Build a model you can evaluate.
Build a model and compare it with a simple baseline. Put the demo and test results in your portfolio so someone can inspect your work.
NYC Accelerator
Data Science & Machine Learning
Take working ML projects into your internship interviews.
Build models, test their predictions and create demos you can show. Get mentor feedback and learn to explain your data, model choices and results.
Your instructors
Taught by engineers who got
the job you want.
They teach your live classes, review your projects and run your mock interviews.
How hiring works now
Interviews have changed.
Many companies now run two rounds: one without AI and one with it. NYC trains you for both.
No AI
Classic round
123456
- Statistics and ML questions, a timer, no AI
- Tests whether you understand the model
Who fails here: people who only ran notebooks.
AI allowed
AI-assisted round
1
X = df.drop("churn", axis=1)2
y = df["churn"]3
model.fit(X, y)4
print(model.score(X, y))AIModel trained. Accuracy 0.98 on the data.
YouThat is the training set. Where is the test set?
- A pipeline you have never seen, AI open
- Tests whether you catch what the AI got wrong
Who fails here: people who only followed tutorials.
At NYC, tests run without AI and projects run with it, so you practise both rounds.
What you’ll be able to show
Bring a working model and the evidence behind it.
Show what your model predicts, how well it performs and where it fails. Answer the questions behind the accuracy score.
Code someone else can run
Document your data preparation and training so the experiment can be repeated.
Results you can explain
Test on unseen data and show the errors alongside the scores.
A model people can try
Let someone make a prediction and inspect what the model can and cannot do.
Duration6 months live + 6 months of recordings
Live classes5 a week, 1.5 hours each
Your toolsPython · SQL · Statistics · ML
Learn withMentors + teaching assistants
You build the work through assignments and projects. NYC gives you live classes, mentor feedback and teaching-assistant support.
Practise the decisions behind the project
It predicts a number.
Is it any good?
Practise a question you should be able to answer about your project: does this model beat a simple baseline?
Illustrative test set · 4 example days
Keep the answers out of training.
Actual demand: 100, 110, 90, 120 units
4 daysIllustrative unseen observations
These four days demonstrate evaluation. A real project needs a larger, representative test set and time-aware splits.
Interactive learning example. Illustrative data and outputs; not student performance or a live AI service.
Your portfolio starts here
Give an interviewer a model worth discussing.
Develop projects you can demo, explain and add to internship applications. These briefs show the kind of work you’ll practise.
Forecast demand with a tested model.
Compare predictions with actual results and use time-aware validation to assess a forecasting model.
Illustrative project brief
Demand forecasting
Time series + baseline + evaluation report
Predict customer churn and inspect the errors.
Train a classifier, compare it with a baseline and explain the cost of false positives and missed cases.
Illustrative project brief
Customer churn
Classification + error analysis + model demo
Illustrative project briefs. Your course assignments and datasets are included in the batch plan.
How NYC helps you get there
Get feedback on your models and results.
Get help with the part you cannot solve. Get feedback on the decisions you made. Apply it to your next version.
Know how to start your own project.
Watch an instructor solve the problem and explain each decision. Then use the same approach in your own project.
Get past the error holding you up.
Bring your code and the error to a teaching assistant. Work out what went wrong and understand how to fix it yourself.
Get your project reviewed by an engineer.
Demo your project and explain your choices to a mentor. Find the gaps in your work and improve them before you put it in your portfolio.
A real NYC software project review
See what an engineer checks in a project.
Watch how NYC reviews a software project. Your course reviews focus on your experiments, evaluation and modelling decisions.
Learning scenes are illustrative. Your batch plan covers mentors, project reviews and support hours.
AI, with your judgement
Use AI to build faster. Explain the model yourself.
Use AI to draft code and investigate errors. Check the experiment yourself so you can explain every result you put in your portfolio.
AI-assisted codingExperiment designLeakage checksReproducibility
Use AI to support your experiments.
Draft code and explore approaches faster, while keeping validation, reproducibility and evaluation in your hands.
Who teaches you
Working engineers, not full-time trainers.
They work in tech today, so you learn what teams use now.
Data science and machine learning
Abhishek
Engineering Analyst, Google
IIT Kanpur. Earlier a data scientist at Optum and a scientist at BARC.
Instructors teach NYC live classes and review student projects. They speak for themselves, not for their employers.
What you’ll learn
The skills behind your ML portfolio.
26 weeks of live classes, from data foundations to machine learning in production. Open a phase to see every week.
01 / Data foundationsWeeks 1–7 · SQL · Statistics · Python
01Data thinking, spreadsheets and SQLWeeks 1–2 · Excel · Sheets · Queries
Data thinking, spreadsheets and SQL
- Week 1 · Data thinking, Excel and Sheets: questions, formulas, cleaning, pivot tables
- Week 2 · SQL I: querying, aggregating and joining data
What you can do after thisTurn a vague question into a measurable one and answer it with a spreadsheet or SQL.
02Advanced SQL and statisticsWeeks 3–4 · Window functions · Uncertainty · A/B tests
Advanced SQL and statistics
- Week 3 · SQL II: CTEs, window functions, cohorts and retention queries
- Week 4 · Statistics: describing data, sampling, confidence intervals and A/B tests
What you can do after thisWrite the analytical SQL used in real reporting and reason about uncertainty.
03Python and analysisWeeks 5–7 · pandas · Cleaning · Visualisation
Python and analysis
- Week 5 · Python for data: NumPy and pandas
- Week 6 · Cleaning messy data and exploratory analysis
- Week 7 · Visualisation, product metrics and a short analysis project
What you can do after thisUse Python to clean, explore and present a dataset, and defend what you found.
02 / Data scienceWeeks 8–18 · Maths · Machine learning · Forecasting · NLP
04Framing and the mathsWeeks 8–9 · Framing · Linear algebra · Calculus · Inference
Framing and the maths
- Week 8 · Framing prediction problems, and linear algebra
- Week 9 · Calculus, optimisation and inference for data science
What you can do after thisFrame a machine-learning problem and understand the maths models rely on.
05Machine learningWeeks 10–13 · Regression · Trees · Features · Evaluation
Machine learning
- Week 10 · Linear and logistic models
- Week 11 · Trees and ensembles: random forests and gradient boosting
- Week 12 · Feature engineering and data leakage
- Week 13 · Model evaluation and experimentation
What you can do after thisBuild, tune and evaluate strong models on tabular data.
06More kinds of dataWeeks 14–16 · Clustering · Time series · NLP
More kinds of data
- Week 14 · Unsupervised learning: clustering and PCA
- Week 15 · Time series and forecasting
- Week 16 · Text data and NLP fundamentals
What you can do after thisPick the right method for segments, forecasts and text.
07Data science projectWeeks 17–18 · Reproducibility · Error analysis · Project
Data science project
- Week 17 · Reproducible data science and error analysis
- Week 18 · Data science project: frame, model, evaluate and defend
What you can do after thisData science work another person can inspect, reproduce and trust.
03 / Machine learning in productionWeeks 19–26 · Pipelines · Serving · MLOps · Final project
08ML software and servingWeeks 19–21 · Tested code · Pipelines · APIs · Docker
ML software and serving
- Week 19 · From notebooks to tested code; data and training pipelines
- Week 20 · Experiment tracking, and serving models behind APIs
- Week 21 · Docker, cloud and CI/CD for ML
What you can do after thisTurn experimental model code into a reliable service a team can maintain.
09Production MLWeeks 22–23 · Monitoring · MLOps · System design
Production ML
- Week 22 · Monitoring, data drift and MLOps: versioning, rollouts and retraining
- Week 23 · Scalable ML and ML system design
What you can do after thisRun, monitor and update models in production.
10Final ML projectWeeks 24–26 · Plan · Build · Deploy · Defend
Final ML project
- Week 24 · Plan: problem, data, metric and pipeline design
- Week 25 · Build, deploy and monitor
- Week 26 · A simulated failure, the final review and your defence
What you can do after thisA production ML system you can explain end to end.
Monday to Thursday: learn and practise. Friday: build, case, debug or review. Each live class is 1.5 hours.
Course fee
Your first internship starts with the work.
The course includes project work and learning support. Pay once, or in 12 monthly payments on EMI, with interest as low as 1% a month on the course fee. Add one month of internship preparation after the course, with a complimentary session with a MAANG engineer, for ₹15,000.
Data Science & Machine Learning
Your selected plan
₹6,720/monthfor 12 months · interest as low as 1% a month on the course fee
Less than one semester of private engineering college fees.
- Course fee
- ₹72,000
- Internship preparation + MAANG engineer session
- ₹15,000
- EMI interest (1% a month on the course fee)
- ₹8,640
- Total
- ₹80,640
Included in the course
- Course lessons and guided practice
- Projects for your portfolio
- Project feedback from mentors
- Teaching-assistant doubt support
- NYC learning community
Optional support
See everything includedRésumé reviews, referrals, timed test practice and mock interviews with engineersEMI is 12 monthly payments, with interest as low as 1% a month on the course fee. The add-on has no interest.
What if I do the work and still don’t finish a live project?
You join the next batch’s live classes at no extra fee, once. Doing the work means attending at least 85% of live classes, watching every class you miss within 7 days, submitting every weekly task, attempting the practice work, and building and presenting your final project. A live project runs at a public link, its code is on GitHub, and you can explain it in your final review. Ask within 30 days of your course ending. Read the full rules.
Who is Data Science & Machine Learning for?
College students preparing for data science and ML internships. Build from Python and statistics to projects with trained models, test results and demos you can explain.
Is the add-on optional?
Yes. The ₹15,000 add-on is one month of internship preparation after your live classes end, plus a complimentary 45-minute session with a MAANG engineer. Tick it in the fees section to see your new total, or join the course without it.
Can I start from the foundations?
You can start without prior machine-learning experience. The course builds Python, data handling and mathematical foundations before modelling. Expect substantial coding and quantitative practice.
Can I manage this alongside college?
Yes. Live classes are 1.5 hours, Monday to Friday, about 7.5 hours a week, plus project work. Fridays are build, case or review days. Missed a class? Watch the recording. You keep recordings for 6 months after live classes end.
What will I be able to show?
A modelling portfolio with runnable code, baseline comparisons, test results and a working demo. You build it through assignments, project reviews and practice. The project briefs above are examples.
How will I get help?
Teaching assistants help with code and concepts. Mentors review your project decisions so you know what to improve. Your batch plan includes the support channels and review schedule.
How much does this course cost?
The course fee is ₹72,000, or ₹6,720 a month for 12 months on EMI (interest as low as 1% a month on the course fee). The optional ₹15,000 add-on is one month of internship preparation after the course, with a complimentary session with a MAANG engineer (₹7,970 a month on EMI with the add-on). An advisor shares the complete fee breakdown before you enrol.
Does this course guarantee an internship?
NYC helps you build practical skills and a portfolio you can explain. Dedicated internship preparation is available as an optional add-on. Employment is not guaranteed; employers make hiring decisions.
Join NYC
Put your first ML project in your portfolio.
Develop models you can demo and explain in internship interviews. Get the Data Science & Machine Learning project plan, batch schedule and enrolment details.
On the call
01 / Your goals & starting point
02 / Curriculum, support & timetable
03 / Your plan, fees & joining