https://ift.tt/c8wTBfz The steps that led me to become a Senior Data Scientist in a multinational company Image by SpaceX on Unsplash ...
The steps that led me to become a Senior Data Scientist in a multinational company
You really want to work with data, right? But what really comes to your mind when you think of that?
The area has a wide variety of data-related jobs and a need for highly skilled people. It has already been said that data is the new oil, and that data scientist is the sexiest job of the 21st century. The media floods us with every remarkable advance of artificial intelligence.
And you want to be part of that? This guide will show you a little bit of reality and how to start a career in data.
1 — Understand which of the jobs you like the most
The main goal of any data team is to create data-based solutions to help the business. This is a quite broad and maybe understated objective, simply because there are many forms to achieve it:
- By creating dashboards that allow the upper management to make well-informed strategic decisions.
- Developing models capable of predicting complex variables, such as product pricing or customer propensity.
- Reducing the processing time for relevant information that might help the customer choose our product.
- Finding patterns that might indicate market tendencies, or some necessity of the public.
- And many other use cases that we can employ data-driven solutions to bring value to the business.
Usually, a data team has many roles: data scientist, data analyst, data engineer, and machine learning engineer are some of these roles.
This is the ecosystem of a data team, and one thing all these roles have in common is: they all need to understand really well their own activities, but also a little bit of the activities of the other roles. And all of them need to understand the business they are working on.
I need to emphasize that it is not as glamorous as it sounds. Most of the work is dealing with data in form of tables and graphs, and not using some kind of robot-making magic tool.
Now that you’ve chosen the role that you like the most, what you should do next?
2 — Study programming
The starting point is to learn programming.
Lately, I have been seeing many people saying that “it is not necessary to know programming to work with data”, and although the area is walking at a fast pace towards the codeless modeling, we still use programming skills for many other things.
If anyone asks me, I will say without hesitation: learn Python.
Its differential is that you can create the backend of a website, a game, a smartphone app, and many other things besides data manipulation. I have automated several processes, including interfaces with MS Office products and webpages, using Python.
And it is unavoidable: as the complexity of the solutions grows, the “standard” solutions stop making sense and we need to customize them. How? Coding. Creating new classes, functions, pipelines until the framework is adapted to our needs.
However, if you are interested in starting, do not let programming be a barrier. Learn the basics very well, things such as the main structures, functions, classes. You will have plenty of time to learn the rest better.
3 — Dive deep into the area you like…
I usually say that a 20h online course will not magically transform you into a data scientist (or analyst, or engineer). But I believe that this is a really good start if you didn’t have any previous contact with data.
Start with courses, participate in workshops and meetups, read about the subject. Suddenly you will be facing a whole map of what is inside the scope of your area. This is when you finally know what you need to focus on.
This is how I still do it! I usually read and talk a lot about data science, and every single time I face something I need to know better, I include it in a study list to check later.
Do not be satisfied with knowing which tool to use, but always try to find out how each of them works. With time you will become more and more specialized without even noticing.
Just beware of this trap: the data subject is too broad, and it feels that we need to understand half a million things from start. Breathe. Calm down. Tackle one small subject at a time.
4 — … but also know the other areas a little
As I said before, in the data ecosystem we need to be close to the other roles.
If you are a data engineer, you will need to understand the needs of the scientists, which tables are the most used by analysts, which features are created manually every time they are needed because they are not available on the data lake…
It’s no use beingsuperheroero in your little world if you don’t know your colleague’s work, and it is also no use trying to be a know-it-all. It is humanly impossible to be an expert in all areas, but it is extremely necessary to know at least the basics.
Here the tip is still on: online courses and free content can help you develop this wider foundation, without the need to go too deep in a subject.
5 — Create a portfolio that stands out
We face a lot of competition in the area, so you need something to stand you out!
For starters, my tip here is: build a portfolio.
Search the internet for cool problems and projects, create the solutions, and post on your GitHub or your kaggle. Show practical use cases that you understand andperform your role well.
Create study groups and solve cases and competitions together. Teach each other. These cases can go inside your portfolio and you may find good friends along the road.
Conclusion
Working with data is challenging, and requires constant learning. We need to keep studying and tracking what is new, besides learning about the businesses we are working with.
Even so, working with data is rewarding, and is cool to know that your work is positively affecting the business.
I hope this (not so) short guide helps you starting ystarter in data, or that at least it makes you understand it better.
5 tips to start a career in data was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
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