How to change careers and become a data scientist - one quant's experience

This post has been translated into Chinese here.

I sometimes receive emails asking for guidance related to data science, which I answer here as a data science advice column. If you have a data science related quandary, email me at [email protected]. Note that questions are edited for clarity and brevity. Other installments of the data science advice column include:

Q: This question is a composite of a few emails I’ve received from people with some limited programming skills, living outside the Bay Area, and interested in becoming data scientists, such as the following:

Q1. I’m a financial analyst at a major bank. I’m in the process of pivoting to tech as a software engineer and I’m interested in machine learning, which lead me to your post on The Diversity Crisis in AI. Do I need a masters or PhD to work in AI?

Q2. I am in the 6th year of my PhD in pure mathematics and am going to graduate soon. I am really interested in data science and I want to know if I want to get a job in this area, what can I do or what should I prepare myself so that I can have the skill sets that the companies need? I’m now reading books and trying to find some side projects that I can do. Do you have any ideas where I can find these projects that will interest employers?

Q3. I have a graduate STEM degree and have worked as both a researcher and a teacher. I am currently in the midst of a career transition and looking for industry roles that might need both analytical and instructional skills from their employees. My knowledge is more on the science side though rather than in software. The Internet can get to be a pretty overwhelming place to pull information from without the actual sharing. Do you have recommendations for programming courses and workshops that are also friendly to a teacher’s budget? And what coding languages or skills would you say would be most helpful to focus on developing?

A: I think of myself as having a somewhat non-traditional background. At first glance it may seem like I have a classic data science education: I took 2 years of C++ in high school, minored in computer science in college (with a math major), did a PhD related to probability, and worked as a quant. However, my computer science coursework was mostly theoretical, my math thesis was entirely theoretical (no computations at all!), and over the years I used less and less C/C++ and more and more MATLAB (why oh why did I do that to myself?!? Somehow I found myself even writing web scrapers in MATLAB…) My college education taught me how to prove if an algorithm was NP-complete or Turing computable, but nothing about testing, version control, web apps, or how the internet works. The company where I was a quant primarily used proprietary software/languages that aren’t used in the tech industry.

After 2 years working as a quant in energy trading, I realized that my favorite parts of the job were programming and working with data. I was frustrated with the bureacracy of working at a large company, and of dealing with outdated and proprietary software tools. I wanted something different, and decided to attend the data science conference Strata in February 2012 to learn more about the world of Bay Area data science. I was totally, absolutely, blown away. The huge enthusiasm for data, all the tools I was most excited about (and several more I’d never heard of before), the stories of people who had quit their previous lives in academia or established companies to work on their passion at a startup… it was so different and refreshing to what I was used to. After Strata, I spent a few extra days in San Francisco, interviewing at startups and having coffee with some distant acquaintances that I found had moved to SF - everyone was very helpful and also apparently addicted to Four Barrel Coffee (almost everyone I spoke with suggested meeting there!)

I was star-struck, but actually switching into tech made me feel totally out of it… For my first many conversations and interviews with people in tech, I often felt like they were speaking another language. I grew up in Texas, spent my 20s in Pennsylvania and North Carolina, and didn’t know anyone who worked in tech. I’d never taken a statistics class and just thought of probability as real analysis on a space of measure 1. I didn’t know anything about how start-ups and tech companies worked. The first time I interviewed at a start-up, one interviewer boasted about how the company had briefly achieved profitability before embarking on rapid expansion/hiring. “You mean this company isn’t profitable!?!?” I responded in horror (Yes, I actually said that out loud, in a shocked tone of voice). I now cringe in embarrassment at the memory. In another interview, I was so confused by the concept of an “impression” (when an internet ad is displayed) that it took me a while to even get to the logic of the question.

I’ve been here five years now, and here’s some things I wish I’d known when I was starting my career move. I’m aware that I’m white, a US citizen, had a generous fellowship in grad school and no student debt, and was single and childless at the time I decided to switch careers, and someone without these privileges will face a much tougher path. While my anecdotes should be taken with a grain of salt, I hope that some of these suggestions turn out to be helpful to you:

Becoming ready for a move to data science

Most importantly: find ways to work whatever you want to learn into your current job. Find a project that involves more coding/data analysis and that would be helpful to your employer. Take any boring task you do and try to automate it. Even if the process of automation makes it take 5x as long (and even if you only do the task once!), you are learning by doing this. Analyze any data you have: from research for an upcoming purchase (i.e. deciding which microwave to buy), data from a personal fitness tracker, nutrition data from recipes you’re cooking, pre-schools you’re looking at for your child. Turn it into a mini-data analysis project and write it up in a blog post. E.g. if you are a graduate student, you could analyze grade data from the students you are teaching Learn the most important data science software tools: Python’s data science stack (pandas/numpy/scipy) is the #1 most useful technology to learn (read this book!), followed closely by SQL. I would focus on getting very comfortable with Python and SQL before learning other languages. Python is widely used and flexible. You will be well-positioned if you decide to switch to more software development work or to go full-steam into machine learning. Use Kaggle. Do the tutorials, participate in the forums, enter a competition (don’t worry about where you place - just focus on doing a little better every day). It’s the best way to learn practical machine skills. Search for data science and tech meetups in your area. With the explosion of data science in the last few years, there are now meetups in countries all over the world and in a wide variety of cities. For instance, Google recently held a TensorFlow Dev Summit in Mountain View, CA, but there were viewing parties around the world that watched the livestream together (including in Abuja, Nigeria, Coimbatore, India, and Rabat, Morocco).

Online courses

Online courses are an amazing resource. You can learn from the world’s best data scientists in the comfort of your own home. Often the assignments are where most of the learning occurs, so don’t skip them! Here’s a few of my favorites:

As one of the questioners highlighted above, the amount of information, tutorials, and courses available online can be overwhelming. One of the biggest risks is jumping from thing to thing, without ever completing one or sticking with a topic long enough to learn it. It’s important to find a course or project that is “good enough”, and then stick with it. Something that can be helpful with this is finding or starting a meet-up group to work through an online course together.

Online courses are very useful for the knowledge you gain (and it’s so important that you do all the assignments, as that is how you learn). However, I have not seen any benefit to getting the certificates from them (yet– I know this is a newer area of growth). This is based on my experience as having interviewed a ton of job applicants when hiring data scientists, and having interviewed for many positions myself.

News sources

Twitter can be a surprisingly helpful way to find interesting articles and opportunities. For instance, my collaborator Jeremy Howard has provided over 1,000 links to his favorite machine learning papers and blog posts (NB: you’ll need to be signed in to Twitter to read this link). It will take some time to figure out who to follow (and may involve some following and unfollowing and searching along the way), although one short cut is to look at who wrote the tweets you like in the link above, and follow them directly. Look up data scientists at companies that interest you. Look up the authors of libraries and tools that you use, or are interested in. When you find a tutorial or blog post you like, look up the author. And then look up who these people retweet. If you are unsure what or how to tweet, I think it can be helpful to think of Twitter as a way to (publicly) bookmark links that you like. I try to tweet any article or tutorial that I think I may want to reference back to in a few months time.

The machine learning subreddit is a great source of recent news. You may find a lot of it inaccessible at first, but after a couple of months you’ll start seeing more and more that you recognize

It’s helpful to sign up for newsletters such as Import AI newsletter and WildML news

Moving to the Bay Area

Do whatever you can to move to the Bay Area! I realize that this won’t be possible for many people (particularly if you have children or for a variety of visa/legal residency issues). There are so many data science meet-ups, study groups, conferences, and workshops here. There is also an amazing community of other bright, ambitious, hungry-to-learn data scientists. I had trouble even figuring out which were the most useful things for me to learn from afar. Although I’d started studying machine learning on my own before moving here, coming to SF rapidly accelerated my learning.

My first year in San Francisco was a period of intense learning for me: I attended tons of meetups, completed several online courses, participated in numerous workshops and conferences, learned a lot by working at a data-focused start-up, and most importantly met scores of people who I was able to ask questions of. I completely under-estimated how amazing it is to be able to interact regularly with the people who are building the tools and technology that excite me most. I’m surrounded by people who love learning and are pushing the cutting edge of what is possible. That TensorFlow Dev Summit I mentioned above that people watched from around the world? I was lucky enough to be able to attend it live, and my favorite part was the people I met.

One good approach to moving here is taking a “not-your-dream-job”; i.e. try to get to a place where you’re surrounded by people you can learn from, even if it’s not a role you’d otherwise be interested in. I decided to switch careers in early 2012, before Insight or other data science bootcamps existed. I applied to a few of what were my “dream jobs” at the time and was rejected. In hindsight, I think this was a mix of my lacking some needed skills, not knowing how to market myself properly, and doing a fairly brief job search. In March 2012, I accepted an analyst position at a start-up I was excited about, with the hope and an informal agreement that I could move into an official data science/modeling role later. Overall, it was a good choice. It let me move to San Francisco rapidly, the company I joined was great in a number of ways (including having a weekly reading group that was working through Bishop’s Pattern Recognition and including me on a field trip to meet Trevor Hastie and Jerome Friedman), and my manager was supportive of me doing more engineering-intensive projects than the role was officially scoped for. One year later, I landed what on paper was my dream job: a combined data scientist/software engineer role with a startup that had fascinating datasets.

There are also some good bootcamps in the area, which generally also provide many opportunities to connect with interesting people and companies in the data science space.

Insight Data Science is a 7-week, free, intensive bootcamp for graduates with PhDs in STEM fields. Potential downsides: Since it is only 7 weeks, part of which is focused on networking and job search, I believe it’s mostly for people who already have most of the skills they need. Also, it’s very competitive to get into.

Data Science boot camps such as Galvanize or Metis. Positives: These are 12-week immersive experiences, that provide structure and networking opportunities. Downsides: These are rather expensive. Some factors to consider: How close is your background to what you need? That is, if you have little programming experience, it may be necessary to do something like this, but if you are transitioning from a closely related field, this may be overkill. Also, how motivated are you with independent self-learning? If you struggle with it, the accountability and structure of a bootcamp could be helpful.

There are many factors in deciding whether to do a bootcamp. A big one is how much structure or external motivation you need. There are a lot of amazing resources available online. How much discipline do you have? Note that it’s important to accept what you need to best learn. I find the motivation of online courses and having assignments really works for me, and I used to feel embarrassed that this was easier for me than having a completely independent side project. Now, I’ve accepted this and try to work with it. Other questions to ask: how much do you need to learn, and how quickly can you learn on your own? If it’s a lot, a bootcamp may really speed that up. One area where I think bootcamps can particularly shine is teaching you how to put a bunch of different tools/techniques together.

You can also move here without a job. This requires a number of things, including: ample savings, US legal residency status, and not having children, so it won’t be an option for many people. However, if you are able to do it (i.e. a US permanent resident, coming from finance), it can be a good option. Searching for a job in tech can be a full-time job, as data science and engineering interviews require a lot of studying to prepare and many require time-intensive take-home challenges. In hindsight, I’ve often done rushed job searches when I was working full-time and job-searching, and that has lead me to a few sub-optimal decisions. You will certainly find plenty of ways to fill your time with studying for interviews, coding side projects, and attending workshops and study groups. Also, two things that surprised me when I switched to tech are how frequently people switch jobs, and how normal it is to take time off between jobs for learning new things or traveling (so there is less reason to worry about gaps in your resume, as long as you have good answers about what you were learning during that time).

The huge caveat: I was unaware of how sexist, racist, ageist, transphobic, and morally bankrupt Bay Area tech is (despite its grandiose claims to be creating a better future) 5 years ago when I made my move. A few years later, I became so discouraged that I considered leaving the tech industry altogether. Tales of betrayal, callousness, and cruelty abound: for instance, someone I’m close to had his family medical emergencies exploited for profit by his coworkers, and many of my friends and loved ones have had similarly awful experiences. However, the community of passionate, fascinating people and access to cutting edge technology keeps me here, and given the choice, I’d choose to move here all over again. I currently feel very lucky with fast.ai to be working on the problems that I find most interesting and believe will have the greatest impact.

Answers to miscellaneous related questions

Do I need a masters or PhD to work in AI? I firmly believe the answer is NO, and I’m working to make that even more of a reality than it already is. In fact, AI PhDs are often not that well-positioned to tackle practical, relevant business problems, because that is not what their training is for. Academia is focused on advancing the theoretical bounds of the field, and is motivated by what can be published in top journals (very different from what will create a viable business!). Read more about fast.ai’s educational philosophy here and check out our free, online course Practical Deep Learning for Coders.

Should I learn Ruby after learning Python? There’s no reason for an aspiring data scientist to learn Ruby. It’s similar enough to Python that it won’t teach you new concepts (the way learning a functional language or lower-level language would), and there is not much of a data science eco-system.

Where can I find side projects that will interest employers? I think I can find random data sets online, but I guess employers do want to see how I handle a real situation? Don’t feel like your side project needs to be completely unique, or involve a unique or unusual data set. It’s fine to use a dataset you get from Kaggle for a side project. It’s fine if your project isn’t ground-breaking. When creating side projects, blog posts, or tutorials, think of your audience as the person who is one-step behind you, as they are the one who you are best positioned to help. You may be worried that a project or post wouldn’t be interesting to someone senior in the field, or that someone else may have done something similar. That’s fine! It’s good just to get your work out there.