Ridiculous Rules About DATA ANALYST

13 11
13 11

It’s the year 2014, and we still live under ridiculous rules about “Data Analyst” that were made up in the 1940s.

Seriously, what is up with that? It has never been more important to have a data analyst on your team than it is right now for syn stock forecast. That’s why we’re taking this opportunity to clear up some of these misconceptions about data analysis and make sure you’re not making any of these mistakes as a Data Analyst:

1. You Don’t Need Degrees To Be A Data Analyst   

Unless you’re planning on being a professor, don’t go to graduate school for anything other than what you want to use your degree for. There’s more to being a data analyst than just having a degree, and many fields that don’t provide any form of education are nonetheless very important positions for the success of your companies.

If you do go to grad school, make sure you get invaluable experience in at least one data-related discipline so that you can feel confident in the quality of what you’re doing before starting a company. There are no prerequisites for data analysis; however, there are prerequisites for every other job out there. Your background is what matters most, not your degree.

2. Data Scientists Are Bad At Everything Else 

Data scientists are supposed to be the go-to people for every technical problem in business, and as such they have a reputation for being unable to communicate effectively with regular people. That’s not exactly true. It’s more likely that these people need to learn how to communicate effectively more than anything else. It’s the same reason why it’s easier for someone who graduated from computer science school to code because that person received training in how to code. Learning how to think strategically is what will separate you from other data analysts, and this is something everyone can learn at any point in their life.

3. Data Scientists Are Mutants  

This is just an extension of the last one, but a Data Analyst is not “a better version of everyone else” who can do things like “learn to code” and “understand business logic” and all these other things. The notion that they can pick up any skill and instantly be great at it is just not true. If you’re going to hire a data scientist, make sure they have a track record of success with the skills you want them to learn in the future. Otherwise, you might end up with someone who’s good at something that’s useless to what you need done.

4. Data Scientists Are People Who Understand Algorithms 

I mean, they’re supposed to be, but that’s a pretty watered-down definition. Sure, it’s true that a data scientist needs to understand algorithms to do well in their job, but that doesn’t mean the only thing they need to do is complete assignments and memorize them. That’s just one step towards getting an actual job and making money.

A real data scientist makes use of all of their skills as well as being familiar with something called “domain knowledge” which is about anything other than how to actually build algorithms. It’s a weird concept I know, but it’s the most important thing you can learn if you want to be a good data scientist.

5. Data Scientists Think Like Business People 

Similar to the last point, nobody is saying that someone who knows how to program isn’t going to be able to bring value to your company. However, when you see that data scientists need logic and creativity and problem-solving skills and all these other things, it often means that they don’t think like business people . That needs to be accounted for in their work flow.

Don’t hire a data scientist for an analysis and expect them to naturally “get” what you’re asking for unless it’s pretty obvious. That’s why it’s better to always have someone on hand (especially as a data analyst) that can communicate with business people on your team.

6. Data Scientists Are Magic People 

Although rare, there are some people out there who can do magic with their skills, and they can make you feel like you’re going to get a magic solution to all your problems when they do work for you. However, they’re the minority, and the majority of data scientists don’t have those talents. The “magical thinking” is just something that happens around them because of their skills rolling off into every area of their life.

The important thing to remember when you’re hiring a data scientist is that even though they’re very good at what they do, their success relies on the people around them not being bad at what they do. Data scientists are only as good as their support staff, and if your data analysts aren’t doing the best job possible, then your data scientists won’t be the best either.


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