How to Become a Data Scientist  

There are 2.5 quintillion bytes of data created every day, and 90 percent of the data in the world was generated in the past two years alone.1 That means the amount of information we have access to—and can use to enhance the way we do business—is almost limitless. We just need data scientists to get there.

What Does a Data Scientist Do?

Data scientists build tools to gather, interpret, and analyze data so they and other teams within their organizations can draw conclusions from it. This, in turn, can help companies solve problems, predict activity, and understand how certain campaigns and strategies are performing. 

The data scientist role requires a broad variety of skills, including:

  • Computer programming
  • Data mining (discovering patterns in large data sets)
  • Machine learning (teaching machines to learn from data)
  • Database management
  • Data visualization

Data scientists also use a variety of tools and programming languages depending on the organization they work for and what their role entails. These can include MATLAB, SQL, Python, Hadoop, SAS, Java, Hive, Pig, and C++, among others.

Data Science vs. Data Analytics

Data scientists

write algorithms and build statistical models in order to properly gather and organize data for analysis. They build the tools that allow organizations to glean the information they need from data.

Data analysts

use the tools data scientists build to draw meaningful insights and solve organizational problems.

These roles often work alongside each other.

Data Science Careers

Almost every organization needs a data science team to help them leverage data. In fact, LinkedIn ranks data scientist as one of its top three emerging jobs of 2020.2

Not only is the data science career path growing, it’s well compensated. Advertised data scientist jobs pay an average of $105,000.3

In order to take advantage of these opportunities, you’ll need a well-rounded data science skill set. A master’s program like the University of Denver’s online Master of Data Science, which is designed for entrepreneurs and innovation-driven data scientists, will equip you with this crucial knowledge base in as few as 18 months.

Are You Ready to Join the Future of Data Science?

Learn more about the online Master of Data Science program from the University of Denver today.

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What Makes a Good Data Scientist?

There are a few traits—or “soft skills”—that make a good data scientist. These include:

Curiosity: A data scientist is always asking questions. What kind of data points will best suit the organization’s needs? How can that information be gathered as efficiently as possible? In order to do their job effectively, they’ll need to embrace and enjoy this curiosity.

Organization: 2.5 quintillion bytes of data might seem overwhelming. But with the proper organization, huge data sets can be managed and reduced to digestible “bites.” Streamlined tools and organized processes are almost as important to the job as data itself.

Persistence: Data scientists create the tools to analyze data from scratch—and it often takes several attempts to find the correct approach. They must always be reorganizing, reassessing, and re-starting, and maintain their dedication in the process.

Data Scientist Qualifications

There are a few qualifications you’ll need before applying to data science positions.

A Bachelor’s Degree

Most data science jobs ask that candidates have a bachelor’s degree. Majors like computer science, statistics, physics, mathematics, or economics will expose you to some of the topic areas you’ll be working with later on.

A Master’s in Data Science

While a master’s isn’t required to get a data science position, the advanced expertise can help you do your job better and more efficiently. It’s important to choose a master’s program that will cover subject matter you’ll be using on the job—like Python, data mining, machine learning and data visualization.

If your undergraduate degree wasn’t in data science or a similar field, it can also be helpful to find a program that offers “bridge” courses to get you up to date. The University of Denver’s online Master of Data Science program, for example, offers an introduction to Python and two math courses that you can take alongside your core course curriculum.

Work Experience Requirements

Beyond your on-the-job training, undergraduate and graduate coursework—particularly master’s degrees focused solely on data science—offers the opportunity to become comfortable with the types of problem sets and data management tools you’ll be using in a professional setting.

Internships can also be helpful. While researching schools to attend, be sure to review the career support services they have available. With the University of Denver’s program, you’ll be able to leverage career resources and a rich alumni network long after graduation. 

Why Choose DataScience@Denver?

If you’d like to start or advance your career in the fast-growing field of data science, the University of Denver’s online Master of Data Science program is one way to do so. Beyond allowing you to earn your degree without relocating, our program offers a variety of benefits.

  • Enroll without prior programming experience: As mentioned above, our program offers three “bridge” courses to help you bring your programming and math skills up to date. You will take these courses alongside your core coursework.
  • Develop in-demand skills: Our curriculum covers key topics like probability and statistics, Python software development, and data mining. We constantly update our course offerings to align with current industry needs.
  • Collaborate in real-time: With courses delivered through live, face-to-face video and an online platform that allows small breakout sessions, you’ll be able to work through topics, ask questions and interact with peers and faculty face to face in real time.

Get Started

Take the next step in joining an industry that’s growing quickly. Request information about DataScience@Denver today.

1 Forbes, 2018

2 Emerging Jobs Report, 2020

3 The Quant Crunch Report, 2017