Data Science or Software Engineering? Both!

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After a little break from writing, I'm back to share a bit more about my learning journey. I might take these breaks from time to time for two main reasons: my full-time job, and my daughter who can be born any minute now (and I'm REALLY anxious and excited about it!) Soon, I'll be a dad-dev - hopefully, not a dead-dad-dev!

chris-liverani-dBI_My696Rk-unsplash.jpg Photo by Chris Liverani on Unsplash

It has always been difficult for me to choose something, regardless if it's the ice cream flavour or my career. However, when I found myself a daddy-to-be living in a foreign country with no family or friends around for any kind of support, I felt like I had to choose a clear path. After long hours researching, reading blog posts (like this!), doom-scrolling through Twitter, I came to a decision: I wanted to learn how to deal with data.

There's a lot of blog posts out there pontificating how data science is already a dead career, but I disagree. From my experience - specially now, dealing with a giant database of customers in a big company in the UK -, it feels like the need to access structured, clean data in a efficient manner, and fast enough to not become a burden for the company's operation is a must. And I say that from a user point of view, as my job relies heavily on accessing data swiftly.

It's a huge mistake to think that the abundance of data we have today is a delight for data scientists. It's definitely not. Although we have plenty of data to work today, a big portion of this data is incomplete, unstructured and, ultimately, useless. This makes the job of professionals dealing with data harder and harder. Every year, lots of technologies emerge, trying to cover the hardships of dealing with so much data and the professionals need to keep up with them.

You see, I'm referring to "professionals", generally, and not to data scientists. And that's where my thoughts have been wandering since May, when I chose to pay for the course-bundle of this amazing place called 365 Data Science . It seems like a lot of professionals, in multiple industries, need more and more to deal with data, so even if you don't want to be a data scientist, strictu sensu, you can incorporate these skills on your education. Therefore, I want to be a software engineer, but I want to be a software engineer with a strong set of skills on data, and that's my bet on how to make myself stand out in the job market. I have no idea if I'm right, but to be honest, I have a feeling. The reason I chose this course-bundle I mentioned is that it covers not only the basics of mathematics and statistics (and I constantly need a refresh on those topics), but it also covers softwares and languages, such as Tableau, PowerBI, SQL, Python, R, technologies such as machine learning using Tensorflow and so on.

That is my personal choice, of course. But, I also rely on a lot of free resources on the internet and, basically, with the correct amount of time and willpower, you can learn all these topics for free - there are plenty of YouTube videos on statistics and mathematics, for instance, let alone Python and all the other softwares I mentioned.

Hope it helps! :)

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