10,000+Skillsbiz learners trained
Data Science & AI · Enrolments open
Build data science and AI systems you can explain, evaluate and deploy.
Develop strong foundations in Python, mathematics and SQL before progressing through machine learning, deep learning, computer vision, NLP and MLOps. Learn to justify modelling choices, analyse errors and carry a model beyond the notebook.
✓Live online✓Weekly 1:1 mentorship✓Maximum 20 learners
Program fee
₹1,40,000+ GST · EMI options availableApplied learning
8 projectsincluding a final capstoneProgram duration
12 months
The complete journey includes teaching, practice, assessments, projects and career preparation.
Structured curriculum
36 weeks · 8 modules
Python and mathematics through machine learning, deep learning and MLOps.
Applied learning
8 projects
Realistic Indian business contexts using public or synthetic datasets.
Program fee
₹1,40,000 + GST
Pay through up to 12 zero-interest EMIs, subject to final provider terms.
4.8/5Skillsbiz learner rating
8Projects in every program
20Maximum learners per cohort
Small live cohorts
Every cohort is limited to 20 learners to support interaction, questions and individual attention.
Flexible batch options
Choose from monthly weekday or weekend cohorts delivered live online.
Eight portfolio projects
Apply the curriculum through realistic briefs, documented case studies and a final capstone.
Weekly 1:1 mentorship
Review concepts, project decisions and your learning progress in recurring individual sessions.
90-day career support
Prepare your resume, LinkedIn profile, portfolio and interviews with structured support after completion.
Long-term learning access
Keep lifetime access to learning materials and revisit class recordings for 12 months after completion.
From enrolment to career preparation
A structured path from foundations to portfolio-ready work.
The pace varies by program, but every pathway follows the same sequence: establish direction, build core skills, complete reviewed work and prepare to present it professionally.
Program start
01Establish your direction
Assess & Plan
Review your starting point, target roles and program expectations before beginning the core curriculum.
BaselineRole directionLearning plan
Core curriculum
02Build the fundamentals
Learn & Practise
Develop technical and analytical fluency through live classes, guided exercises and regular mentor reviews.
Live classesGuided practiceMentor reviews
Project phase
03Apply what you learn
Build & Review
Complete eight realistic projects, strengthen them through feedback and document the clearest work for your portfolio.
Case studiesWritten feedbackPortfolio
Final phase + 90 days
04Prepare for opportunities
Present & Apply
Refine your resume and LinkedIn profile, complete three mock interview rounds and receive 90 days of placement assistance.
ATS resumeMock interviewsPlacement support
Who this program is for
Choose this program when prediction and modelling are central to the work.
Data Science & AI is the most technically demanding program in the School’s portfolio. It suits learners prepared to spend meaningful time coding, revisiting mathematics, debugging experiments and reasoning about why a model succeeds or fails.
Data Science or Data Analytics?
If your target work is primarily dashboards, reporting and business insight, Data Analytics + Gen AI is the more direct route. Choose this program when machine learning, deep learning or AI-system development is central to the role you are working towards.
Students and recent graduates
Connect mathematics, statistics and code to practical machine-learning workflows and reviewed portfolio projects.
Technical career startEngineers and developers
Build on existing programming confidence with modelling, evaluation, deep learning and deployment-oriented work.
Technical upskillAnalysts and working professionals
Progress from descriptive analysis into prediction, model validation and more technical ownership of data products.
Analyst to modellerCareer switchers ready for sustained practice
Follow a structured technical path with repeated coding, experiments, project iteration and technical explanation.
Career transitionTechnical outcomes
Develop the judgement to build and defend a complete modelling workflow.
Learn to reason clearly about data, assumptions, baselines, validation and failure modes—not simply train algorithms. Carry that discipline through to deployment and monitoring.
01 / FRAME
Translate a vague problem into a modelling task.
Decide what should be predicted, what information is available and which outcome actually matters.
Problem formulation
02 / BASELINE
Build evidence before chasing complexity.
Create sensible baselines, choose evaluation metrics and separate genuine improvement from noise.
Scientific discipline
03 / DIAGNOSE
Understand where a model breaks.
Use error analysis, validation and segment-level investigation to identify weak cases and hidden assumptions.
Model judgement
04 / SHIP
Think beyond the notebook.
Package, expose and monitor models with reproducibility, versioning and operational behaviour in mind.
Production thinking
Interactive model lab
Different problems demand different evidence.
Explore four common problem families. The point is not to memorise algorithms; it is to recognise how the target, data shape, evaluation and failure analysis change.
01 Define the target before the model.02 Pick metrics that reflect the cost of mistakes.03 Inspect errors, not only averages.
Problem family
Build the programming and mathematical language needed to reason about machine-learning systems rather than treating models as black boxes.
Key topics
Advanced PythonLinear algebraProbability & statisticsCalculus for machine learning
Applied work: explain a modelling problem in code and mathematics, including assumptions and validation checks.
Work confidently with structured data before modelling begins: query, combine, inspect and prepare information for reproducible analysis.
Key topics
SQLWindow functionsCommon table expressionspandasExploratory data analysisData preparation
Applied work: turn a relational dataset into a documented, feature-ready analytical table and explain the data decisions.
Learn the supervised and unsupervised modelling workflow from problem framing and baselines through evaluation and interpretation.
Key topics
Supervised learningUnsupervised learningFeature engineeringModel evaluation
Applied work: establish a baseline, compare multiple models and defend the evaluation metric selected.
Move beyond foundational models into advanced methods for stronger prediction, temporal analysis and personalised recommendations.
Key topics
Ensemble methodsSupport vector machinesTime-series analysisRecommendation systems
Applied work: improve a baseline while documenting why each modelling decision is justified and where overfitting risk appears.
Develop practical neural-network intuition and learn how deeper architectures are trained, debugged and evaluated.
Key topics
Neural networksConvolutional neural networksRecurrent neural networksTransfer learning
Applied work: train, diagnose and compare neural architectures while tracking errors rather than only final accuracy.
Apply modern learning systems to unstructured image and text problems, with attention to representation, evaluation and failure modes.
Key topics
Image classificationObject detectionText classificationNamed-entity recognition
Applied work: build a vision or language prototype and create an error-analysis report around its weakest cases.
Treat a model as a software system: package it, expose it, version it and plan for what happens after it leaves the notebook.
Key topics
Flask & FastAPIDockerMLOps foundationsModel monitoring
Applied work: move a trained model into a simple service with versioned artefacts, reproducible setup and monitoring considerations.
Bring the full workflow together in a substantial project that can be explained technically, defended critically and presented clearly.
Key topics
Industry-style capstonePortfolio developmentInterview preparationSystem-design practice
Applied work: present problem framing, data choices, modelling trade-offs, validation, limitations and deployment thinking as one coherent case.
01Dataquality · lineage
→
02Modeltrain · validate
→
03ServeAPI · package
→
04Watchdrift · errors
Systems thinking
A useful model is a living system, not a notebook screenshot.
Deployment changes the questions. Inputs evolve, data pipelines break, behaviour drifts and users interact with outputs in unexpected ways. The MLOps module exists so model quality is connected to reproducibility and operating reality.
ReproducibilityCan another person rebuild the same result?
VersioningCan you identify which data, code and model produced an output?
MonitoringWould you notice if model behaviour changed after release?
CommunicationCan stakeholders understand limitations and safe use?
Eight portfolio projects
Build evidence across different kinds of data science and AI work.
Work with public or synthetic datasets in realistic Indian business contexts. Each project develops a different part of the modelling workflow, culminating in an end-to-end capstone.
Risk modelling · 01
Credit Decision Risk Model
Frame a binary-risk problem with attention to leakage, class balance, calibration and the business cost of different mistakes.
Review lens: Is the model actually better than a simple baseline, and is the chosen threshold defensible?
PythonXGBoostMLflow
Recommendation · 02
Personalised Product Ranking
Build a recommendation prototype that separates candidate generation, ranking logic and offline evaluation from surface-level “AI personalisation”.
Review lens: What does your offline metric fail to tell you about user experience?
PythonScikit-learnFastAPI
Forecasting · 03
Multi-series Demand Forecast
Compare naive, statistical and ML approaches across multiple product or location series while handling seasonality and backtesting correctly.
Review lens: Which segments improve, which regress, and why is one average metric insufficient?
PythonPandasStatsmodels
NLP · 04
Support Ticket Routing System
Classify and route incoming text requests, then investigate ambiguity, rare labels and language patterns behind incorrect predictions.
Review lens: Which errors create the highest operational cost and where should a human stay in the loop?
Hugging FacePythonFastAPI
Computer vision · 05
Visual Quality Inspection Prototype
Train an image classifier or detector for product-quality checks and document class imbalance, augmentation choices and difficult visual cases.
Review lens: How does performance change on lighting, angle or rare-defect conditions?
PyTorchOpenCVTensorFlow
Anomaly detection · 06
Transaction Anomaly Detection
Identify unusual transaction patterns in an imbalanced dataset while comparing supervised and unsupervised approaches.
Review lens: Does the evaluation reflect the operational cost of missed anomalies and unnecessary investigations?
PythonScikit-learnXGBoost
MLOps · 07
Model Service & Monitoring
Package a trained model behind an API, track artefacts and design a monitoring plan for data quality, latency and model behaviour.
Review lens: Could another developer reproduce, deploy and diagnose this system without your notebook?
DockerMLflowFastAPI
Capstone · 08
End-to-End Data Science System
Frame a substantial problem, prepare data, compare models, analyse errors and present a deployment and monitoring approach.
Review lens: Are the modelling choices, limitations and system behaviour documented well enough to defend in a technical review?
PythonDockerMLflow
Technical toolkit
Build practical fluency across the modern data science workflow.
Core work uses Python, TensorFlow, PyTorch, Scikit-learn, Keras, OpenCV, NLTK and Docker. Supporting tools are introduced where they serve a specific task in data preparation, modelling, deployment or monitoring.
Python
NumPy
Pandas
Jupyter
Scikit-learn
TensorFlow
PyTorch
PostgreSQL
GitHub
AWS
Google Cloud
Hugging FaceXGBoost
SciPy
Keras
OpenCVNLTK
MLflow
FastAPI
Docker
Airflow
SparkLearning & career support
Structured support throughout and beyond the program.
Receive individual guidance while you learn, written feedback on technical work and practical career preparation as you begin presenting your projects to employers.
Live learning support
Stay connected to the curriculum through live teaching, recordings and long-term access to learning support.
Weekday and weekend live batchesRecordings for 12 months after completionLifetime learning-material accessFive years of structured doubt support
Mentorship & project review
Work directly with a mentor to strengthen your code, modelling choices, experiments and portfolio presentation.
Weekly one-to-one mentorshipWritten feedback on portfolio workReviewed capstone and technical projectsRepeat-batch access for up to five years
Career preparation
Prepare to discuss technical foundations, modelling decisions and project work clearly during applications and interviews.
Resume, LinkedIn and portfolio guidanceThree mock interview roundsOne-year internship pathway and LOR90 days of active placement assistance
Career directions
Roles this technical foundation can support.
These are role directions, not job guarantees or salary promises. Actual eligibility depends on your prior background, depth of project work, interview performance and the requirements of each employer.
Data Scientist
Frame predictive problems, build and evaluate models, analyse errors and communicate model behaviour to technical and business stakeholders.
Machine Learning Engineer
Move closer to software and systems concerns around training pipelines, model serving, reproducibility and deployment.
AI Developer
Use machine-learning and deep-learning components inside applications while building stronger judgement around model limitations.
Applied ML / Research Associate
Support experimentation-heavy teams where careful modelling, literature-informed approaches and rigorous evaluation matter.
DSSchool of Data Science · Skillsbiz Education
Certificate of Completion
Data Science & AI
Issued and digitally verifiable by Skillsbiz Education after the program completion requirements have been met.
Skillsbiz Education certificate
Receive a digitally verifiable program certificate.
After meeting the completion requirements, you will receive a Certificate of Completion in Data Science & AI, issued and digitally verifiable by Skillsbiz Education.
✓Records the specific program completed.✓Issued by Skillsbiz Education.✓Verifiable through the official Skillsbiz certificate portal.
Certificate verification: Use the official Skillsbiz Education portal to verify certificates issued by the company. Open verification portal ↗
Program FAQs
Questions before choosing Data Science & AI.
Review the starting requirements, duration, fees, learning format and career support before you apply.
Compare all four programsYou do not need to arrive as a mathematician, but you should be willing to work carefully through linear algebra, probability, statistics and calculus for machine learning. Basic coding familiarity is helpful; admissions can assess your starting point and recommend any preparation.
The program runs across 12 months, with 36 weeks of structured curriculum delivered through eight sequential modules. The wider journey includes scheduled teaching, guided practice, assessments, eight projects, capstone work and career preparation.
Data Analytics is centred on querying, analysis, reporting, visualisation and business communication. Data Science & AI goes substantially deeper into mathematical foundations, predictive modelling, deep learning, unstructured data and MLOps.
Generative AI is a focused LLM-builder path around prompting, LLM application development, retrieval and advanced GenAI systems. Data Science & AI is broader across classical machine learning, deep learning, computer vision, NLP and deployment. They serve different goals.
You will complete eight projects spanning risk modelling, recommendation systems, forecasting, NLP, computer vision, anomaly detection, MLOps and an end-to-end capstone. Projects use public or synthetic datasets in realistic Indian business contexts.
The program fee is ₹1,40,000 + GST, with up to 12 zero-interest EMIs available. New live online cohorts begin monthly, with weekday and weekend batch options. Admissions will confirm the current timetable and payment-provider terms before enrolment.
Career support includes resume, LinkedIn and portfolio guidance, three mock interview rounds, job-portal access, referrals and placement drives, followed by 90 days of active placement assistance. These services support your job search but do not guarantee employment.
Yes. After meeting the completion requirements, you will receive a Certificate of Completion in Data Science & AI, issued and digitally verifiable by Skillsbiz Education.
Ready to build advanced data science and AI capability?
Apply now, book a free counselling session or request the detailed brochure to review the curriculum, projects, fee and next available batches.
Speak with admissions
Tell us about your technical background and target role.
An admissions advisor will help you assess program fit, prerequisites, batch options and payment terms. Expect a response within 12 hours during working hours: 10:00 AM–6:00 PM, Monday–Saturday.
Secure enquiry: your details are sent directly to Skillsbiz Education admissions for follow-up.