Data Science Careers Shaping Our Future

The position of data scientist has been ranked as the top job in the United States by Glassdoor for the last four years in a row. In addition to this, the United States Bureau of Labor Statistics forecasts that employment in the field of data science will increase by 27.9 percent between now and the year 2026 as a direct result of the need for those with expertise in this area. Not only is there an extremely high demand for skilled data scientists, but there is also a discernible talent gap in the industry.

In an interview with Forbes, the managing editor of insideBIGDATA, Daniel Gutierrez, said that "the word on the street is there's clearly a lack of individuals who can perform data science." If you have an interest in computers, mathematics, and the process of finding answers via the examination of data, then getting a graduate degree in data science or data analytics could be the next logical step for you to take.

What exactly is the science of data?

Data science is utilized by "computing professionals who have the skills for collecting, shaping, storing, managing, and analyzing data [as] an important resource for organizations to allow for data-driven decision making," according to Martin Schedlbauer, PhD, a professor of data science at Northeastern University. Your purchases on Amazon, your Facebook feed, the suggestions that Netflix gives you, and even the face recognition that is necessary to login in to your phone are all examples of interactions with technology that incorporate data.

The online retail giant Amazon is a perfect illustration of how beneficial data collecting can be for the typical consumer. Amazon's data sets will remember what you've looked for, as well as what you've bought and how much you spent for it. Because of this, Amazon will be able to tailor future views of its homepage to better meet your requirements. For instance, if you do a search on Amazon for camping equipment, infant products, or groceries, you will not be inundated with advertisements or product suggestions for senior supplements. Instead, you are going to see goods that may genuinely be of use to you, such as a foldable high chair designed specifically for use when camping with babies.

Discover the Differences Between Data Science and Data Analytics by Reading This Article

In a similar vein, data science may be helpful in that it can remind you of purchases that you make often. If you get diapers on a monthly basis, for example, you may notice that around the same time each month, there is a coupon or promotion that has been cleverly placed. This application of data is intended to serve as a trigger, causing you to think something along the lines of "I just recalled that I need to purchase diapers, and I should get them now since they are on sale."

The use of data science is beneficial for both consumers and businesses. According to research conducted by the McKinsey Global Institute, the use of big data can boost a retailer's profit margin by as much as 60 percent. Additionally, "services enabled by personal-location data can allow consumers to capture $600 billion in economic surplus," which means that customers are able to buy a product or service for a price that is lower than they had anticipated. For instance, if you planned to spend $7,500 on a jacuzzi but ended up finding the same model for $6,000, you would have an economic surplus of $1,500. The use of data science may simultaneously boost the profitability of retailers and save customers money, which is a win-win situation for an economy that is healthy.

Why is the Study of Data So Important?

Although data science makes it possible for businesses to exert control over our shopping behaviors, the significance of collecting data goes far deeper than this.

Wearable trackers that encourage individuals to adopt better behaviors and that may warn people to potentially significant health conditions are one way in which data science can contribute to the improvement of public health. In addition, data may enhance diagnosis accuracy, expedite the search for remedies for certain illnesses, and even halt the spread of a virus. In 2014, when an epidemic of the Ebola virus occurred in West Africa, researchers were able to monitor the progression of the disease and identify the regions that were most likely to be affected by it. With the use of this statistics, public health authorities were able to get ahead of the outbreak and stop it from becoming a global pandemic.

The field of data science has important applications in almost every industry. For instance, data is used by farmers for the purpose of efficient food production and delivery, by food suppliers in order to reduce the amount of food that is wasted, and by charitable organizations in order to increase fundraising efforts and estimate the amount of money that will be required.

Steven Levitt, an economist and the author of the book Freakonomics, said in a lecture that he gave in 2015 that CEOs are aware that companies are losing out on the significance of Big Data, but they do not have the appropriate teams in place to do the tasks. According to what he has said, "I genuinely do feel still that the mix of cooperation with corporations' big data and randomization [...] is clearly going to be at the core of what economics is and what other social sciences are moving ahead."

Not only is a job in data science a good option because it is popular and pays well, but data may very well be the fulcrum on which the whole economy revolves. This is one reason why pursuing a career in data science is a smart move.

Jobs in Data Science That Are In High Demand

Experts in data science are required not just in the IT industry but also in practically every other industry. In point of fact, just one-half of one percent of the workforce in the United States is employed by the five largest technology companies: Google, Amazon, Apple, Microsoft, and Facebook. On the other hand, in order to compete for these well-paying and in-demand positions, an advanced degree is often necessary.

According to KDnuggets, a leading site on Big Data, "Data scientists are highly educated–88 percent have at least a master's degree, and 46 percent have PhDs. And while there are notable exceptions, a very strong educational background is usually required to develop the depth of knowledge that is necessary to be a data scientist."

The following is a list of some of the most prominent occupations in data science that need an advanced degree to enter.

1. Data Scientist

$117,212 is the Typical Annual Wage

Finding, cleaning, and organizing data for businesses are often required duties for this position. In order to uncover patterns that will be beneficial to an organization and assist drive strategic business choices, data scientists will need to be able to examine enormous volumes of complicated raw and processed information. Data scientists have a far deeper understanding of the underlying technology than data analysts do.

2. Engineer specializing on machine learning

$131,001 is the Typical Annual Wage

The majority of the time, the duties of a machine learning engineer consist of developing software solutions and creating data funnels. In most cases, they are required to have solid abilities in statistics and programming, in addition to having understanding of software engineering. In addition to creating and constructing machine learning systems, it is also the responsibility of these individuals to conduct tests and experiments in order to monitor the functioning and performance of such systems.

3. A Scientist Who Works with Machine Learning

The Typical Annual Wage is $137,053

Researching novel data methodologies and algorithms that may be implemented in adaptive systems, such as supervised, unsupervised, and deep learning methods, is often required of workers in jobs of this kind. Researchers that specialize in machine learning often go by titles such as Research Scientist or Research Engineer.

4. Application Architects and Developers

$129 000 is the Typical Annual Wage

Tracking the behavior of programs used inside an organization and how they interact with one other and with users is often one of the requirements for this kind of job. The design of the architecture of applications is another emphasis of the work of application architects, which also includes the construction of components such as the user interface and the infrastructure.

5. Chief Architect of the Enterprise

The Typical Annual Wage is $150,782

Typical Job Requirements: The primary responsibility of an enterprise architect is to ensure that an organization's strategy and the technology required to carry out its goals are aligned. In order to do this, it is necessary for them to have a comprehensive grasp of both the company and its technological demands. Only then can they develop the necessary systems architecture to suit those goals.

6. Architect of Data Systems

The Typical Annual Wage is $118 868

Typical job requirements include making certain that data solutions are constructed with performance in mind and designing analytics apps that may run on a variety of platforms. Data architects are responsible for a variety of tasks, including the development of new database systems, the enhancement of the functionality and performance of existing systems, and the facilitation of access for database administrators and analysts. In addition, data architects frequently look for new ways to create database systems.

7. Infrastructure Architect

The Typical Annual Wage is 127 676 dollars.

Typical job duties include ensuring that all of the company's systems are operating as efficiently as possible and are able to support the development of new technologies and system prerequisites. Cloud Infrastructure Architect is a comparable job title, and its primary responsibility is to supervise the cloud computing strategy of an organization.

8. Data Engineer

The Typical Annual Wage is $112,493

The typical requirements for this job include performing batch processing or real-time processing on the data that has been acquired and stored. In addition to this, data engineers are accountable for the construction and upkeep of data pipelines, which are responsible for the creation of a strong and linked data ecosystem inside an organization and the provision of information to data scientists.

9. Developer of Business Intelligence (BI) Systems

The Typical Annual Income Is $92,013

Job requirements often include designing and developing techniques to aid business users in swiftly locating the information they want in order to make better business choices. BI developers are responsible for this. They are very knowledgeable about data and make use of business intelligence (BI) technologies or design bespoke apps for business intelligence analysis in order to make it easier for end users to comprehend their systems.

10. Statistician

$88,989 is the Typical Annual Wage

Statisticians are responsible for collecting, analyzing, and interpreting data in order to find patterns and correlations that may be utilized to guide corporate decision-making. Typical job requirements for statisticians include the following: In addition, the day-to-day tasks of statisticians often involve the design of data gathering procedures, the communication of results to stakeholders, and the provision of strategic advice to organizations.

11. Data Analyst

The Typical Annual Wage is $69,517

Typical job requirements include transforming and manipulating massive data sets so that they are suitable for the analysis that firms want to do. In many businesses, the responsibilities of this function might also involve monitoring web analytics and doing A/B test analyses. Data analysts also contribute to the process of decision-making by generating reports for organizational leaders that effectively explain patterns and insights derived from their study. These reports are then used by organizational leaders in making decisions.

Data Scientists Are in High Demand Always and Everywhere.

"there is a definite need for experts who understand a business need, can create a data-oriented solution, and then execute that solution," Schedlbauer adds, despite the fact that some data science job will probably be automated within the next 10 years.

Experts in data science are required in practically every industry, from the security of the government to online dating applications. Big data is crucial to the success of millions of companies and government agencies, allowing them to provide superior service to their clientele. Careers in data science are in great demand, and there are no signs that this upward trend will reverse itself any time soon, if ever.

Getting a Foothold in the Industry

If you are interested in entering the profession of data science, there are a variety of things you can do to get yourself ready for the difficult and fascinating opportunities that are available in this industry. You will need to demonstrate your skills and past job experience in order to impress potential employers, which is maybe the most crucial thing you can do. You may acquire such abilities and experiences in a variety of ways, one of which is by enrolling in an advanced degree program in the subject matter that most interests you.

For example, the master's degree programs in data science and data analytics that are offered by Northeastern University are geared to enhance the abilities that businesses are looking for in prospective employees. In addition, students in both types of programs are given the chance to take part in internships, co-ops, and other types of experiential learning opportunities, which gives them the chance to get practical experience before they graduate. After you have thought about things like your personal history, hobbies, and professional ambitions, you will be able to figure out which degree program is the best fit for you and take the next step in accomplishing your objectives.