As you read this, you might already know a bit about me—Prabakaran Chandran, currently in my second semester of the MS in Data Science program at Columbia University. Before this, I spent about six and a half years in industry as a data scientist and machine learning engineer. That experience gives me a particular lens through which I evaluate what makes a "good" data science program—not from textbook ideals, but from understanding the actual gaps in the field, the real needs of data scientists and ML engineers, and how academic programs can genuinely prepare people for what lies ahead.
I want to share an honest, nuanced perspective about Columbia's Data Science program. Not the Reddit hot takes, not the LinkedIn hype, but a grounded view of what this program actually offers and who it might serve well.
The Two Meanings of "Best"
When people ask if Columbia Data Science is "one of the best programs," I think about two dimensions. There's the ideal—what a program offers conceptually, theoretically, in terms of potential. And then there's the pragmatic—which is deeply subjective and depends entirely on how an individual navigates that potential.
What I can speak to is the potential. What you do with it is yours to shape.
The Architecture of the Program: Why DSI Is Different
Columbia's Data Science program isn't housed within a single department. It operates through the Data Science Institute (DSI), established in collaboration with New York State government and built on the foundation of three major departments across two schools:
From the Graduate School of Arts and Sciences:
- Statistics
From the School of Engineering and Applied Science (SEAS):
- Computer Science
- Industrial Engineering and Operations Research (IEOR)
This isn't just administrative trivia. It means you have default access to professors, research labs, PhD students, and coursework across all three departments. But it goes further—DSI has affiliated faculty from Electrical Engineering, Biomedical Engineering, Columbia Business School, Journalism, SIPA (School of International and Public Affairs), the Law School, and the Climate School.
The Data Science Institute isn't governed by one department's agenda. It's collectively shaped by minds from fundamentally different disciplines. This creates something rare: a data science program that isn't purely engineering-biased but genuinely interdisciplinary.
Research: Not an Afterthought
Because DSI supports research across disciplines, you'll find initiatives spanning fundamental ML research to applied work in social science, climate science, environmental sustainability, policy, and journalism.
Key research pathways include:
- DSI Scholars Program: A structured pathway connecting students with professors and labs for specific research projects. It's competitive but provides genuine mentorship and hands-on research experience.
- DSI Seed Grants: Faculty receive funding through this program and subsequently hire graduate research assistants. This is another avenue into serious research work.
- Lab Affiliations: You can connect with labs across NLP, speech processing, causal inference, climate science, law, and many interdisciplinary spaces. The model isn't always one-on-one with a professor—it's typically collaboration with the lab ecosystem, working alongside PhD students, building relationships, and gradually deepening your involvement.
A note on publishing: producing quality papers demands real commitment. Three or four hours a week won't cut it. But if you dedicate yourself to a problem, associate with the right lab, and commit to eighteen months of focused effort, publication is absolutely achievable. Most students, honestly, don't have that patience—they're optimizing for industry roles. That's fine. But for those with genuine research ambitions, the infrastructure exists.
Course Structure: Freedom Within Framework
The standard structure includes six core courses and three electives. Some dismiss this as limiting. I see it differently.
The Core Courses:
- Algorithms
- Probability and Statistics
- Exploratory Data Analysis and Visualization
These might sound "basic" to someone scanning a syllabus online. They're not. The probability and statistics course goes deep into measure-theoretic foundations, moment generating functions, and statistical nuances far beyond what you'd encounter in a typical bootcamp or even many graduate programs. The courses start with fundamentals but escalate to critical depth.
For freshers, these three cores build exactly the foundation needed: how to work with data, how to think algorithmically, and how to approach data-driven problem solving. This is where computer science and data science diverge—CS is largely systems-driven (databases, distributed computing, backend architecture), while data science is about leveraging data to solve problems across industries and domains.
On Waivers and Flexibility:
If you've covered equivalent material in your undergrad or through industry experience, you can apply for waivers. I've seen people successfully waive one or two cores, opening up space for additional electives. Some students end up with six electives instead of three.
Elective Landscape:
This is where the program shines. Every semester—especially fall—you'll find 15-20 unique machine learning and deep learning courses across Columbia. Data science students have reserved seats in many, but can also register for courses across Computer Science, IEOR, Statistics, Electrical Engineering, and beyond.
The range is remarkable:
- Theoretical depth: Mathematics of deep learning, statistical learning theory, information-theoretic perspectives on ML
- Applied breadth: Applied ML, applied deep learning, generative AI systems, scalable LLMs, high-performance ML
- Domain-specific: Deep learning for finance, functional genomics, ML for biomolecular engineering, climate modeling
- Paradigm diversity: Probabilistic modeling, causal inference, reinforcement learning, state-space modeling
- Systems-oriented: Edge computing, robotics, embedded ML, signal processing
You can construct a trajectory. Someone wanting to specialize in AI systems takes scalable ML courses and reduces theoretical load. Someone pivoting to finance focuses on econometrics, time series, risk analytics. Someone like me—with six years of applied industry work—deliberately chose reinforcement learning, causal inference, and probabilistic machine learning to explore foundational depth I hadn't had time for while shipping products.
The program accommodates almost any serious direction you want to pursue.
The Lived Experience: What I've Observed
Course Rigor:
Assignments demand genuine cognitive work. You can't just pull code from the internet and submit. Whether it's probabilistic machine learning, causal inference, applied deep learning—every course I've seen demands real understanding. I've watched data science students work through the night in the DSI study spaces, collaborating on presentations and problem sets that push them.
This rigor matters. It builds the kind of clarity and depth that compounds over a career.
The Community:
I'm probably the most experienced data science professional in my cohort doing this master's. Yet the program makes sense for me. I've also seen it work for:
- Complete freshers straight from undergrad building their niche
- Backend engineers transitioning to ML systems and deployment
- Bioinformatics professionals deepening their genomics-ML intersection
- Students with CS and economics backgrounds pursuing econometrics and social science applications
Finding your people matters. Not everyone shares your specific interests—you need to identify like-minded collaborators and travel together. If you're interested in quant finance, find others on that path. If you're research-focused, connect with the theoretically-inclined. Jumping between groups without focus can dilute your experience.
Campus Life:
Columbia's campus is genuinely alive. Libraries full of people working. Study spaces buzzing even on holidays. Buildings open 24/7. It's one of the most vibrant academic environments I've encountered. You'll find robotics clubs, AI alignment groups, policy-focused AI discussions, biomedical ML communities, entrepreneurship networks—platforms exist for virtually any interest. The only requirement is showing up.
The New York Factor
Being in New York isn't incidental to this program. It's structural.
Industry Access:
New York is home to headquarters of major enterprises, the real engines of commerce—not just VC-funded startups, but hedge funds, banks, insurance, consumer tech, SaaS companies. The risk-taking here is more calculated than San Francisco's moonshot culture. But that means mature businesses with real problems and real scale.
Every week, you can find meetups on Luma, Eventbrite, Meetup.com—tech, design, entrepreneurship, AI, product management. Bloomberg, JP Morgan, TD Bank, J&J—companies actively visit campus. DSI organizes employer meetups and career fairs specifically for data science students.
Career Support:
There's a dedicated person collecting resumes, routing internship notifications, coordinating company visits. I've watched my senior batches land at Microsoft, Meta, Capital One, Citadel, JP Morgan, C3 AI, Palantir, and various startups. Freshers have gotten offers in the $160-180k range—outcomes that even experienced professionals sometimes struggle to achieve.
Living Logistics:
Expect $1,400-2,000 monthly for housing depending on proximity to campus. Sharing near Columbia runs around $1,400; one or two subway stops away (Upper West Side, near Columbia Business School) can be $1,000. Safety is fine—I stay one subway station away, good neighborhood, comfortable room. Columbia runs shuttle services in the morning and evening cab services.
Loans around $100k can cover the program. Teaching assistantships and course assistantships are available through structured departmental portals—many curious students in my cohort have secured these positions. They provide both income and pedagogical experience.
The Capstone and Industry Connection
The capstone project pairs you with industry partners—genuine first professional connection for many freshers. But honestly, by that point, most students have already visited multiple company offices through meetups and events. New York's density of tech and business creates constant opportunity for those who seek it.
A Word on Perspective
If you've been absorbing narratives from Twitter or LinkedIn, recalibrate your expectations. Twitter's tech discourse is dense with Y Combinator energy, indie hackers, and startup romance. New York's reality is different—structured businesses, calculated risk, enterprise scale. Neither is better; they're different ecosystems with different values.
Similarly, don't confuse LinkedIn influencer content—interview prep cheat sheets, bootcamp promotions—with what genuine depth in this field requires. The ocean of knowledge in data science, ML, and AI is vast. Academic rigor provides something different: the clarity, foundation, and exploratory freedom that compounds over a career.
I never share interview questions or cheat sheets on LinkedIn. Over years in this field, I've learned that learning never ends. Academic programs offer the freedom to explore, to seek what genuinely interests you, to build leverage that pays dividends for decades.
Who Should Consider This Program
This program is ideal if you:
- Want genuine freedom to craft your trajectory—theoretical, applied, domain-specific, or interdisciplinary
- Value depth over shortcuts and understand that rigor builds durable foundations
- Are curious about research and want pathways into labs and publications
- Appreciate being in a major city with constant industry access
- Want exposure to world-class faculty across multiple departments
- Are willing to actively engage—reach out to professors, join labs, attend events, initiate conversations
This program may not fit if you:
- Just want an Ivy League credential without engaging the opportunities
- Plan to focus entirely on interview prep without leveraging courses or research
- Prefer highly structured, prescriptive programs that tell you exactly what to do
- Aren't interested in the exploratory freedom the program provides
If interview prep is your only goal, frankly, you can do that anywhere—a state university, a suburb school, a cheaper option. You'd be preparing on your own anyway. But if you want to build something—skills, knowledge, connections, research—this environment rewards that investment.
Final Thoughts
Columbia's MS in Data Science is a complete package. New York as a city offers everything. Columbia as a university spans multiple dimensions—research, industry, entrepreneurship, interdisciplinary collaboration. The program itself provides freedom in coursework, depth in rigor, and genuine pathways into whatever niche you want to build.
It's not about the ranking—whether Columbia is top 10 or top 20 matters little at the individual level. What matters is whether you'll engage with what's available. Every ranking tier has successful people because individuals determine their outcomes. The infrastructure here supports almost any serious ambition.
I've found exactly what I needed: theoretical depth I couldn't pursue while building products in industry, research directions I'm genuinely curious about, and an ecosystem that lets me craft my own path.
If that resonates with you, this might be your place too.
If you have questions, feel free to reach out to me on LinkedIn. All the best with your decisions.