🚵🏼 🎀 🐚 How I (PhD Neurobiology) became a Data Scientist in 6 months 🐍 🖐🏿 👃🏻

Four tools that I used in training without spending a cent.

I just ran away from eight years of study and hard work with no plan. You might be wondering why people do such things. The fact is that for a long time my boss fought off my desire to work, and I understood that it was time to change something.

My young man invited me to become a data scientist. My reaction, of course, was “You're crazy!”, Because I knew absolutely nothing about programming. No doubt he overestimated my abilities. So the impostor syndrome again reminds of itself.

After about two weeks, my friend Anna suggested the same thing. With a little thought, I seriously began to think about this idea. Why not? So I decided to become a newbie again and start a new life as a data scientist.

I wanted to study at my own pace, so I decided to take online courses. I figured that with PhD in neuroscience I already got enough formal training to work in data science. I just needed practical knowledge.

I will talk about four different courses I took and how they led me to work with data science in a healthcare startup in Silicon Valley.

Most of the online courses I found were free at that time. So I challenged myself - to get all the necessary skills without spending a dime. What can I say, I'm a real miser.

Basic skills

When I first left my doctorate at the University of California, San Francisco, I had absolutely no programming experience. In my research, I used statistical indicators, but on a small scale. All analyzed data sets were created by me in the laboratory. For this reason, the number of observations was extremely small. I needed to learn how to write code and analyze data in a much larger volume.

Getting started writing code

When I decided that I want to become a data scientist, first of all I wanted to learn how to write computer programs. Since I had never coded before, the whole process was completely unknown to me. I decided that if I hate writing code, then data science is definitely not for me. It seemed like a good idea to start with.

I was lucky because my partner Ben worked in many technology sectors and was able to point me in the right direction. He suggested that Python would suit me best. Python is ideal for data analysis, it is versatile and does a great job with large amounts of data. A start has been made.

Learning programming

1. Codecademy

At the beginning of training, I used Codecademy . I started with the “Introduction to Python” course, but I'm not sure that it still exists, since I passed it back in 2014. If I needed to start learning Python right now, I would probably start with the Analyze Data with Python course .

It seems to me that Codecademy is a great starting point. The main advantage for me was that I could write code directly in the browser. It’s also all the worse for me to correctly install software on my computer. Therefore, in the beginning it was nice to avoid this. It was calm from the thought that if my code didn’t work, it was because of a syntax error , and not an error in installing software.

I also liked how classes could be given for several minutes. I went to Codecademy when I had free time, and solved several problems awaiting my attention. This step-by-step progress meant that I was not afraid of volumes and was not stuck in them.

By the time I finished the course, only a few courses were offered on the site, and this one was free. I was amazed at the quality of free online programs.

Once I learned the basics of Python, I needed to start improving my knowledge of statistics and learning how to analyze data on a larger scale.

Learning Data Analysis

2. Coursera Data Science Specialization by Johns Hopkins As a
second course, I completed the Coursera Data Science Specialization by Johns Hopkins. At that time, it was possible to get an honorary certificate for free, and you only had to pay for a verified certificate.

The verified certificate was not so important to me. I had to demonstrate skills from the courses during technical interviews. Therefore, I chose the free version.
This series of courses was taught in the R language, which for me was a drawback. R is an excellent programming language for statistical analysis and is popular in academia. However, for data science, I wanted to learn Python. It seemed to me that it would be more useful for startups where I wanted to work.

I looked into several courses on data analysis in Python, but they suggested skills that I did not have yet. It seems to me that most of these courses were aimed at software developers who wanted to move to data science. Therefore, the creators of the course suggested that you already have significant programming skills and you already know how to set up your environment in Python.

I liked that this course explained all aspects from the very beginning. The first lesson had step-by-step instructions on how to install R and R-Studio. It was easier to take further courses, knowing that technical problems would no longer arise.

This course was also taught by the Department of Health, which was very suitable for me. My experience in the medical sciences made it easy for me to understand the examples given. They cited the effects of air quality on asthma and other health data sets. Therefore, I could focus on the content of the course, rather than trying to understand the scripts presented for data analysis.

This series of courses really helped me to get started at a basic level of understanding the basic aspects of data science. She covered R programming, data cleansing, analysis, regression, and machine learning. I really enjoyed learning to code and use code to analyze data, so I was inspired to continue learning.

Information Interviews

At this stage of my retraining, I started asking my friends to introduce me to people in San Francisco, who also moved from academia to data science. Some were able to help me, so I scheduled as many informational interviews as possible.

My friend introduced me to a data scientist from Modcloth with a similar career path. She was also a neuroscientist whose advice came in handy to me.
Her main recommendation was to learn SQL.

Exploring Database Queries

3. DB5 SQL Stanford Online

In the course of Johns Hopkins on Coursera there was not a word about SQL. My new acquaintance said that most of her daily work consisted of searching the database. She needed to retrieve information for business management and marketing teams. Statistical analysis and machine learning took very little time.

I followed her advice and started an independent course, “ SQL course Stanford Online ”. Of all the courses I took, this one is my favorite, because the teacher turned out to be excellent and used simple examples to explain concepts. She also used several methods to explain one concept.

Since then, I have recommended this course to so many, because I believe that every data scientist needs a good foundation in SQL. Many courses in data science that I came across did not tell how to get data from a database using SQL. In my opinion, this is a big mistake. In most courses, you can find a ready-made CSV file for students, but I rarely saw this directly in my work.

As soon as I finished the SQL course on the Stanford Online platform, I started applying for vacancies in data science. Then I lived in Australia and started getting interviews on Skype at startups in the Bay Area in San Francisco. During the interviews, I wanted to continue to develop my skills.

Fixing concepts

4. edX Foundations of data analysis

Then I took the Foundations of data analysis course on the edX platform using R. It helped me remember the concepts that I already took on the Coursera course.

I strongly believe that various teaching methods help to absorb new information. It’s much easier to understand the statistics and concepts of machine learning, studying them in the second round. I think this course has given me a deeper understanding of the topics.

I was still finishing the course when I successfully passed an interview at Amino, a healthcare startup in San Francisco, received a work visa, and moved to the United States.

Getting a job in data science

It seems to me that the final interview was successful because I had not only decent code writing skills and statistics, but, more importantly, I had a background in healthcare, experimental design, and the scientific method.
In my opinion, it was these additional competencies that made my resume more meaningful in the eyes of employers, and they ventured to take me to a startup. I was a newbie who needed several times more training. It seems to me that all the courses taken were exactly enough for the selection committee to draw attention to me, but my experience in the field of healthcare really distinguished me from other candidates.

Therefore, if you want to change your profession and move to data science, I would recommend looking for those companies where your existing knowledge will be appreciated.

What I would like to learn

The main gap in my knowledge that I would like to fill before starting work in a new company was the use of Git through the command line. I had never used a terminal or command line before, so I had no idea how to use Git to commit my code to my company’s Github repository.

Several experts took a long time to get me up to date. I would like to at least have an idea about this topic so as not to lose their precious time. My colleagues are absolutely amazing and seemingly not at all against helping me, but in the early days I felt a burden.

In the end, I got involved, the “ Learn Code the Hard Way Command Line ” course also helped me a lot .

If you are considering a similar transition to data science - go for it! For me, this choice was correct. Everyone studies in different ways, but if you have self-discipline and finish what you start, you have every chance to learn data science using online courses. I wish you good luck and will be happy to answer any questions.

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How I (PhD Neurobiology) became a Data Scientist in 6 months