Data Scientist Profile

What is a Data Scientist?

As a data scientist, you’ll mine data, looking for answers to essential questions. For example, as a data scientist with a professional baseball team, you might be tasked with analyzing data on an opposing team’s hitters. As a data scientist for a financial firm, you may be expected to make forecasts about your company’s future economic performance.

If this sounds complex, it is. Businesses have extensive collections of data to assist in problem-solving, planning, and fueling future growth. All that data needs to be mined, sorted, and analyzed. Data scientists perform these functions, and are typically also required to present their findings to relevant stakeholders.

Additionally, data scientists:

• Look for patterns in data
• Use SQL, Python, SAS, and R to analyze data
• Develop models and algorithms to examine data
• Use machine learning to automate data analysis processes
• Use predictive analytics to hypothesize about future events
• Build data mining architectures

If you Google “what is data scientist jobs,” you’ll see that the above list of job duties is not exhaustive. Nevertheless, it gives you a basic idea of what data scientists do each day.

What is a Data Science Degree?

Data science degrees are available at every level of education, and in most cases, you’ll have a choice between online and in-person instruction.

A two-year associate’s in data science degree program can provide a basic foundation in the field through introductory courses on analytics, programming, predictive modeling, and more. Though this degree can offer a strong introduction to the field, you would likely need to earn a four-year bachelor’s degree in order to qualify for work as a data scientist.

A bachelor’s degree in data science will build on the foundational topics identified above with more narrowly focused areas of data science such as artificial intelligence, machine learning, game theory, and more.

If you’re interested in advancing to a leadership role or providing educational instruction in data science, you might consider earning an advanced degree. A master’s degree in data science will combine coursework in specialized areas of concentration—for instance, data science and financial analytics; or data science and healthcare informatics—with an original research undertaking or capstone project.

Let’s discuss these degrees in a little more detail.

Qualifications for a Data Scientist (Entry-Level)

The basic data scientist degree needed to get a job is a bachelor’s degree. You don’t necessarily need to major in data science, though. A related field like statistics or computer science can help you get your foot in the door, especially if you have some relevant experience in the data science field.

The typical bachelor’s degree in data science program:

• Requires the completion of 120 credits
• Takes a minimum of four years to complete
• Can be completed on-campus or online
• Provides introductory instruction on data science, data analytics, data visualization, etc.

While the bachelor’s degree is a basic threshold for work as a data scientist, you can actually begin fulfilling knowledge requirements before enrolling in your undergraduate program.

Many excellent online courses in data science can help you build essential skills independent of a degree program. For example, you might take an online course in Python to learn the basics. Since Python is commonly used for data analysis, it’s one of the best data science subjects you can study.

There are other subjects in data science worthy of your time before you begin a bachelor’s degree program:

• SQL
• R
• Java
• Machine learning
• Artificial intelligence
• Data visualization
• Big Data

There are dozens of more focused courses within each of these subjects. If you’re already familiar with data science, intermediate and advanced courses are available to challenge you and help you develop new skills.

These online courses can help you develop the critical skills required for entry-level jobs in data science. Hiring managers want workers with skills related to:

• Math
• Probability
• Statistics
• Programming languages (e.g., Python, SQL, R)
• Data visualization tools (e.g., Excel, Tableau)

In addition to refining these critical skills, completing some preliminary online courses can help you build a portfolio that showcases your abilities.

A portfolio is essential for landing a data science job. You can tell hiring managers about your skills, but the proof is in the pudding. A portfolio gives you solid evidence that you can do what you say you can do.

Master’s-Level Data Scientist Qualification

Perhaps the most common data science job requirements are a master’s degree and a specialization.

With a master’s degree, you have an opportunity to develop a deeper understanding of the topics you studied in your undergraduate degree program. Master’s studies also give you the chance to learn new skills and specialize in your areas of interest.

For example, let’s say you discovered a particular interest in data visualization during your bachelor’s degree studies. As a graduate student, you can get a degree in data visualization and focus specifically on:

• Understanding what questions to ask about data
• How to effectively use data visualization tools and techniques
• How to effectively communicate meaning about data to others

With this specialization, you’ll have an even greater skill set to offer businesses and organizations. The same goes for other master’s level specializations.

The typical master’s degree in data science program:

• Requires the completion of 30-60 credits
• Takes a minimum of 1-2 years
• Can be completed on-campus or online
• Focuses on specific areas of data science, like data visualization, data analytics, or big data

Master’s programs usually have academic requirements for admission including an undergraduate degree from an accredited institution and a minimum GPA threshold. It is typical for an advanced degree program to require at least a cumulative 2.5 GPA on a 4.0 scale.

Many master’s programs in data science require you to submit other documentation, like a portfolio, letters of recommendation, or a personal statement. Some universities further require graduate applicants to submit GRE scores.

How to Pursue a Career in Data Science

As noted earlier, completing a bachelor’s program in data science or a related field is your first step to becoming a data scientist. Some undergraduate programs include practicum or internship placements. If your program has no such requirement, it is advisable to seek out an internship placement on your own.

Internships are an excellent learning experience that give you real-world job experience while also allowing you the opportunity to learn from industry experts. There is good networking potential for future employment as well.

After your undergraduate studies, it’s wise to find entry-level employment in the field. Again, this is an opportunity to get relevant work experience and expand your skill set, even if you plan to continue your studies in an advanced degree program. You may actually find that some organizations are willing to help pay tuition for employees pursuing master’s degrees.

This brings us to the next step in your career development - completing a master’s program.

Many jobs in data science require highly developed skills in a specific area. A master’s degree is key for obtaining the knowledge and skills that jobs with high responsibilities (and high pay) require. This is true whether you want to be a big data scientist, a data engineer, specialize in machine learning, or something in between.