Data science interviews

The aim of this guide is to provide the “delta” between a software engineering interview and a data science interview, for a mathematically-oriented researcher. This guide is suitable for researchers well-versed in some combination of statistics, probability, machine learning, optimization, and related areas.

If you don’t already have software engineering interview skills, I recommend looking additionally at guides for building those skills. Almost all of those skills (writing code live, designing algorithms, etc.) and good interviewing practices (asking good questions, showing interest, communicating well) still apply here, except there will be fewer computer systems questions and less emphasis on best practices in software engineering.

Anyway, here is how I prepared for my data science interviews.

Step 0: what is data science?

Learn your audience. It’s good for context to know what exactly people mean when they are talking about data science. it’s sufficient to read the top articles from google, and I’ve provided some here.

Step 1: imagine the problems that the company will ask you

What are the questions that the company would like to know from their data? What kind of data do they have? How can the data be related to the company’s mission / bottom line / target audience? How can their data affect their day-to-day operations? How can it affect their longer-term business decisions? What kinds of methods would you use to solve these problems? What kinds of visualizations would be appropriate? How do some of these questions then translate into products or features?

Step 2: pseudo-structured wandering through wikipedia++

The goal here is primarily to familiarize oneself with the terminology of data mining, with an emphasis on the high-level concepts and what people tend to use in practice. I’ve listed a bunch of topics I reviewed below, and the goal is to develop the following for each area:

  • Have an intuitive understanding (and way to explain) the concept / technique / method
  • Be able to write the (main) relevant equations; know the key properties
  • Have an example at hand to demonstrate usage/understanding
  • Understand when it is used and why; know related tools and when it is better to use one vs the other; what are considered “good” values and how to determine/assess/validate them



  • Learning rate (machine learning) == step size (convex optimization)
  • Spectrum analysis == frequency domain analysis == spectral density estimation
  • Predictive analytics == predictive modeling and forecasting
  • Multinomial logistic regression == softmax regression == multinomial logit == maximum entropy (MaxEnt) classifier == conditional maximum entropy model

Step 3: okay, what do other people do for interview prep?

Additionally, there is an explosion of data science books (e.g. this or this) and blogs that I’m sure are also very useful for data science interviews.

If you are a grad student in a technical field, leave a comment with your interview preparation techniques!

Insightful advice for academia from WICSE

Today I attended the WISCE Berkeley-Stanford annual meetup and there was quite a bit I learned that I think applies to anyone in the field, so I wanted to share it here (along with more women-specific things). In particular, I found the academia panel to be very insightful and wise. Here were the key points.

First off, there are lots of resources specific to women + careers in EECS from an annual conference called Rising Stars in EECS. It’s neat because the talks are all online and the advice is quite specific to EECS and covers both academia and industry.

The panelists

The first invited panelist is Professor Ruzena Bajcsy (Berkeley), who has been in the field for 40+ years and thus has been through many of the shifts through the generation. The second panelist is Professor Tsu-Jae King Liu (Berkeley, current EECS department chair). She was immediately asked a bunch of questions about time management, chair responsibilities and whatnot, to which she says that of course being chair is a lot of work and easily takes up half of her time. The third and last panelist is Dr. Sadia Afroz (Berkeley, current postdoc).

Shifts in academia

When asked about what the biggest changes in academia have been, Ruzena commented that she’s noticed almost an obsession with money among young researchers… people are always talking about their salary, grant funding, etc. She cautioned (and reminded) us that she chose academia because she wanted to do her own projects, to think independently… not to be led where the money lies.

Advice for new faculty

Ruzena, now 80+ years old, still advises a full lab, comes to work every day, stays fully engaged in the community and the field. I find it remarkable and admirable and awesome. When asked the question “What advice would you give a new young faculty?” she gave the advice: you really want to have a job where every day you are happy to go to work. She commented that she is literally happy to come into the lab every day, including weekends (our meetup was on a Saturday). Specific to academia, she advised that if you are not curious, don’t like teaching, don’t like exploring wild ideas (and keep in mind that many of those ideas won’t work out), don’t do it.

A lot of these qualities actually ring true with me. I love teaching, mentoring; I like exploring new ideas, I like thinking about the future, and certainly I’m curious. But at the same time, it’s really important to me that the things that I work on see the light of day and actually make a real difference in people’s lives. So I asked the question: is curiosity in conflict with broad impact? It seems clear to me that, at some point, if you want to carry an idea out further and broader, then you need to spend the time and effort to invest in that single idea… which of course detracts from other curiosities. So then is academia still suitable if your life drive is the impact and results rather than the curiosity?

Ruzena’s response was actually unexpected but heart-warming and wise. So, Ruzena had just seen the talk I presented at this meetup, which was focused on developing large urban systems. In my particular context, she agreed that academia is probably best suited for this type of work because it can connect me with a wide group of people in many different related fields, and this is necessary since urban systems touch on so many different studies, e.g. EECS, civil engineering, urban planning, economics, public policy, etc. But more generally, she advised that everyone take advantage of the breadth of courses and resources that are available to us (at a top school); to explore a bit because anything narrow that we learn now will become out of date. And so it is prudent to pick up a breadth of skills.

Nice gals lose?

Often times, women (and actually many people in EECS) feel like they need to be more aggressive in order to succeed, but feel conflicted because it is counter to their personality. When asked the question of “Should I be more aggressive if I want to attain professorship?”, Liu responded that confidence is everything; that the key is to develop confidence in your own abilities, over time and to recall that often times people are just curious… there’s no need to get defensive. In order to have people recognize you as an individual rather than as a woman is to have confidence from within, display that confidence, and engage in conversations.

Advice for yourself?

The next question was: now, putting yourself back in your own shoes as a grad student, what’s one piece of advice that you’d give yourself, in order to succeed in academia? Ruzena responded succinctly: learn as much mathematics as you can. Liu’s advice was to really get to know your classmates; in fact, that also helps with building confidence. Sadia’s response was to not be afraid to ask questions, make the critical comments about other people’s work — to not assume that other people know better — because often times, you will be right. And I full-heartedly agree with all of the responses. The bottom line is really to gradually build up your own confidence.

Relatedly, what’s one thing you did right?

To this, Liu responded with seeking out a good mentor, one who is separate from your academic advisor. This person should believe in you, be able to provide emotional support. Ruzena, being the only female in robotics at the time (eeeesh) and immersed in a somewhat WASP-y culture (white anglo-saxon protestant), simply tried to make friends, looking for those (male) colleagues who were open to equality. She slowly built up a network and community for herself, slowly but surely. It wasn’t revolutionary by any means, she commented, but she was really supported by this network despite the opposing culture. What we have here now with WISCE is not far off. It’s a great community of like-minded people, and it’s really important that we stick together and support one another.

Thanks to Alex Lee for feedback on this article!

Professor Seth Teller

seth teller - shrinkrob

Professor Seth Teller, my former advisor (as an undergrad researcher), mentor, and academic role model, passed away earlier this month. He truly and passionately worked towards addressing important problems (with autonomous vehicles, assistive technologies, and robotics for disaster recovery at least!), somehow with both vigor and patience; he has done so much, and yet there was so much more to do and more to come. The situation is entirely shocking to me, and I wanted to share some words.

On 7/12, I was reminded of how compassionate of a community MIT is. I woke up to an email with the subject line “Call me ASAP” from my former grad student supervisor David Hayden. It had been more than 1.5 years since we were last in touch, but he wanted me to hear the sad news from a person and not from something less personal (i.e. email, internet). (Thank you, David, I really appreciated it!) And it’s a great reflection of the warm person that Seth was (and the people around him!). No matter how busy he was, he would smile and greet me in the halls, and always made time to meet with me when I asked. Throughout the day, friends messaged me, and I messaged friends, to make sure that everyone was doing alright. A couple years out of MIT now, I am reminded that Seth has touched so many people, so many lives. Even friends who did not know him except as a professor reached out to say a few kind words.

I want to share one small anecdote, which has affected me to today.

About 2.5 years ago, having very little clue about what I wanted to do/achieve with my life (what some of us fondly call the quarter-life crisis), I went to Seth Teller at a loss and told him the executive summary of my vague interests: “I think self-driving cars are pretty cool.” Seth had co-led the MIT DARPA Urban Driving Challenge team back in 2006, but at this point, it was mostly a past project. He told me the following: “If you have an idea of what your passion in life is, then you have to go after it as hard as you possibly can. Only then can you hope to find your true passion.”

Anyone can tell you to go after your dreams. Seth’s insight is that dreams and ambitions are not always clear from the start — they may be hidden, they may manifest themselves in several forms. He knew that hard work is required to find them, extract them from the mess of school and experience and daily life, pursue them, and achieve them.

Shortly after, I left his group and joined the Distributed Robotics Lab (under Daniela Rus, MIT), where I started studying transportation problems from a computational/robotics perspective and did my Masters thesis on algorithms for automatic mapping (“GPSZip: semantic representation and compression system for GPS using coresets”). And now I have moved on to Alex Bayen’s group at Berkeley to continue studying the problems of estimation, prediction, and control/automation of current and future transportation systems.

In short: When I grow up, I want to be like Seth Teller. I want to work on important problems, and I want to help people. I want to support the people around me, and I want to help people find and go after their dreams. And I want to always take the time to smile and say hello.

I am grateful for every short minute I spent with Seth. For more information, here is the initial press release, the investigation update, and his personal website. I do not know the circumstances for his death, but I am very sorry for the world (and especially those closest to him) for the loss.