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Towards Data Science
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It may sound like the revenge of structured data, but it’s actually just a survey conducted by Kaggle Platform. The data of the 2019 Kaggle Machine Learning and Data Science Survey was made available here and used so we could listen to the stories they could tell us about data scientists.
These were the questions that guided us through this analysis:
And here are the stories they told us:
I know this first figure may look like bad news for you if you’re just getting started in the journey to become a data scientist. I don’t want to give you any spoiler, but if you feel disappointed now, go check the last section called Education x Salary and it may calm you down again.
Most of the data scientists who answered the survey has indeed a Master’s degree on their back. Still, it is very common that they attend online courses through popular platforms like Coursera, Udemy, DataCamp, Udacity, and many others.
It could indicate that, no matter what is your formal educational background, you will have to continuously seek knowledge, especially in this technological field that brings us news almost every single day.
When hearing about Data Science, it is inevitable to think about the hype of artificial intelligence and machine learning algorithms. But when it comes to a regular day in a data scientist’s life, would that be the main task?
Over 60% of them actually have to deal with data analysis instead of just worrying about what algorithm to use or finding a way to improve their models. Actually, the survey shows us that it doesn’t matter if you are a data scientist or a data engineer, data analysis will play a big role in your activities.
Also, we can see that almost 35% of the data scientists who participated in the survey performs activities related to data infrastructure. That shows how different roles in the Data Science field relate to one another.
Probably it would be no surprise if I would tell you that Python is the most popular programming language among data scientists. But what if I asked you what is the second language they use the most?
If your answer was SQL, you got it right! SQL, or Structured Query Language, is a simple but powerful language, and its commands can be grouped into four main functions: data definition language (DDL), data manipulation language (DML), data control language (DCL), and data query language (DQL).
It shows us that a great part of the work for a data scientist is to understand data, by executing queries in databases, transforming, manipulating, and analyzing them.
But language is just one of the tools that we can find in a data scientist’s toolbox. What about the most used databases and machine learning frameworks?
We can see that among these relational databases, MySQL is the most popular, although others like PostgreSQL and Microsoft SQL Server also seem to be valuable tools for data scientists.
If we talk about machine learning frameworks, Python’s popularity leads Scikit-Learn to be the most used framework, and it is followed by Keras, XGBoost, and TensorFlow.
You could be a data or business analyst, a data scientist, a data or database engineer. If you live in the United States, lucky of you. That is where you are more likely to get higher salaries.
For data scientists, we have special good news.
Among all these professions, data scientists seem to be the ones with higher amounts in their accounts at the end of the year.
However, this is not only about good news.
Even though we have a significant number of people who preferred not to declare their gender (and at this point we have to say that Kaggle allowed self-describing gender in case they didn’t relate to the male-female option), we can see that among those who declare themselves as one of these two genders, sadly but not surprisingly, women appear to get lower payments in comparison to men.
If you came here right from the first section, I’m here to calm you down: even though Doctoral and Master’s degrees apparently relate to better payments, it looks like no matter what your formal educational background is, there is plenty of space for you in the room if you dedicate yourself to learning the necessary tools and concepts.
We can see that salary differences among most of the different backgrounds are not that high, which means that even though it could be a challenging path, you can certainly find your way through Data Science.
Through this article, we came up with a big picture when it comes to better understand how it is like to be a data scientist in the real world, and here we have a quick resume on what we have seen so far:
After all these stories that data shared with us, have you already decided where to start your journey through Data Science?
And if you are already on that path, which new tool will you start learning today?
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Towards Data Science
Data Scientist | Reach out to me through social media: https://www.linkedin.com/in/evertonbin/ | https://evertonbin.netlify.app/
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