Four Habits of a Good Data Analyst

Data Science Skills

If you ask any seasoned data analyst, they would tell you that there are some tricks of the trade to help you improve or maintain the efficiency of your processes. When you find yourself sifting through endless sets of data during the day, you’ll be thankful to have a few good habits in your back pocket. Here are for key habits to practice as a good data analyst:


1. Value more familiar tools over “fancy” tools

The latest tools are not always the greatest, and sometimes the best decision is to stick to the tried-and-true. A good data analyst will value tools that they are familiar with because in the end, this will make you much more efficient. If you are constantly needing to purchase, learn, and adapt to new tools, there is a lot of room for wasted time (and money). Efficiency is essential in data analytics, and like the old saying goes why fix it, if it isn’t broken, right?


This is not to say that you should find a tool and stick with it forever—just be smart about your upgrades. Always make sure you are choosing quality tools, but also taking the time to learn them properly. Even when you’re forced to jump right into an important project using a new software, takes a little extra time to learn the ins and outs  so you avoid any critical mistakes from the beginning.


2. Think ‘simplicity’ over ‘complicated’ when it comes to algorithms


As a data scientist, you’ll have to get used to the idea that not everyone in the office is going to know what you’re talking about. You should be able to simply and easily explain even the most complicated things in a way your entire team can understand. Many times even your project manager won’t be able to decipher your data jargon, so be sure to keep this in mind for all your projects. There’s little to no value in an analysis that doesn’t make enough sense to put the plan into action.


3. Consider more sources over more data


Many times, you’ll get a better idea of how consistent a set of data is by finding  additional sources, versus finding additional data from the same source. If you spend a good portion of time only limiting yourself to one source of data, you might not be seeing the whole picture. Tap into your resources to pull more data and you’re likely to gain greater insight on the situation.


4. Make sure you know how the software works

Instead of just assuming you know how to use a given software, make sure you’re taking advantage of all the features it has to offer. The software is designed to make your job easier, after all.

Data science is a complex field, but with these four habits, you can help make your next big project a little easier.


If you’re an aspiring data analyst and want to know more about how you can form great habits of your own, learn more about our program here or give our admissions team a call at (202) 803-5112.


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