How To Become An Awesome Data Scientist

Our modern world is (quite literally) run by data.

Big Data in particular has totally transformed almost every industry on the planet, algorithms crunch data on a millisecond to millisecond basis to handle everything we throw at our favorite apps and websites, and today’s modern technology is powered by the flood of data we have available at our fingertips thanks to our “always online” culture.

And while machines do a decent amount of the heavy lifting understanding, interpreting, and interfacing with the data that is available – mostly because there’s so much out there that trying to parse all of that alone as a single human being would be next to impossible – data scientists are responsible for digging even deeper to extrapolate information, relationships, and connections that change our world all the time.

Because data represents the next big “gold rush”, and because so many businesses are looking to gain competitive edges and advantages over others in their space with data, the data scientist career field has exploded in demand.

Should you become a data scientist?

It’s hard to imagine a better time in human history to make this kind of career choice!

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Is It a Good Idea to Become a Data Scientist?

There isn’t a single industry on the planet, nor aspect of modern life, really, that data analysis, data interpretation, and data science don’t come into play.

Think of all the time that you spend online and then think of all the time (aggregate) that nearly every human in the connected world spends online. Think of every decision every one of these individuals make, no matter how small it may seem on the surface – and then realize that every one of those decisions is trackable, can be analyzed, and can be interpreted to better understand why we do ANYTHING that we do.

The financial markets are heavily impacted by data science. The health and wellness industries are heavily impacted by data science. The technology industries (no surprise here) are heavily impacted by data science.

Every industry on the planet is looking for people that can help them better understand and interpret the raw data they now have available at their fingertips. And we’re not just talking about giant operations here, either. Even small businesses have access to Big Data that wasn’t ever available previously – and they want to compete globally with the “big boys” using this information as well.

Combine this global and nearly universal demand for professionals that can analyze, study, and extrapolate from Big Data with the fact that this industry – and this career field, really – is still in its infancy and you can see why so many people are getting ready to make the switch over there and why it is so lucrative).

What Kind of Education Do I Need to Become a Data Scientist?

The field of data science is really just starting to flush itself out, but as a baseline of education you’re going to need to focus significantly in the areas of mathematics and statistics – and that’s just dipping your toe in the water.

Regardless of how you position yourself as a data scientist later in your career you are never going to be in a situation where you’re not studying numbers, raw data, and statistics. This is the raw material of this industry and you’ll need to have as firm a grasp on this information (of the theory and the functional use of these disciplines) if you’re going to have any opportunity to succeed in this world.

Machine Learning A-Z: Hands-On Python & R In Data Science

Both of these fields are entirely pragmatic which gives those that have particularly analytic and systems-based mindsets a major advantage. But don’t fall into the trap of feeling like you are not naturally inclined to become a data scientist if you aren’t all that crazy about math or statistics. With brute force and discipline you can teach yourself – or learn – almost anything, including the more advanced areas of these fields.

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Some topics you definitely want to master before even becoming a data scientist are:

SQL for querying databases and Python, because Python is the number one language among data scientists.

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The Complete SQL Bootcamp

Another area you want to study is business, but not the “by the books” business lessons so much as the practical, down on the street, actually selling something aspects of business.

Analysts that can only extrapolate and analyze data in theory – operating in some kind of fantasy world that doesn’t exist in nose to nose situations – are always going to end up losing out to those that can combine the technical skills of analysis with the “soft skills” of business.

These are the kinds of skills that will teach you what to focus on during your analysis, what you should be prioritizing, and what you can all but discard when you are going through your data sets.

A lot of analysts and newbie data scientists get sucked into focusing on the more theoretical areas of this field that are exciting to them without ever thinking if there is some kind of practical application.

Don’t get into that trap.

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What Kinds of Career Paths Lead to Becoming a Data Scientist?

Honestly, as a data scientist the world is kind of your oyster when you want to pursue different career paths – and there are plenty of career paths that can lead you to becoming a data scientist as well.

Obviously, any type of analyst position that deals with crunching raw numbers and data is going to be a natural entry points into the career path that can lead to becoming a data scientist. At the same time, however, different types of researchers, different types of investigative journalists, and computer programmers alike can all lead you down a path towards becoming a data scientist.

However, the barrier to entry into some of the top companies can be quite high as they require a Masters or a PhD minimum.

Salespeople, entrepreneurs, investors, and those from completely disparate positions that have to do with data – or are willing to learn everything there is to learn about data and analysis – can start down this career path without a lot of impediment. That’s where MOOCs come into play. They have considerably levelled the playing field.

Data Science from Scratch: First Principles with Python

What Kind of Pay Can Data Scientists Expect?

A big attraction of becoming a data scientist has to be the fact that you will be working on important projects every single day, exciting new developments that quite literally helped to shape the world around you – and may shape not just today’s world but tomorrow’s in far into the future.

The idea of having even just a small hand in creating world changing technology, solutions, or products and services is usually more than enough to attract anyone that’s considering whether or not they want to go down this career path.

But should you become a data scientist for the money, too?

Well, there definitely is a lot of opportunity to earn a fantastic salary in this career field as of right now.

According to PayScale.com your average data scientist in the United States makes just over $91,000 at the entry level point.

A decent amount of relatively new data scientists make considerably more than that, and when you add in bonuses, profit-sharing potential, and a whole host of other compensation packages it isn’t at all entirely unreasonable for a data scientist to be making $200,000 or more in almost no time.

As we have stated a couple of times throughout this quick guide this is one of the most in demand career fields right now.

According to those same reports from PayScale.com, the majority of data scientists working right now (59%) have anywhere between one and four years of experience in this field. 15% have less than a single year of experience, 16% have between five and nine years of experience, and only 11% have more than 10 years of experience.

If you’re interested in taking the plunge and becoming a data scientist there is no time like the present to hit the ground running. Opportunities abound in this very lucrative career field.M