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How is the Job Outlook for Data Scientists?

Data Science has a promising outlook for the future.Data science is not a new field, but it is certainly one that is growing in popularity.

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

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

Data scientists 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, including industry knowledge, contextual understanding, and doubt of current assumptions. This is but one of innumerable examples of what a data scientist does.

Individuals interested in working in the data science field often wonder about 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 job outlook for data scientists, there is a surge in need for skilled workers in this field. That’s very good news for you!

Job Duties of Data Scientists

Data scientists 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, and 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, and machine learning to prepare it for use in modeling. When examining data, data scientists thoroughly clean and condense the information to discard anything that is 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 of time in question is in May, data about package delays in December could be eliminated to streamline the final data set.

Additionally, data scientists look for trends, opportunities, and hidden weaknesses within the data. 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 and 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

In addition to high-quality education, data scientists need a variety of technical skills.There are quite a few pathways to become a data scientist.

In some cases, you might be able to enter this field without a data science 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 scientists 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 scientists and a very high demand for them, so 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.

Where a bachelor’s degree focuses on broad topics, a master’s 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, participating in an internship can result in making connections with people in the industry. Those connections can come in handy after you graduate and begin your job search.

Aspiring data scientists also commonly gain professional certification to remain competitive in the field. 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.

In addition to high-quality education, data scientists need a variety of technical skills. This includes:

  • Programming skills
  • Coding skills
  • Statistical and mathematical skills
  • Ability to deploy machine learning
  • Ability to use artificial intelligence in data mining and analysis

Likewise, data scientists 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 technical and non-technical skills listed above are just a few examples. There are many other skills that would be valuable to you as a data scientist.

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.

Job Outlook for Data Scientists

According to the United States Bureau of Labor Statistics (BLS), the employment of all computer and information research scientists is expected to grow by 15 percent through 2029. This is deemed much faster than the average for all professions. In fact, the total expected job growth for the same period for all occupations is just 4 percent.

What is driving the need for more data scientists is the rapid adoption of data analysis by various businesses and industries. Data-driven decision-making is helping businesses improve production, streamline operations, and reduce costs, all of which are 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 scientists. 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. Workers with a data science education and work history might consider other lucrative options, such as:

  • Machine learning engineer
  • Enterprise architect
  • Infrastructure architect
  • Data engineer

Each of these careers (and many others like them) offer an excellent salary in addition to being in-demand options. In fact, 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 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 double-majoring 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

Data Scientists earn a higher salary than the national average.er salarThe 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 data from the BLS: the median annual salary for computer scientists was $122,840 in 2019. This is a whopping $83,030 higher than the median annual salary for all occupations.

The pay range for careers in this field also points to impressive salaries. 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. In fact, it doesn’t mean you’ll make the median wage of $122,840, either.

If you’re just out of college, have just 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 improves greatly. Combined with that additional experience, the training you get and the skills you develop along the way will also help you improve your salary standing.

There are other factors at play regarding salary. 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 how much money you earn as a data scientist.

Should You Become a Data Scientist?

Data scientists are 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, data scientists are the answer.

So, should you become one? 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, it could also be a job that results in a very nice payday for you as well.

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