A career-focused program that takes you from Python fundamentals to production-grade machine learning and generative AI applications. Statistics, modelling, experimentation and MLOps taught through hands-on learning, industry case studies and real client projects — then a 3-month industry internship to turn it into experience.
The Data Science with Generative AI Program is a 12-Month Career Program built for students, final-year candidates and freshers who want a genuine data science role — not a certificate that says they watched some videos.
You start at the fundamentals: Python, data wrangling with pandas, and the statistics that make the difference between a model that works and a model that got lucky. From there you move through the full machine learning arc — regression, classification, ensembles, unsupervised learning — then into deep learning, natural language processing and computer vision.
The generative AI layer is not an afterthought bolted onto the end. Large language models, embeddings, retrieval-augmented generation and evaluation run as a full track in their own right, because that's where a growing share of data science hiring now sits. You'll build and evaluate GenAI applications, not just call an API once in a demo notebook.
Every module produces something for your portfolio. By month nine you'll have a reviewed GitHub profile, deployed models and a capstone project. Then comes the 3-month industry internship on real client projects under industry mentorship — followed by placement preparation, mock interviews and 100% job assistance until you're hired.
One continuous career-focused track. You don't finish a course and then go hunting for experience — the industry internship is built into the program.
Live instructor-led sessions covering Python, statistics, machine learning, deep learning, NLP, computer vision, generative AI and MLOps — each module closing with a graded notebook, model or deployed application.
You join a delivery team on live client data problems — scoping, building, evaluating and deploying models — with weekly mentor code reviews and stakeholder demos.
Three connected tracks: the analytics and statistics foundation, the machine learning core, and the generative AI stack that now sits on top of both.
Practical learning throughout: each module is anchored to a real business scenario and ends in a deliverable. Every tool is taught the way it's used on the job — not in isolation.
Build the programming foundation everything else in the program rests on — written the way data scientists actually write it.
Most real data doesn't arrive as a tidy CSV. Learn to go get it.
The layer that separates a data scientist from someone who can call scikit-learn.
Learn to interrogate a dataset until it tells you what it's hiding.
The workhorse models that still solve the majority of real business problems.
Finding structure when nobody has labelled anything for you.
Neural networks from the mechanics up — so you can debug them, not just run them.
The two application domains that dominate deep learning hiring.
The track that makes this program current rather than historical.
Making a GenAI system you'd actually put in front of a customer.
A model on your laptop is a hobby. A model in production is a job.
End-to-end delivery on a real client problem, under industry mentor supervision.
Concrete, demonstrable capabilities built through practical learning — the kind you can show in a portfolio and defend in an interview.
Structure, test and ship data science code that another engineer can read, run and extend — not just notebook spaghetti.
Turn a vague business question into a measurable hypothesis, choose the right test, and know when a result is noise.
Train, tune and validate models with honest evaluation — catching leakage, imbalance and overfitting before a stakeholder does.
Design RAG pipelines, engineer context, fine-tune where it's justified, and evaluate LLM output rather than trusting it.
Containerise a model, serve it behind an API, track experiments and watch for drift once it's live.
Walk a hiring panel through a real deployed project — the trade-offs, the failures and what you'd do differently.
The final 3 months are an industry internship on live client data problems — the experience that separates a hireable fresher from a hopeful one.
You're assigned live briefs from Ergebins Technologies' delivery pipeline and partner organisations — real data, real constraints, real stakeholders.
A practising data scientist reviews your code, your modelling choices and your evaluation weekly, the way a senior would in a real team.
On completion you receive an Internship Certificate and a Project Experience Letter — verifiable experience for your résumé and background checks.
Portfolio development is built into the program. You leave with work you can walk a hiring manager through, line by line.
Build an end-to-end churn model on subscription data, quantify the revenue at risk, and translate model output into a retention plan leadership can act on.
Model seasonal demand across product categories, benchmark statistical against ML forecasts, and quantify the stock-out cost you remove.
Build a retrieval-augmented generation system over a large document corpus, with chunking strategy, vector search, citation grounding and an evaluation harness.
Apply transfer learning to a diagnostic imaging dataset, handle severe class imbalance, and report performance with clinically meaningful metrics.
Ship a production GenAI application end to end: API service, containerised deployment, monitoring, guardrails and an evaluation report.
Designed for students and freshers. You need curiosity and consistency far more than you need a prior computer science degree.
Certificates are awarded on assessed performance — completed modules, reviewed projects and a finished industry internship.
Awarded on assessed completion of all twelve modules, graded assignments and your capstone project — issued by DICA, the education brand of Ergebins Technologies Private Limited.
Issued on completion of the 3-month industry internship, confirming the client engagement, your role and the duration of the placement.
A verifiable letter detailing the live client projects you delivered, the stack you used and the outcomes achieved.
Common roles our graduates step into. Final compensation depends on background, performance and location.
Frame business problems statistically, build and validate models, and turn model output into decisions the business acts on.
₹8 – 18 LPATake models from notebook to production — services, pipelines, monitoring and retraining.
₹8 – 20 LPAOwn the analytical layer: extract, model and visualise data, and answer the questions leadership hasn't asked yet.
₹5 – 12 LPADesign and evaluate LLM applications — RAG systems, prompt pipelines, fine-tuned models and guardrails.
₹10 – 24 LPABuild the semantic models and reporting infrastructure an organisation runs its decisions on.
₹6 – 14 LPASupport applied research teams on experimentation, benchmarking and model evaluation.
₹9 – 20 LPAPlacement preparation starts in month seven, runs through your internship, and continues until you accept an offer.
One-on-one resume rewrites tuned to the roles you're targeting, plus a reviewed portfolio of your live projects, dashboards and capstone work.
HR, aptitude and technical mock interviews with recorded feedback. You rehearse the real rounds before you sit in them.
Business communication, stakeholder storytelling, group discussions and presentation practice — taught to corporate training standards.
Profile shortlisting, referrals into our hiring partner network, interview scheduling and continued career support until you accept an offer.
Placement preparation, mock interviews, and referrals into a recruiter network that hires for analytics, data science, and AI roles.
Company names and marks belong to their respective owners.
Outcomes from recent cohorts — live projects, internships and offers.
I joined in my final year with zero programming background. The Python and statistics modules were slow enough to actually land, and by the internship I was writing model evaluation reports for a live client.
The RAG and LLM evaluation modules are what got me shortlisted. Every interviewer asked about the capstone — a deployed GenAI assistant with a real evaluation harness.
Mentor code reviews changed how I write. I came in producing notebooks nobody could run and left with a GitHub profile a hiring manager actually read through.
The program is 9 months of learning + 3 months of industry internship = 12 months in total.
No. Module one starts at core Python with no assumed background. What you do need is consistency — roughly 12 to 15 hours a week, every week, for nine months.
School-level mathematics is enough to start. Statistics, probability and the linear algebra you need for deep learning are rebuilt from fundamentals inside the program, always tied to a practical use rather than taught in the abstract.
Yes. The 3-month industry internship is included in the 12-Month Career Program at no additional cost. You work on real client projects and receive an Internship Certificate and Project Experience Letter on completion.
Three things: generative AI is a full track rather than a bonus module; deployment and MLOps are taught so your work leaves the notebook; and the program ends inside a real delivery team rather than at a final exam.
Resume building, LinkedIn and portfolio development, GitHub review, mock interviews across HR, aptitude and technical rounds, soft skills training, hiring drive access, and referrals into our hiring partner network — including Technify, Infosys, LTIMindtree, EY, Capgemini, Grail Consultant, Intellicia and Thread Security.
Yes, many students do. Live sessions are scheduled outside standard college hours and every class is recorded. Talk to a counsellor about aligning the internship phase with your academic calendar.
Yes. We accept UPI, credit and debit cards, Visa and Mastercard, and offer No Cost EMI on selected cards and partner lenders, subject to eligibility and approval.
Share your details and a career advisor will call you back within one working day with the full curriculum, batch dates, fee structure and No Cost EMI options.
Book a free 15-minute career counseling session and reserve your seat before applications close.