I didn’t know what I didn’t know: How I started from zero in data analytics
The motivation to write this post is to provide a comprehensive reply to the questions I have been asked six times just today (and to save some time by not having to type individual replies).
>What class did you take to get started in data analytics?
>How much did you spend on the classes?
>Can a person from a non-tech background do fairly well in data?
>Will I be able to learn data and programming if I don’t have a “math” mindset?
>Was it hard to get your first job in data?
>How much were you paid at the start?
About myself: I graduated with a BA in English Philology, I worked in marketing for about 7 years, I have never programmed before. In 2016 I hit a very low point in my life, I had no money, burned through my startup, lost my job, went through a divorce, and was raising two kids on my own with no idea how to pay the rent next month. My main motivation to move to tech was to make enough money to support myself and the kids in the Bay Area. I was scared about a future without a good career. And I was even more scared of failure should I be brave enough to transition into tech. Because of this fear I postponed learning data and programming for about 2 years.
At 36 I thought I was too old to learn programming. At 40 I realized I wasn’t.
Hopefully this post can bring some clarity to those who are just making this sharp career turn and are a few years behind me. I love how Rachel Thomas put it “You are best positioned to help people one step behind you.” If you are just starting, this post is for you.
Let’s be clear about classes and courses and how those can help you. I don’t think there is just one magic class that will give you everything you need to know. If you don’t have a CS or engineering degree (I didn’t!) expect to take a variety of classes on the same topic. Why? You will find out that every teacher explains the material differently and listening to the same content from three or four different instructors can help you understand it throughly. I advise not to focus on finding the best course, but instead sign up and attend a few courses in parallel. You can do classes on Coursera, at a community college, on Udemy, or even do tutorials on TeamTreeHouse or DataCamp. During the first two years attend as many classes as you can, ideally every day. For example, I started with R specialization on Coursera. I never use R in my work now, I use Python and Tableau, however, learning R gave me some basics of data analytics and allowed me to orient myself in the field.
You don’t need a large budget for learning python and data anlaytics. Most of Coursera classes are at $49/month (at the moment of writing). Data Science in community college cost around $1,300 for an academic year. Some classes and bootcamps are free or have scholarships. At the end of this post I will list the classes and courses I took.
I think that you can move to data analytics from any background, at any age, and at any stage of your career. And you can be succesful! However, please remember that it will take time to advance in the field, it will not happen after a six-months bootcamp. I think that you can get an entry level job as data analyst after one-two years of evening/week-end studies and some Github code to prove your skills. If you can, while you study apply for a job that is not technical but bordering with tech and from where you can grow into a more technical role. Feel free to check jobs at Accelerate Change (the company where I started my tech career). I love their culture, kindness, and focus on constant learning supported IRL (allocation of learning time and budget).
Can you succeed in a data career if you don’t have a “math” mindset? I don’t think this was my case and I can’t talk outside of my own experience. I always loved math and statistics and I welcomed the idea of working in tech, I just was very apprehensive that I wouldn’t be good enough. On the other hand, I don’t think there is such a thing as a “math” or “not-math” mindset. If you can do your household bills and understand the sale percent off on an item then you understand numbers, and with a little push you can understand data. Is it possible that our fear of failure in a tech role convinces us that we don’t have a “math” mindset? I was always against sabotaging myself, thus I would put the whole idea of a “math” mindset on a back burner and give it a try.
My main advice: don’t quit. Even if you don’t understand the material and lose faith in yourself, just keep showing up to class, keep googling answers to the questions you have, keep asking questions in the class, keep doing your homework. If you don’t quit during the hard moments (first two years), I am sure you can be good at it and find a job.
The hard part about the first two years (specially for those coming from a non-technical background) is that you seem to live in the middle of a black hole. The problem is not that you don’t know something. The problem is that you don’t know what you don’t know. And this makes you feel like living in the darkness with no vision or clarity. Take my word for it, just keep showing up. With discipline it will get better and eventually you will see the light and things will make sense. If you lose hope, may the discipline be the light to guide you.
As we joke with a fellow data scientist, you are a good data director if you can google the correct answer in under 4 tries. There is a lot of truth to this joke. In many cases you will master a narrow skill set that your job requiers. You don’t have to memorize the rest (it is actually impossible), what you need is to understand the problem, know what tools are available, and be able to search for what you need. Ability to formulate the question correctly is THE skill to master. Aim at that!
Let’s get to the job part now. Was it difficult to get my first job in data? No, it wasn’t, at least in my case. However there is a caveat. I started working at Accelerate Change as a content writer while I was learning data analytics, my starting salary was in the $50–60K range. I worked closely with people running A/B tests. After gaining some confidence in the first six months I started helping our growth-hacking team and in a year was promoted to the A/B Experimenter role. Meanwhile I moved to study data science and took a few statistics courses. I was eager to learn and test as much as I could, I talk about it in this interview (in Russian only). In a year my manager offered me the Lead Experimenter role which allowed me to grow both financially and professionally. A few years later I got a Data and Technology Director position at a different venture within the same accelerator, I wrote about it previously here.
Bottom-line, if you can, get a job that will allow you to grow from a non-technical to a technical role. It might be easier than you expect.
I think I answered the key questions about how I started. If you have more questions feel free to ask in the comments or in the Facebook group.
List of classes and courses I took:
R Specialization (Coursera, $49/month)
CS50 (Harvard, free) — Great class to gain general idea about programming.
Python & Data Science in community college — Unfortunately the college I went to stopped offering those classes, however I found similar programs here: Python and Data Science.
Statistics (class in Russian, free) — There are various good statistics classes that I can recommend
Data Science Basics (free videos by Prof. Michael Pyrcz, GeostatsGuy) — Michael has videos about Python, Data Science, ML and I found his explanations to be very digestible.
GDG bootcamp (free if you attend all the classes) — There are no current bootcamps, but I am sure there will be more in the future
FastAi (free) — This is an absolutely wonderful ML course that I am currently taking. Highly recommended if you know python.
Machine Learning (Coursera, free)
Additional Resources:
Python and Data Science Learning group on Facebook — We post in this group jobs, classes, and resources. Feel free to join even if you are from the outside of the Bay Area.
AiAi (free for women and minorities) — Check available courses, fill out the contact form on their site, and feel free to ping me if you are interested in being part of the next cohort.
