If you had a time machine, it would be a cinch to pick a career. You could go to the 1980s and be a part of the personal computing revolution. Or you could help light the world in the late 1800s as an electrical engineer. 

But you don’t have a time machine. Or a crystal ball. Or a genie in a bottle. Fortunately, you don’t need them in order to make an educated career choice. Why? There’s one obvious technological revolution everyone agrees on: In the 21st century, everyone needs a data analyst.

Data analysis, according to Forbes, is the “new oil.” In other words, data is the commodity that will define this century. Ditto, says The Economist: “The world’s most valuable resource is no longer oil, but data.” 

Having a background in the world’s most valuable commodity means you’ll never have to worry about a paycheck again. Might a career in “new oil” be the best path for you? Let’s take a look.

What Does a Data Analyst Do?

Read any data analyst job description, and you might think it sounds highly technical and challenging. Because data analysis is the process of inspecting, modeling, and mapping out vast swaths of information, these jobs often sound like long days of staring at a computer screen. And that’s not wrong. 

But data analysis also requires creative thinking, ingenuity, and problem-solving. If you like puzzles, there’s a decent chance you’ll also like being a data analyst. Let’s zoom in on the individual duties you’ll have:

Data Collection

There’s nothing to analyze if you can’t collect enough data to work from. It starts with lassoing in the data that drives business decisions. Surveys, website analytics, customer behavior, customer observations, financial data—these are all the raw materials that arrive for processing. 

This is often the role of a different type of data specialist (more on that a little later), but you may have tasks that overlap with data collection.

Database Management

If you work in data, databases are your new home. If you were to work in oil and gas, your job would really be to monitor and maintain oil drills and similar technology. In data, it’s often the same. The databases handle the data, but you have to know how to fix coding errors, review and modify data, or translate databases into actionable material that other departments in your business can use.

Data Processing

Just as wheat doesn’t become bread without a few steps, you can’t work with data until it’s in an acceptable, malleable format. Tools like Excel or programming languages like Python and SQL are the big names here.

Modeling, Algorithms, and Creativity

Data analysis can sometimes feel like forecasting the weather. Your job is to take oftentimes overwhelming amounts of data and turn that into actionable insights. That’s where creativity comes in. You have to spot patterns in the data—or, failing that, come up with original ways to look for patterns in the data.


You might be a brilliant mathematician or statistical scientist, but you won’t understand how to become a data analyst until you know how to share the analysis you gather. If “data” is a raw material like oil, then think of communication as your finished, refined product: the insights to share with the decision-makers in your business.

How to Become a Data Analyst

Degree and Education Requirements

What’s the best degree to get if you want to guarantee yourself a job in data? A computer science degree? A data science degree? A mathematics degree or a statistics degree? Is there even such thing as a data analytics degree?

There’s no single ideal answer here, though you’ll do best with a bachelor’s degree in data science, information technology, or statistical analysis. A math degree will likely be acceptable if you have the technical skills to suit the position. 

The skills you bring from previous jobs or through specific certifications are equally as important as your formal education.


A certification in Python demonstrates you have the basic skills to handle some of the most universal programming concepts in the modern data world. Even the Python Institute notes that it’s ideal for someone looking to get into data analysis. Python is especially useful for creating visualizations that C-level executives in business can use to turn data into business decisions.


Structured Query Language (SQL) is the “language” of data. Why? It’s typically the programming language preferred by data administrators who manage mass quantities of data. Speaking this language will help you in database modification as well as adding, uploading, and deleting data.

Microsoft Excel

Laypeople might recognize Microsoft Excel as rows and columns for tracking a fantasy football league. In business, it’s so much more. Rather than raw data, Excel helps you modify data and process it through pre-defined parameters with a few clicks. This helps you “lasso” and process data quickly, automating the hard manual labor so you can skip to the creative work of figuring out what to do with all of your data.

Brush Up Your Excel Skills

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The Average Data Analyst Salary

In a field as big as data, there are bound to be a lot of different salaries. So, let’s look at some…well, data about data.

Glassdoor estimates the average data analyst salary is about $76,000 with “total pay” factored in. But there’s a big range—data analysts can make anywhere from $35,000 to $150,000 depending on experience, qualifications, title, and location. 

What Data Analyst Jobs Are Currently Available?

At many companies, the role of “data analyst” will typically be organized by rank—junior analyst, senior analyst, senior analyst II, and so on. These are especially useful for helping standardize the pay grade of people with specific data analysis experience.

However, you’ll also see a few other types of jobs under the data umbrella, including:

  • Data engineers, who focus on building the infrastructure to handle large sets of data, such as visualization with Python
  • Data scientists, who specialize in collecting and organizing unstructured data using machine learning and other advanced techniques
  • Business intelligence analysts, a type of data analyst who focuses on patterns and key performance indicators (KPIs) that drive business results
  • Quantitative analysts, who are especially adept at discovering financial opportunities in raw data
  • Freelance data analysts are the same as data analysts, with the only difference being that they work on a contract or per-gig basis

Other jobs, like marketing and sales analysts/engineers, also have their toes in the data analysis waters. 

Remote Data Analyst Jobs

There’s good news and bad news, and they both revolve around this fact: Working with data often means spending time in front of a computer.

The bad news? It might not help out the ol’ posture. The good news? It’s easy to be a remote data analyst—all you need is an internet connection and someone willing to pay you for your skills in languages like SQL or Python.

Freelance Data Analyst Jobs

Although some companies like to hire in-house data analysts so they can build familiarity with the company’s need for specific types of data, there’s no reason you can’t work freelance, either. 

Smaller companies might not have the budget for a full-time data analyst but still may need your services. Other companies might supplement their analyst staffing during busy seasons. According to Springboard, many of these freelance data analysts can even make more money freelancing than through full-time work.

The Difference Between a Data Analyst and a Data Scientist

To put it simply, a data scientist can specialize in gathering data, while a data analyst’s job is to interpret and organize that data. And don’t be surprised to see some positions where the two jobs overlap, either.

Fortunately, there are a few rules of thumb you can use to understand which position fulfills which role:

  • Data analysts have the task of interpreting and organizing data that already exists. This is one reason freelance data analysts are so common. They’re able to onboard with a company and work with existing data, using their skills to organize that data and turn it into insights.
  • Data scientists are the ones in search of data. They think about the best ways to obtain high-quality, meaningful data. Just like a scientist in a lab looks for new insights by designing experiments and gathering results, scientists of data in business seek out knowledge. There’s a surprising amount of creative, outside-the-box thinking required here. You have to be willing to embrace new avenues of data gathering, such as machine learning and advanced programming.

The Art (and Career) of Turning Data Into Decisions

To outsiders, data analysis can sound challenging, mathematical, and highly left-brained. But anyone who’s ever worked with data knows that it requires all sorts of know-how to make sense of the noise. 

It’s not just a set of highly technical skills, like learning Excel. Creative skills are just as important, such as finding patterns, identifying commonalities, and turning dry, rote statistics into meaningful business insights. 

Data may be the most precious commodity in the 21st century. If you want a career in numbers, it’s going to be worth your while to learn how to refine it.

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Written By

Dan Kenitz

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