A decade ago, Harvard Business Review called the role of the data scientist "The sexiest job of the 21st century." Data scientists were at the cutting edge of making sense out of increasingly vast quantities of unstructured, raw data, known as "big data." Advancements made using data science and analytics technologies facilitated the evolution of the modern, data-driven business world.
A decade later, data scientists are still leading the data revolution, and the job outlook for data scientists remains stellar. According to the U.S. Bureau of Labor Statistics (BLS), the profession is one of the fastest-growing occupations in the U.S., with job growth projected at over 31% from 2020 to 2030.
Given this, pursuing a career in data science could be a good fit if you are fascinated with the intersection of technology, information and business. Exploring what data scientists do, the tools they use and the skills they need can help you find a pathway into this in-demand field.
What Do Data Scientists Do?
The U.S. Department of Labor's O*Net OnLine explains that data scientists develop and use analytics tools and methods to "transform raw data into meaningful information."
Data scientists gather and analyze information from both structured and unstructured datasets. Data scientists interpret complex analysis and translate it into understandable representations to communicate data findings to others.
The meaningful information gained through analysis and interpretation provides actionable insight for information users. This insight informs decision-making, problem-solving, strategy development and operational optimization for organizations of all types.
What Kinds of Techniques and Tools Do Data Scientists Use?
According to O*Net, data scientists use technologies and methods such as:
- Data-oriented programming languages
- Data mining
- Data modeling
- Machine learning (ML)
- Natural language processing (NLP)
- Data visualization software
Stitch analysis of LinkedIn data shows that these tools and technologies mirror the skills most needed by data scientists. Stitch reports the top skills listed by data scientists to be data analysis, proficiency with R and Python (statistics- and data-oriented programming languages), data mining and ML.
These tools, technologies and techniques are interconnected and often build to one another. As a result, data scientists use them in fluid ways to accomplish certain tasks or develop new strategies.
For instance, advancements in artificial intelligence (AI) led to the development of ML, which has vastly expanded the capabilities of data analytics software to accomplish complex and accurate predictive and prescriptive analysis.
Similarly, NLP, a subset of ML (and AI as a whole), allows machines to analyze, interpret and manipulate human language. Data scientists can apply NLP to tasks like data mining to automate and improve the gathering, aggregating and interpreting of language-based data sources like social media.
What Other Skills, Knowledge and Education Do Data Scientists Need?
While data science clearly requires specialized, technical expertise, the human component is equally important. Data scientists need to understand the end-use of information and analysis, how data-driven insight will help business users and others create solutions and achieve desired outcomes. This purpose informs what data should be gathered and focused on as well as what kinds of tools, analysis, interpretation and visualization are best suited to the task.
Data scientists must work closely with colleagues and stakeholders to define purpose and goals, iterate data analytics strategies and ensure findings are clearly communicated and acted on appropriately. This all requires communication and collaboration as well as an understanding of decision-making processes and organizational structures.
As to education, Stitch reports, "over 79% of data scientists that list their education have earned a graduate degree." Data scientists may occupy many roles within an organization, including computer and information research scientists. BLS reports this occupation is also growing rapidly and generally requires a master's degree in a related field.
Northwest Missouri State University's online Master of Science (M.S.) in Data Analytics program is designed to prepare students for technical roles in data science and analytics. In-depth courses examine subjects like data analytics, data-oriented programming languages, data visualization, real-time big data analysis, applied NLP and ML.
These studies provide degree candidates with the technical expertise and skills data scientists need. Further coursework explores business intelligence and analytics, helping students understand how professionals can apply data science strategically to improve decision-making and business outcomes.
Notably, Northwest's M.S. in data analytics program is affordable, and students can complete the degree in as few as 12 months. Plus, the program's online structure allows students to continue working and building experience as they complete their degrees. With its targeted studies and skill development, this program can provide a convenient, accelerated path toward a fascinating career in data science.
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