3 Key Things I Learned in My First Data Analyst Job
Recruiters ask for too many skills, applicants get overwhelmed when they see the list of requirements they have to fulfill. But does one need to fulfill them all? And which skills are essential to get an entry job in Data Analytics?
I joined the Data world two years ago, overwhelmed by the requirements just like anyone else, but I made it and I learned 3 things.
1. You need solid SQL skills
At first, I thought I had to be really good in R, and Python, so I went deeply into both of them. That cost me a lot of time because I had no prior coding experience.
I did not want to start with SQL. R is sexy. Python is sexy. SQL is not. You will not build models in SQL, duh? Then, why learn?
SQL can be painful, but it’s essential. You have to query the data, clean it, and manipulate it most of the time in SQL. If you join a startup, you will most probably not build crazy stuff right away and will have to stick to the basics.
2. Communicate clearly
I had no idea what people meant when they were saying, you need good communication skills as a data analyst. I learned to talk when I was two, and I think I got better at communicating after 12 years in high school, and 4 years in university. I am human, wasn’t I made to communicate?
Oh well. I got it now.
Working in a technical field changes the playground rules. You speak a language that not so many understand. When you work in your own box, it’s easy to assume that everyone will understand you.
The learning curve is lying to you. Be simple.
Imagine you work on a project alone or maybe with one more teammate. You have to dig deep into it in order to understand each technical side of the problem. Now you have to translate the project value to the business stakeholders. One thing you must remember— they lack the knowledge you have. They did not dig into it, and they do not want to. This is your job.
Tell them a story, instead of explaining things. They want to understand the value, not what you did.
Be humble, remember it took you a while to understand the problem yourself.
3. Get your statistical basics right
You can start learning how to build ML models, but it would be not efficient. I’m not only talking about time efficiency but also about knowledge efficiency.
If you do not understand what you’re doing, you’re in the wrong direction. Also, most of the time you will not need it. Don’t venture into complexity before you master the basics. If you do not know what I mean, then I will give you an example.
Don’t venture into neural networks if you do not understand trees.
Don’t venture into tress if you do not understand linear regression.
Don’t venture into the big world, without understanding the small world.
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