Understanding the Career of Data Scientists in a Data Science Way
Are you ready for your brand new data science career? Here is what you should know about data scientist careers learned from data science
2018. The future is already here.
Following the gold rush in artificial intelligence, a new career track called “data scientists” has taken the world by storm. With a combination of skills in business intuition and technical soundness, data science is considered the sexiest job in the 21st century. The data science community has witnessed an explosive 275% growth over a short span of 7 years.
Even with the growing interests, the general population still could not have a solid understanding of the data scientist roles.
So, what exactly do data scientists do?
From my personal experiences coming from a financial engineering background, I was habitually more prone to dive right into the large datasets and discover patterns from them. Now having self-taught data science for over a year, I came to revelations that the way data scientists work is different from engineers
So, what exactly are the differences between a data scientist and a software engineer?
In the first part of the blog post, I will help you understand the career of data scientists in a data science way.
Start from an everyday observation
Let’s start from an everyday observation from my personal experiences.
Most of the individuals I encountered from school and internship who wants to become data scientists, mentioned to me that they would like to have a happier career, maybe getting swarmed by software bugs the whole day is not the best thing. But what does the data say? I proceeded to collect the StackOverflow’s developer survey from 2011–2018 to help us understand the truth.
Does a data science role return you a happy career?
Below is the survey question that I was particularly interested in.
The choice of each respondent is recorded in a CSV file as strings. But that was is the best for understanding the distribution of career satisfaction — it has to be in a format that could be easily aggregated. Therefore, I mapped these choices into 3 categories, happy, neutral, and unhappy.
And there come those individuals who just left this question blank. Now we have to think about the reasons why they left the question blank. Most likely it’s because they don’t know how to answer them — which means they don’t have an opinion, and that falls in our “neutral” category.
Now we are ready to find whether data scientists are really “more” satisfied with their jobs. All we need is a benchmark to compare to, so I separated the data into “data scientists” and “other software engineers”. In data science, it is very similar to an A/B test. Here is the finding expressed in a pie chart format below.
What does this mean? Out of the total data scientist surveyed in 2018,
- 56% of them are happy with their jobs, more than the 47% happy population for software engineers.
- However, the 18% of data scientists who are not happy with their jobs are also more than 16% software engineers.
Yes, data scientists, in general, are more likely to have clear opinions about their careers compared to software engineers — You are either happy with your data science career, or you are not.
Does a data science role lead to a healthier lifestyle?
Now we know data scientists are generally on “both sides of the happiness stick”, we would like to verify if data scientists are really living a healthier lifestyle. Here we naively quantify “healthier lifestyles” as “Hours spent on computers” and “meals skipped” survey questions. Obviously, we want less time spent on computers and fewer meals skipped.
While most of the data scientists and software engineers spent 9–12 hours on computers every day, we surprisingly see the percentage of data scientists spent over 12 hours on computers is almost 5% more than software engineers.
Umm… that’s not very “healthy”.
Looking at the meals skipped analysis, data scientists are doing significantly worse than software engineers — though most of the data scientists and software engineers never skip meals, the majority of those who do are data scientists.
Well, that was a little surprising. I thought software engineers should be working on the majority of the software implementations — thus longer hours should be spent on coding and debugging using computers, leading to more meals skipped.
Not quite. My developer friend who worked for LinkedIn told me about his benefits from his personal training program and frequently stand-up meetings that free him from the computer screen. Meanwhile, data scientists might not be writing codes as extensively as software engineers, they spent a lot of time thinking about business problems, researching shreds of evidence, and writing blog posts (like I am doing here), which leads to longer time spent on computers and more meals skipped.
Sorry, no“ healthier lives” for data scientists.
Do data scientists get paid higher salaries for skipping more meals?
Let’s say, even after I told you all these drawbacks of data science careers, you still want in (like I do). Will you earn a higher salary for doing these hard works? Well, yes… and no. It depends on where you live.
Depends on the job market in different parts of the world, data scientists might have the potential to earn a higher paycheck. In countries where the AI industry is more prominent, such as the US and Canada, data scientists are paid better due to their solid knowledge in machine learning. Emerging AI markets, such as China and Japan, are still learning the true definitions of the data scientist role.
Though most likely to be changed in the future, the US is still the best place to work if you want to be a data scientist.
What skills are required to become a data scientist?
I get it. You still want to become a data scientist. What skills do you need? Let’s discuss the technical aspects first — programming. Well, is the database language SQL the only language you need to know?
Yes, SQL is very important, and it still is. But other languages are catching up rapidly. The general sentiment is that Python will replace SQL to be the most prominent language in data science in the future. But here is the most important finding— data scientists are becoming increasingly more versatile. Each of the 7 languages examined represent around 20% of the total users.
So, become a generalist soon!
Besides programming, communication skills are also integral parts of becoming a brilliant data scientist. What is the best way to communicate with the world? Writing blog posts. Okay, maybe that is too much for you. What is the second-best way? Maybe it is contributing to open source projects.
Yes, data scientists are more likely to contribute to open sources than software engineers. Open source projects could be anything ranging from a popular software package to important documentation for a project. Working on these projects will slowly build a strong brand for you as a data scientist.
So, open your GitHub and start contributing!
What you should know if you have made it this far
So, yeah that wasn’t as short as you hoped for. Thank you for making it this far! So let’s do a small recap of what was discussed in this post.
Remember I said about the differences between data science and software engineering?
I started with a question I want to answer, then I collected the relevant StackOverflow survey data to use as the knowledge base. I briefly described the method of transforming data into usable formats — Only then I started to find patterns. That means you should always try to have a question before you collect the data. Most of the time, the dataset is simply too big for you to explore everything.
What are data science careers really like?
Data scientists work hard — that might mean staring at the computer screen for longer times, skipping more meals. But in the end, data scientists do get paid really well, if not better than software engineers. In the end, data scientists will love their jobs.
What skills do we need to become a data scientist?
A combo of versatile technical skills and coherent communication skills. Learn to become skilled in multiple technologies and a frequent open source contributor will lead to a fulfilling data science career.
That’s what I believe you should know about data science careers — in a data science way. If you are interested in reading more about the analysis performed in this project, you could head to my notebook viewer and GitHub page.
And remember to discuss AI with me, because I am 25 years old and happened to have a positive view towards AI.
Join me on the journey of becoming a data scientist! In the next blog post, we will have a little fun and try to predict a person’s career satisfaction using machine learning techniques.