Show what a business should do next.
Investigate a business question, build the dashboard and present your recommendation. Show the evidence behind your answer.
NYC Accelerator
Data Analytics + GenAI
Get internship-ready with real business analytics projects.
Build an analytics portfolio with SQL, Python, dashboards and AI. Get feedback on your work and practise explaining the business decisions your analysis supports.
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
- SQL and a business question, a timer, no AI
- Tests how you reason with data
Who fails here: people who only know the buttons.
AI allowed
AI-assisted round
1
df = pd.read_csv("orders.csv")2
df = df.dropna()3
revenue = df.amount.sum()4
print(revenue)AICleaned the data and summed revenue. Done.
Youdropna() just deleted 30% of the orders.
- A messy dataset you have never seen, AI open
- Tests whether you catch what the AI got wrong
Who fails here: people who trust whatever the AI outputs.
At NYC, tests run without AI and projects run with it, so you practise both rounds.
What you’ll be able to show
Put business answers in your portfolio.
Show an interviewer how you found the problem, checked the numbers and reached a recommendation. Build the analysis yourself, from source data to dashboard.
Data you can trust
Show how you cleaned the data and checked for missing or incorrect values.
A dashboard you can demo
Make sales, customer behaviour or business performance easy to understand.
An answer you can defend
Explain what the business should do and point to the numbers behind it.
Duration4 months live + 6 months of recordings
Live classes5 a week, 1.5 hours each
Your toolsSQL · Python · Power BI · GenAI
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
Sales went up.
Did the business improve?
Practise looking beyond a headline number. Work out whether higher sales actually mean better performance.
Example dataset · not a student result
Revenue is up. What else changed?
January February
+25%Gross sales growth
Gross sales rose from ₹48,000 to ₹60,000. Before calling it a win, check the returns.
Interactive learning example. Illustrative data and outputs; not student performance or a live AI service.
Your portfolio starts here
Show how you solve business problems.
Develop projects you can demo, explain and add to internship applications. These briefs show the kind of work you’ll practise.
Find what drives business performance.
Analyse sales, products and returns. Build a dashboard that helps someone understand where growth comes from.
Illustrative project brief
Retail performance
SQL + dashboard + recommendation
Understand why customers return.
Compare customer cohorts, calculate retention and present the patterns in a clear analysis.
Illustrative project brief
Customer retention
Python + cohort analysis + insight report
Illustrative project briefs. Your course assignments and datasets are included in the batch plan.
How NYC helps you get there
Get your analysis reviewed before you present it.
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. In this course, feedback focuses on your data, calculations, dashboards and recommendations.
Learning scenes are illustrative. Your batch plan covers mentors, project reviews and support hours.
AI, with your judgement
Use AI to analyse faster. Know why the answer is right.
Use AI to draft SQL and Python, then check the results against your data. Explain the analysis yourself when someone questions a number.
Query draftingPython assistanceVerificationData privacy
Use AI without trusting the numbers blindly.
Draft queries and Python code with AI, then verify the calculations against the source data.
Who teaches you
Working engineers, not full-time trainers.
They work in tech today, so you learn what teams use now.
Data analytics and business questions
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 analytics portfolio.
17 weeks of live classes, from data thinking and SQL to dashboards, GenAI and a final project. Open a phase to see every week.
01 / Work with dataWeeks 1–9 · Spreadsheets · SQL · Statistics · Python
01Data thinking and spreadsheetsWeeks 1–2 · Data thinking · Excel · Sheets
Data thinking and spreadsheets
- Week 1 · Data thinking: turn vague questions into measurable ones
- Week 2 · Excel and Sheets for serious analysis: formulas, cleaning, pivot tables
What you can do after thisLook at a dataset as evidence for a question and analyse it cleanly in a spreadsheet.
02SQLWeeks 3–5 · Queries · Joins · Window functions
SQL
- Week 3 · SQL I: querying and aggregating data
- Week 4 · SQL II: joins, subqueries and CTEs
- Week 5 · SQL III: window functions, cohorts and retention queries
What you can do after thisWrite the analytical SQL used in real reporting and product analysis.
03StatisticsWeeks 6–7 · Distributions · Uncertainty · A/B tests
Statistics
- Week 6 · Statistics I: describing data, and when averages lie
- Week 7 · Statistics II: sampling, confidence intervals and A/B tests
What you can do after thisSummarise data correctly and reason about uncertainty.
04Python for dataWeeks 8–9 · Python · pandas · NumPy
Python for data
- Week 8 · Python for data analysis: NumPy and pandas
- Week 9 · Cleaning and transforming messy data
What you can do after thisUse Python to build a repeatable cleaning and analysis pipeline.
02 / Analyse, present and prove itWeeks 10–17 · EDA · Dashboards · GenAI · Final project
05Analysis, dashboards and communicationWeeks 10–13 · EDA · Product metrics · Power BI
Analysis, dashboards and communication
- Week 10 · Exploratory analysis and visualisation
- Week 11 · Business and product analytics: funnels, cohorts, retention
- Week 12 · BI and dashboards: data models and measures
- Week 13 · Presenting analysis to stakeholders
What you can do after thisBuild dashboards that support decisions and present findings people trust.
06Generative AI for analyticsWeeks 14–16 · LLMs · Text-to-SQL · Data assistants
Generative AI for analytics
- Week 14 · LLMs for analysis: prompting for SQL and pandas, and checking what AI gets wrong
- Week 15 · AI-assisted analysis: text-to-SQL, summarising results and automated reports
- Week 16 · A data assistant: ask questions of your own data, with sources, and test its answers
What you can do after thisUse GenAI to analyse faster, and check every answer it gives.
07Analytics projectWeek 17 · Messy data · Dashboard · Recommendation
Analytics project
- Week 17 · A messy multi-source business problem: clean it, analyse it, build a dashboard and defend your recommendation
What you can do after thisAn analysis you can explain and defend in an interview.
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 Analytics + GenAI
Your selected plan
₹5,600/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
- ₹60,000
- Internship preparation + MAANG engineer session
- ₹15,000
- EMI interest (1% a month on the course fee)
- ₹7,200
- Total
- ₹67,200
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 Analytics + GenAI for?
College students preparing for analytics internships. Start with the foundations and build a portfolio of analysis, dashboards and recommendations with mentor feedback.
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?
No prior coding is assumed. Start with data and spreadsheet fundamentals, then build up to SQL and Python. You will still need consistent practice outside class.
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?
An analytics portfolio with prepared datasets, queries, dashboards and recommendations you can explain. You develop 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 ₹60,000, or ₹5,600 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 (₹6,850 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
Build the work for your first analytics internship.
Start building analysis you can show in internship applications. Get the Data Analytics + GenAI project plan, batch schedule and enrolment details.
On the call
01 / Your goals & starting point
02 / Curriculum, support & timetable
03 / Your plan, fees & joining