An M.S. in Data Analytics degree can prepare you for a rewarding career using technology techniques to identify, collect and analyze data. Besides acquiring experience in top programming languages, you will also gain practice with leading data analysis software and platforms. In addition, the curriculum provides development opportunities to meet employer demand for the soft skills typically required for many positions in data analytics. Thus, graduates are ready to hit the ground running with a complete and complementary set of abilities.
The data science industry is a growing, high-demand space and an essential business component for companies across industries. It improves business decision-making, makes accurate forecasts and projections, accurately assesses market conditions and opportunities, creates better customer experiences and solves critical problems. The U.S. Bureau of Labor Statistics (BLS) shows a projected job growth rate from 2020-30 of 22% for computer and information research scientists roles with a current median pay (as of October, 2021) of $126,830 per year.
Why Soft Skills Matter
As companies continue to invest heavily in artificial intelligence (AI) in order to extract insights for solving real-world problems for customers, data scientists who can avoid importing their own personal perspectives and biases into the equations are essential. However, doing that requires self-awareness, empathy and the ability to understand and be understood, among other soft skills.
In addition, baseline skills enable a professional to understand consumer needs, collaborate with executives and communicate with stakeholders. Professionals in this field must be able to listen, understand, communicate, present and persuade. Regardless of technical prowess, poor soft skills are an impediment to rising through the leadership ranks of organizations committed to applying data science.
What Are Baseline Skills?
Baseline requirement is a highly-requested skill for job listings across almost every industry, as are technical qualifications. These talents include soft skills. On average, one in three skills requested in job postings is a "baseline skill," and even in highly technical jobs, baseline skills account for one in four requested skills. The more the position requires a person to interact interdepartmentally and with constituents outside of the company, the more employers will emphasize baseline skills when hiring and promoting for these roles.
So which baseline skills matter most to your future as a data scientist? These seven are highly prized by employers today:
- Communication: Data scientists must be experts at extracting and analyzing data and then communicating their findings intra-departmentally and then inter-departmentally. The latter will often include non-technical personnel, so the data scientist should be an effective communicator who can convey concepts in laymen's terms and help raise data literacy in the organization. This valuable skill includes helping other professionals in understanding how data may provide the key to solving business problems or answering important questions. Communicating effectively in this regard helps to raise the profile of the data science group in the organization and its capabilities, leading to an increased ability to make an impact. For this reason, leaders in the data science group have a vested interest in promoting the capabilities of effective communicators.
- Writing: Much of the work in this field is communicated through writing, informally through emails and formally through reports and summations. Employers are serious about finding professionals with demonstrable success in producing clear, succinct and effective written materials during projects and in communicating findings.
- Research: Research is often needed before starting projects, and data analytics professionals are involved in this research. They may need to uncover facts about the business and industry to understand what organizational problems need to be solved and why. This process involves translating data into results that provide answers, solve problems and lead to innovations for the business.
- Teamwork and Collaboration: Working in independent silos is not how things get done in this field. Data scientists typically work in teams with project managers, software engineers, business executives and other personnel. Leadership often makes promotions after professionals have demonstrated the ability to work well in cross-collaborative roles and to contribute to desired objectives.
- Problem-Solving: Though problem-solving is not always an innate critical thinking aptitude, it is worth developing. Leading programs like Northwest Missouri State University's Master of Science (M.S.) in Data Analytics online program dedicate resources to improving this skill. Students learn to approach problems methodically, choosing suitable approaches to identify root issues, assumptions and paths to resolution. Practice outside of the pressure-cooker of an organization allows students to master problem-solving and demonstrate their successes to potential employers.
- Creativity: This is more than imaginative prowess or the power to create something new (though these aspects of creativity have applications in data science too). Creativity is primarily about how the mind forms associations and combines seemingly unrelated concepts to develop ideas and solutions. Creativity is essential in solving problems because solutions to complex problems are not usually apparent. It takes creative exploration, often in collaborative settings, to arrive at novel solutions.
- Presentation: These skills require creativity and concise communication, organization, relationship building, persuasion and oratory performance skills. An effective presenter has mastered all the above skills and can showcase their capabilities under pressure without making glaring mistakes.
Aspiring data science leaders give themselves a tremendous career advantage by investing early on in their technical and soft skills. Enrolling in an M.S. in Data Analytics online provides the balance of capabilities employers seek in this highly dynamic field.
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