GD rounds on tech, business and current topics with feedback on structure and clarity.
Clear the GD roundAptitude & reasoning
Quantitative, logical and verbal practice in the format used by campus and off-campus drives.
Pass screening testsFees & EMI
Invest in your career, pay monthly
Festive offer · up to 30% offProgram feeAsk for this month's priceAlready counselled? Pay online securely →
No-cost EMI0% interest
Free LMS + BrillicaNext app1 year
Placement supportIncluded
Interview PreparationMock interviews and career guidance
What's included
- Live classroom or online classes
- AI-infused curriculum & tools
- BrillicaNext LMS & app for 1 year
- Projects with mentor reviews
- Job-Readiness Program: resume, GD, mock interviews
- Placement drives & referrals
- Industry-recognised certificate
About the program
About Advanced Data Science Online Course with Gen AI
In today’s world, Data Science has become one of the most in-demand career paths. With the rise of Artificial Intelligence, Machine Learning, and now Generative AI (Gen AI), professionals who master these skills stand out in the job market. If you want to pursue a career in this domain, joining the Best Data Science Course with Gen AI will provide you with the right guidance and skillset, practical knowledge, and future-ready expertise.
At Brillica Services, our Advanced Data Science Course with Gen AI is designed to help learners gain hands-on exposure to real-world tools, techniques, and projects. This course covers everything from data processing to advanced machine learning models and also focuses on integrating Generative AI, the most revolutionary technology of recent times.
Why Choose an Advanced Data Science Course with Gen AI?
Generative AI is reshaping industries like healthcare, finance, IT, e-commerce, and marketing. Unlike traditional AI models that only analyze existing data, Gen AI can generate new data, create content, design solutions, and even automate decision-making. Professionals skilled in both Data Science and Gen AI are now among the most valued in organizations.
By joining the Best Data Science Course with Gen AI, you will:
Gain expertise in Data Science fundamentals and advanced techniques.
Master tools like Python, SQL, Power BI, TensorFlow, and PyTorch.
Work with Generative AI applications like ChatGPT, DALL·E, and LLMs.
Learn to build predictive models, recommendation systems, and AI-powered solutions.
Enhance your career prospects with industry-recognized certification.
Who Can Join This Course?
The Advanced Data Science Course with Gen AI in is perfect for:
Students who have completed their 12th or graduation and want to build a career in Data Science.
Working professionals from IT, analytics, finance, or business backgrounds who want to upskill.
Entrepreneurs and freelancers are aiming to use AI for business growth.
Anyone passionate about learning how data and AI shape the modern digital world.
Detailed Course Curriculum
Our course is designed in a step-by-step manner, covering both theory and hands-on practice.
1. Introduction to Data Science & Gen AI
Overview of Data Science, AI, and ML
Evolution of Generative AI
Applications of Data Science with Gen AI in real-world industries
2. Programming for Data Science
Python fundamentals (data types, functions, loops)
Libraries: NumPy, Pandas, Matplotlib, Seaborn
Data manipulation and visualization
3. Data Wrangling & Preprocessing
Handling missing values
Data cleaning techniques
Feature scaling and transformation
Exploratory Data Analysis (EDA)
4. Databases & Big Data Tools
SQL for Data Science
Introduction to Big Data, Hadoop & Spark basics
Working with structured and unstructured data
5. Statistics & Probability for Data Science
Descriptive statistics
Hypothesis testing
Probability distributions
Correlation and regression
6. Machine Learning & Predictive Analytics
Supervised learning (Linear & Logistic Regression, Decision Trees)
Unsupervised learning (Clustering, PCA)
Model evaluation and optimization
Building predictive models
7. Deep Learning Foundations
Neural Networks
Convolutional Neural Networks (CNNs)
Recurrent Neural Networks (RNNs)
Transfer Learning
8. Generative AI (Gen AI) Module
Understanding Large Language Models (LLMs)
Introduction to ChatGPT, GPT-4, and beyond
Text, image, and code generation with AI
Prompt engineering for Gen AI applications
Building AI-powered chatbots and virtual assistants
9. Data Visualization & Business Intelligence
Power BI
Creating dashboards and reports
Data storytelling for decision-making
10. Capstone Projects & Real-World Applications
Learners will work on industry projects such as:
Customer behavior prediction
Fraud detection system
AI-powered chatbot using Gen AI
Image recognition model
Sales forecasting using predictive analytics
Tools & Technologies Covered
Throughout the course, you will gain hands-on practice with:
Programming Languages: Python, R, SQL
Libraries & Frameworks: TensorFlow, PyTorch, Keras, Scikit-learn
Visualization Tools: Power BI, Matplotlib, Seaborn
AI Platforms: OpenAI, Hugging Face, Google AI, Microsoft Azure AI
Career Opportunities After Completing the Course
Completing the Best Data Science Course with Gen AI in opens up multiple career opportunities in India and globally. Some of the top job roles include
Data Scientist
Machine Learning Engineer
AI Engineer
Business Intelligence Analyst
Data Analyst
Gen AI Specialist
Research Scientist
According to industry reports, the demand for professionals skilled in Data Science with Gen AI is expected to rise by more than 40% in the coming years. Salaries in this field are among the highest in the IT industry, making it one of the most rewarding career paths.
Why Brillica Services?
At Brillica Services, we are committed to delivering high-quality training with real-world outcomes. Our key highlights:
Industry-expert trainers with years of experience.
100% practical learning with live projects.
Flexible learning options: classroom & online.
Placement assistance and career mentorship.
Industry-recognized certification.
Choosing Brillica Services ensures that you get access to the Best Data Science Course with Gen AI, helping you build a strong career foundation in one of the fastest-growing domains.
Final Words
Data is the new oil, and Artificial Intelligence is the future. By mastering both, you can stay ahead in your career. Enrolling in the Advanced Data Science Course with Gen AI at Brillica Services will not only give you practical skills but also empower you to innovate with next-gen technologies.
If you want to build a future-proof career, this is the right time to take action. Join the Best Data Science Course with Gen AI and step into a world full of opportunities, growth, and success.
FAQs
Advanced Data Science Online Course with Gen AI: FAQs
It is a 12-month comprehensive online program that combines core Data Science skills — like Python, Machine Learning, SQL, and Deep Learning — with cutting-edge Generative AI technologies such as LLMs, ChatGPT, and prompt engineering. It is designed to make you job-ready with hands-on projects and real-world tools.
This course is open to students who have completed 12th or graduation, working professionals from IT, analytics, finance, or business backgrounds looking to upskill, entrepreneurs who want to leverage AI for business growth, and anyone passionate about building a career in Data Science and AI.
No prior coding experience is required. The course starts from Python fundamentals and builds up progressively, so both beginners and those with some experience can join comfortably.
The course duration is 12 months and it is delivered in online mode. LMS (Learning Management System) access is provided for 1+ year, so you can revisit all content at your convenience.
You will get hands-on practice with Python, SQL, NumPy, Pandas, TensorFlow, OpenCV, GitHub, LLMs, Gen AI platforms, Power BI, PyTorch, Keras, Scikit-learn, OpenAI, Hugging Face, and Google AI tools — 13+ industry-standard tools in total.
Start your Advanced Data Science Online Course with Gen AI journey
Speak with our counsellor today to explore course fees and secure your admission with no-cost EMI.
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