The Data Scientist Job Outlook

Data science is not a new field, but it is certainly one that is growing in popularity. There are many different data science careers!

If we are to define data science, we might do so as follows: it is an interdisciplinary area of scientific processes, methods, systems, and algorithms to obtain knowledge or awareness of data in an array of forms.

The field of data science combines data analysis with statistics and machine learning. The methods associated with each of these fields are used to gain an understanding and evaluate data.

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In other words, data science uses theories and procedures drawn from a variety of fields. Data science professionals are analytical data experts who possess technical abilities to solve multifaceted problems.

Data science practitioners take a large number of complicated data points, both structured and unstructured, and use their abilities to clean and organize those data points. They do so in a variety of contexts.

For example, a data scientist might solve business challenges by applying their analytic skills to big data. They might use their:

industry knowledge
contextual understanding
doubt of current assumptions

You might be interested in the job outlook for data scientists. After all, if you’re in college and thinking about your future, you want to select a career that you’re passionate about and that will provide you with long-term stability.

The passion part is up to you. But in terms of the data science job outlook, there is a surge in the need for skilled workers in this field. The data scientist job growth projections are positive according to the Bureau of Labor Statistics, and that’s very good news for you!

A Data Scientist’s Job Duties

Data science experts conduct independent research. They analyze extra-large volumes of data from various sources. The source of the data can be internal or external.

Internal data is information that is derived in-house. So, if you work for a healthcare company, an example of internal data might be reported outcomes of specific elective procedures performed by doctors in the network. Likewise, internal data might come from:

company software
databases
reports generated from patient feedback

External data, on the other hand, comes from sources outside of your organization. External data runs the gamut from census data to information generated from a Facebook campaign. Here’s an example of a data scientist using external data:

Let’s suppose you work for a package delivery service. Your job is to ensure that packages are delivered on time. To do that, you examine weather data to see where weather disruptions might occur. By mining that data, you can make adjustments to how packages are processed, where they are sent in transport, and the delivery dates the company promises its customers.

To assess and interpret internal and external data, data scientists implement:

advanced analytics programs
statistical methods
machine learning

When examining data, data scientists thoroughly clean and condense the information to discard anything irrelevant to the task.

So, in our package delivery example from above, a data scientist would eliminate data that isn’t needed for accounting for bad weather. If the period in question is in May, data about package delays in December could be eliminated to streamline the final data set.

Additionally, data scientists look different things in their data including:

trends
opportunities
hidden weaknesses

Using the same example as above, a data scientist might look for weather patterns that commonly occur in May of each year. Is there a pattern of getting a heavy, wet snowstorm each year? If so, the data derived from the analysis could lead to the development of policies and procedures that help minimize shipping delays by rerouting packages during that time of year.

Data scientists also communicate their findings to management. A data scientist might recommend cost-effective modifications to current strategies and procedures. After all, examining the data is not enough. Instead, data scientists should be prepared to explain what they learn to various stakeholders, who can then take action based on the data presented to them.

Education and Skills for Data Scientists

There are quite a few pathways to becoming a data scientist.

Believe it or not, there are data scientist jobs that don’t require a degree. If you possess excellent math and programming skills, you might be able to get your foot in the door. This is rare, so you shouldn’t bet on landing a high-paying data science job without a degree of some sort.

Speaking of degrees, some data science professionals have a bachelor’s degree in a different field. This usually takes the form of a degree in:

Math
Programming
Information technology
Data analysis
Machine learning
Software engineering

If you have a degree in one of these (or another closely related field), making the transition to data science will likely be easier if you have no degree at all.

Of course, getting an undergraduate degree in data science is perhaps the best route to take to start your career in this field.

Typically, a bachelor’s degree in data science includes about 120 semester credit hours of study. You can expect to take about four years to complete a degree program, though you could finish faster if you take summer courses.

Like many job fields, having a bachelor’s degree often means you qualify for entry-level or mid-level positions. However, there is a shortage of data science professionals, and their skill set is in high demand. There are instances in which a bachelor’s degree could help you land a much more lucrative position. Again, this is the exception and not the rule.

To maximize your career options, a master’s degree in data science or a closely related area is recommended. The data scientist outlook for employment with a master’s degree is favorable.

Where a bachelor’s degree focuses on broad topics, a master’s in data science degree program provides a thorough understanding of the field. Master’s studies usually include opportunities for internships and networking activities as well. Not only do internships provide a unique learning experience, but they also help you apply what you already know to real-world situations.

What’s more, students who participate in an internship can make connections with people in the industry. Internships allow students to gain experience as part of a data science team. Those connections can come in handy after you graduate and begin your job search.

Aspiring data science professionals can earn professional certification to remain competitive in the field. Some data science positions require certification. Common certifications for data scientists include the Certified Analytics Professional and Cloudera Certified Professional: CCP Data Engineer.

In both instances, these certifications require applicants to meet a very high standard of knowledge and skills. These skills are tested in rigorous certification exams.

Skills You’ll Need

In addition to high-quality education, data science jobs require a variety of technical skills. This includes:

Programming skills
Coding and computer science skills
Statistical and mathematical skills
Ability to deploy machine learning
Ability to use artificial intelligence in data mining and analysis
Data analysis skills
Computer and information research skills

Likewise, a data analyst should possess the following non-technical skills:

Critical thinking
Analytical skills
Problem-solving skills
Intellectual curiosity
Effective written and verbal communication
Sound knowledge of the industry in which you intend to work

The skills listed above are just a few examples. Many other skills would be valuable to you as a data analyst.

Again, the more education you get, the more data science skills you will possess, particularly of a technical nature. As noted earlier, it is possible to enter this career field with little or no formal education. However, you will vastly improve your chances of becoming a data scientist by getting at least a bachelor’s degree from an accredited institution.

Data Scientist Job Outlook

According to the United States Bureau of Labor Statistics (BLS) Occupational Outlook Handbook, the data science job market is expected to grow by 15 percent through 2029. This is deemed much faster than the average for all professions. The total expected job growth for the same period for all occupations is just 4 percent.

What’s driving the need for more data scientists? Data science job growth can be partially attributed to the rapid adoption of data analysis by various businesses and industries. Data-driven decision-making is helping businesses:

improve productivity
streamline operations
reduce costs

These results highly attractive to businesses in a wide range of industries!

As demand for more and more data grows, so too does the need for qualified data science professionals who understand big data. From writing computer algorithms to mining data, companies rely on data scientists now more than ever to help inform stakeholders about how to move the company forward.

But it isn’t just data scientists that are expected to be in high demand in the coming years. The data science career outlook for workers with a data science education and work history is also excellent! This group of professionals might consider other lucrative options, working as:

Machine learning engineers
Enterprise architects
Infrastructure architects
Data engineers

Each of these careers (and many others like them) offer an excellent salary in addition to being in-demand options. The four careers listed above each have an average yearly wage of more than $100,000 per year. That means that for many data science careers, you’ll find that they are both well-paying and in high demand. That’s precisely what you want as a college graduate!

It should be noted that to increase data science job prospects, even more, data scientists should have a good working knowledge of the specific industry or industries in which they wish to work.

So, if data science in healthcare is your passion, consider a double-major in data science and business. At the very least, minor in business and do an internship where you can put your data science skills to work in a business environment. Whatever your area of interest is, this is a good path to take. And while some work in the field of data science will be taken over by computers in the coming years, there should still be rapid growth of jobs in this sector.

Salary for Data Scientists

The ever-increasing demand for data scientists is a good thing when it comes to salary. As businesses and other organizations have a greater need for well-qualified data scientists, they are presenting applicants with competitive starting and continuing salaries.

Evidence of this is seen in the most recent BLS data scientist figures. The median salary for a computer scientist was $122,840 in 2019. This is a whopping $83,030 higher than the median annual salary for all occupations.

The pay range for data science jobs also points to impressive salaries. Data science BLS figures show the lowest ten percent of earners still make nearly $70,000 per year. The top ten percent of earners make nearly $190,000 per year.

In examining this salary data, you must keep one thing in mind – the salaries offered to job applicants vary widely. That is, just because you’re a data scientist doesn’t mean you’ll make $190,000 per year. It doesn’t mean you’ll make the median wage of $122,840, either.

If you’re just out of college with a bachelor’s degree, and little or no work experience, you can expect to be on the lower end of the pay scale. In reality, if you have a master’s degree and little or no work experience, you might be on the bottom of the pay scale as well.

But as you gain experience, your ability to command a higher salary as a data scientist improves greatly. You’ll improve your salary standing with:

additional experience
training
new skills

There are other factors at play regarding salary for a data scientist. The specific location of employment certainly matters, as do the duties that are assigned to you in your specific position. In some cases, the geographic location in which you work can influence the data science outlook and how much money you earn.

Is the Data Scientist Career Path the Right Choice for You?

A data scientist is vital to a variety of organizations because they are part computer scientist, mathematician, and trend spotter.

The increasing popularity of this profession is a reflection of how entities think about big data. Data scientists help increase revenue and discover business insights to boost production. For any organization seeking to enrich its business and become more data-driven, a data scientist could be the answer.

So, should you become a data scientist? That depends on you! If data science interests you, a career in this field could be a very good fit. And as we discussed above, data science jobs can result in a very nice payday for you as well.

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