4.9★
AI-InfusedAdvanced Data Science Online Course with Gen AI
12 MonthsOnline
Next batch 16 Nov 2026
Free LMS · 1 yrPlacement supportNo-cost EMI
New ArrivalAI-InfusedAdvanced Data Science
Data Science with Agentic AI online course
Brillica Services offers a comprehensive Data Science with Agentic AI Course designed to help learners develop skills in data analysis, machine learning, Generative AI, and autonomous AI systems. This industry-aligned programme combines foundational concepts with practical coding, real-world case studies, and portfolio projects. Whether you are a student, fresher, working professional, or aspiring data scientist, the course provides a structured learning path to prepare for emerging data science and AI career opportunities.
Duration15 Months
ModeOnline
Next batch16 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
15 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, Gen AI and Agentic 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
- LLM evaluation, guardrails and responsible AI
- LangSmith / Langfuse, FastAPI, Docker and vLLM serving
- Chatbots vs workflows vs autonomous agents
- Agent anatomy and reasoning patterns (ReAct, Plan-and-Execute, Reflection)
- Tool and function calling; custom tools
- An agent from scratch in pure Python
- LangChain agents and LangGraph (routing, loops, human-in-the-loop)
- Short- and long-term memory, Mem0 and Agentic RAG
- Model Context Protocol (MCP) servers and clients; A2A protocol
- Supervisor, hierarchical and swarm patterns
- CrewAI, AutoGen (AG2), OpenAI Agents SDK and Google ADK
- Data-analysis, research, browser and coding agents
- n8n, Langflow and Flowise
- Agent evaluation, observability and guardrails
- Deploying agents with FastAPI
Tools & technologies
Master Industry Tools Through Hands-On Practice
Python
NumPy
Pandas
SQL
Matplotlib
TensorFlow
Scikit Learn
GitHub
Gemini
OpenAI
Anthropic
FastAPI
Docker
LangChain
LangGraph
PyTorchProjects
Build Real Projects. Showcase Your Skills
Project 01
E-commerce Data Analytics
Analyze sales data, customer behaviour, and product performance to identify business trends and actionable insights.
Project 02
Customer Segmentation
Use machine learning clustering techniques to group customers based on purchasing patterns and behaviour.
Project 03
Fraud Detection System
Build predictive models to identify suspicious transactions and detect potentially fraudulent activities.
Project 04
AI Document Assistant (RAG)
Develop an AI-powered assistant that answers questions using uploaded documents and Retrieval-Augmented Generation.
Project 05
Multi-Agent Workflow Automation
Create collaborative AI agents that coordinate tasks, generate content, and automate multi-step workflows.
Project 06
Natural Language to SQL Analyst
Build an AI application that converts natural-language questions into SQL queries and summarizes database results.
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 ScientistGenAI EngineerAgentic AI DeveloperML EngineerAI EngineerAI Automation 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
Kumod SharmaData Science ConsultantEXP 4+ yrs
Alumni
Recently placed Brillica learners
Pankaj BhandariSr. Data Analyst
Placed at
Sahil SinhaData Breach Analyst
Placed at
Kanishka GuptaData Analyst
Placed at
Shreya SaranData Scientist
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 Data Science with Agentic AI online course
Our Data Science with Agentic AI training programme brings together traditional data science techniques and modern AI technologies. Learners begin with SQL, Python, data analysis, mathematics, and statistics before progressing to machine learning, deep learning, natural language processing, Generative AI, and Agentic AI.
The 15-month programme includes 13 modules across five learning phases, helping students progress from working with structured data to developing intelligent AI applications. Through guided coding, practical exercises, case studies, and GitHub portfolio projects, learners gain experience applying their knowledge to real-world problems.
What You Will Learn in Our Data Science with Agentic AI Course
The curriculum follows a step-by-step learning approach, starting with programming and data foundations and progressing towards intelligent AI systems.
Python Programming and Data Analysis
Learn Python programming fundamentals, data structures, functions, and object-oriented programming. Develop practical skills in SQL and MySQL for database management and querying. Explore NumPy and Pandas for data manipulation, cleaning, and exploratory data analysis. You will also learn to create visualisations using Matplotlib, Seaborn, and Plotly to communicate insights effectively.
Statistics, Machine Learning and Predictive Analytics
Build a foundation in mathematics, probability, and statistics for data science. Understand supervised and unsupervised machine learning, regression, classification, clustering, feature engineering, and model evaluation. Explore predictive analytics applications such as fraud detection, customer churn prediction, loan default risk analysis, demand forecasting, and recommendation systems.
Generative AI, LLMs and AI Agents
Understand Generative AI, large language models (LLMs), prompt engineering, embeddings, and Retrieval-Augmented Generation (RAG). Learn how to work with language model APIs and develop applications such as document assistants, support chatbots, resume screeners, and summarisation tools. Progress towards Agentic AI concepts, including reasoning patterns, tool calling, memory, and autonomous task execution.
Agentic AI Frameworks and Workflow Automation
Learn how AI agents interact with tools, follow multi-step workflows, and coordinate tasks. Explore frameworks and technologies such as LangChain, LangGraph, CrewAI, AutoGen, and the OpenAI Agents SDK. Understand multi-agent systems, human-in-the-loop workflows, agent evaluation, guardrails, and deployment using FastAPI. The curriculum also introduces workflow tools such as n8n, Langflow, and Flowise.
Real-World Projects and Case Studies
Apply your knowledge through practical projects across data analytics, machine learning, deep learning, NLP, Generative AI, and Agentic AI. Build a GitHub portfolio module by module, with examples including customer segmentation, fraud detection, sentiment analysis, document-based AI assistants, and multi-agent content workflows.
Tools and Technologies Covered in Data Science with Agentic AI Training
The programme covers a broad range of tools used in data science and AI development. These include Python, MySQL, PostgreSQL, NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, XGBoost, TensorFlow, PyTorch, and Hugging Face Transformers.
For deployment and production workflows, learners explore FastAPI, Streamlit, MLflow, Docker, GitHub Actions, and cloud platforms such as AWS, Azure, and GCP. The Generative AI and Agentic AI modules include OpenAI, Anthropic, and Gemini APIs, LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, vector databases, and other relevant AI frameworks.
Why Choose Brillica Services for Data Science with Agentic AI Training?
Brillica Services focuses on practical learning, structured training, and career preparation. The programme combines concept explanations, live coding demonstrations, guided practice, and independent exercises to help learners understand how data science and AI techniques work in real applications.
Students work on portfolio projects throughout the curriculum and receive support with resume and portfolio building, mock interviews, soft-skills training, and interview preparation. Brillica also provides placement assistance and connects learners with hiring opportunities through its placement team. Successful course completion includes a Certificate of Completion from Brillica Services.
Data Science with Agentic AI Course Duration
The Data Science with Agentic AI course duration is 15 months. The programme consists of 13 modules organised into five phases, progressing from data foundations and mathematics to machine learning, deep learning, Generative AI, and Agentic AI. The curriculum specifies 450 class days, with two-hour sessions. The suggested learning schedule is five days per week, allowing learners to follow a structured progression through the course.
Data Science with Agentic AI Course Fees
The Data Science with Agentic AI course fees range from ₹80,000 to ₹1,10,000. The final fee may depend on the selected programme and applicable admission terms. Learners can contact Brillica Services to confirm the current fee, available payment options, and any applicable No Cost EMI facility. Before enrolling, ask the counselling team for a detailed fee breakdown, course syllabus, upcoming batch schedule, and information about the facilities and career support included in the programme.
Who Can Join Our Data Science with Agentic AI Course?
The programme is suitable for learners who want to build a foundation in data science and progress towards advanced AI applications.
Students and Freshers
Students and freshers interested in programming, analytics, machine learning, and artificial intelligence can explore this structured learning path.
Working Professionals
Professionals looking to upgrade their technical skills or explore career opportunities in data science and AI can learn through practical exercises and projects.
Data Analysts and Aspiring Data Scientists
Data analysts can strengthen their knowledge of predictive modelling, machine learning, Generative AI, and AI-powered workflows.
Developers Interested in AI
Python developers and software professionals interested in building LLM-powered applications and autonomous AI agents can explore the advanced modules.
Data Science with Agentic AI Course Eligibility
The course begins with foundational topics, including an introduction to data science, SQL, and Python programming. This progression can help learners build their knowledge step by step. An interest in mathematics, statistics, programming, and problem-solving is beneficial. However, the exact educational qualifications, prior programming requirements, and admission criteria should be confirmed with Brillica Services before enrolment.
Career Opportunities After Data Science with Agentic AI Course
Completing a Data Science and Agentic AI course can help learners prepare for several technology-focused career paths, depending on their practical skills, experience, portfolio, and employer requirements.
Data Scientist
Analyse datasets, identify patterns, build predictive models, and support data-driven business decisions.
Machine Learning Engineer
Develop, evaluate, and deploy machine learning models for applications such as forecasting, classification, and recommendation systems.
AI Engineer
Build AI-powered applications using machine learning, deep learning, and Generative AI technologies.
Agentic AI Developer
Develop AI agents that use tools, manage multi-step tasks, interact with data sources, and support automated workflows.
Data Analyst
Use SQL, Python, statistics, and data visualisation to analyse information and communicate actionable insights.
Other potential career paths include MLOps Engineer, NLP Engineer, and Generative AI Engineer, depending on the learner's specialisation and practical capabilities.
Projects Included in Data Science with Agentic AI Training
The programme includes portfolio projects across its 13 modules, with projects pushed to GitHub to help learners demonstrate their practical skills. Examples listed in the curriculum include e-commerce analytics, customer segmentation, fraud detection, demand forecasting, plant disease detection, sentiment analysis, and resume screening.
The Generative AI and Agentic AI modules include projects such as chat with documents using RAG, support chatbots, invoice extraction, research report agents, natural-language-to-SQL analysts, multi-agent content teams, and AI hiring assistants. These projects help learners explore how data science and AI can address practical business problems.
Placement Assistance After Data Science with Agentic AI Training
Brillica Services provides career preparation and placement assistance to help learners prepare for relevant opportunities. Support outlined in the programme includes resume and portfolio building, mock interviews, soft-skills sessions, interview preparation, and coordination with hiring partners. The placement team also supports learners through interview opportunities and career guidance. Actual hiring outcomes depend on individual skills, performance, experience, and employer requirements; placement assistance does not guarantee a job.
Enrol in the Data Science with Agentic AI Course at Brillica Services
Build your skills in data science, machine learning, Generative AI, and Agentic AI through a structured programme with practical projects and career preparation. Contact Brillica Services to enquire about the detailed syllabus, current course fees, upcoming batches, admission requirements, or a demo session. Take the next step towards developing industry-relevant data and AI skills.
FAQs
Data Science with Agentic AI online course: FAQs
A Data Science with Agentic AI course combines data analysis, statistics, machine learning, and AI technologies with systems that can use tools and perform multi-step tasks. It helps learners progress from analysing data to developing AI-powered applications and workflows.
The indicative course fee range provided for this programme is ₹80,000 to ₹1,10,000. Contact Brillica Services to confirm the current fee, payment options, and any applicable EMI facilities.
The programme begins with foundational topics such as SQL, Python, and data analysis before progressing to advanced AI concepts. Beginners can enquire about the admission criteria and recommended prerequisites to determine their suitability.
Data science focuses on extracting insights from data, identifying patterns, and building predictive models. Agentic AI focuses on systems that can use tools, make decisions within defined workflows, and perform multi-step tasks. Combining these fields can support more automated, data-driven applications.
The curriculum includes Python, MySQL, PostgreSQL, NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers. It also covers technologies such as LangChain, LangGraph, CrewAI, AutoGen, vector databases, LLM APIs, and deployment tools.
Potential career paths include Data Analyst, Data Scientist, Machine Learning Engineer, AI Engineer, Agentic AI Developer, MLOps Engineer, and NLP Engineer. Suitable roles depend on the learner's skills, projects, experience, and employer requirements.
The Data Science with Agentic AI course at Brillica Services has a duration of 15 months. It includes 13 modules covering data foundations, machine learning, deep learning, NLP, Generative AI, and Agentic AI.
Start your Data Science with Agentic AI online course journey
Speak with our counsellor today to explore course fees and secure your admission with no-cost EMI.
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