Data Science
Professor Brown with Data Science student looking at an equation on the board.

What is Data Science?

Data science is the field of study that extracts relevant insights and develops strategy from data for business and industry. Combing tools, methods, and technology such as data analysis/modeling, human-machine interaction and algorithms, data scientists ask and answer questions like what happened, why did it happen, what will happen, and how can the results be used for planning and decision-making. Data science’s multidisciplinary approach for analyzing large amounts of data blends principles and practices from mathematics, statistics, artificial intelligence, and computer engineering.

Why Has Data Science Grown? 

The field of data science emerged in response to the growing abundance of data generated by business, industry, technology, engineering, and science as a result of richer content and high-speed communication networks. According to Statista.com, the amount of data stored globally hovered around 97 zettabytes in 2022; projections place the data load at approximately 181 zettabytes by 2025. In other words, data is everywhere.

To manage all this information, business and industry are adopting data-driven decision-making now more than ever—and they need skilled professionals who can make sense of massive amounts of real-time data. Due to this growing need, data scientists are present in almost all organizations, from retail to finance to healthcare.

Data science continues to evolve as one of the most promising and in-demand career paths for skilled professionals. Effective data scientists are able to identify relevant questions, collect data from a multitude of different data sources, organize the information, translate results into solutions, and communicate their findings in a way that positively affects business decisions. These skills are required in almost all industries, causing skilled data scientists to be increasingly valuable to companies.

What Do Data Scientists Do?

The term data scientist was coined as recently as 2008, when companies realized the need for data professionals who are skilled in organizing and analyzing massive amounts of data. Data scientists gather, organize, clean, and analyze data much like data analysts, but they are more forward-looking and prediction-oriented.

Data scientists are expected to help solve issues that can vastly affect a company’s success trajectory. Data scientists use analytical, statistical, and programming skills to collect, analyze, and gain insights from large data sets. They use the data to build machine learning models and use the resulting information to develop data-driven solutions to difficult business challenges.

Careers and Job Titles in Data Science Include:

  • Data Scientist: Data scientists examine which questions need answering and where to find the related data. They have business acumen, analytical skills, and the ability to mine, clean, and present data. Businesses hire data scientists to source, manage, and analyze large amounts of unstructured data, which are then synthesized and communicated to key stakeholders to drive strategic decision-making.
  • Data Analyst: Data analysts bridge the gap between data scientists and business analysts. They are provided with questions that need answering, and they organize and analyze data to find results to guide high-level business strategy. Data analysts are responsible for translating technical analysis to qualitative action items and effectively communicating their findings to diverse stakeholders.
  • Data Engineer: Data engineers manage exponential amounts of rapidly changing data. They focus on the development, deployment, management, and optimization of data pipelines and infrastructure to transform and transfer data to data scientists for querying.
  • Data Architect: Data architects ensure that data is accessible and formatted appropriately for data scientists and analysts. They design, create, and maintain database systems that match the requirements of a specific business model and job requirements. Their task is to maintain these database systems’ functionality.
  • Machine Learning Scientist: A machine learning scientist, also known as a research scientist or research engineer, researches new approaches to manipulating data and designs new algorithms to be used. They are often a part of the research and development department, and their work usually leads to research papers.
  • Machine Learning Engineer: These professionals are familiar with machine learning algorithms like clustering, categorization, and classification. Machine learning engineers have strong statistics and programming skills and some software engineering knowledge.
  • Business Intelligence Developer: Business intelligence developers are in charge of designing and developing strategies that allow business users to find the information they need to make decisions quickly and efficiently. Business intelligence developers have a basic understanding of the fundamentals of business models and how they are implemented.
  • Data Storyteller: Often, data storytelling is confused with data visualization. Data storytelling is not just about visualizing the data and generating reports and stats; rather, it is about finding the narrative that best describes the data and using it to help others better understand the data.
  • Database Administrator: A database administrator is in charge of managing and monitoring databases, making sure they function properly, keeping track of data flow, and creating backups and recoveries.

What Tasks Do Data Science Professionals Do?

  • Analyze, manipulate, or process large sets of business or financial data using statistical software.
  • Apply feature selection algorithms to models, predicting outcomes of interest such as sales, attrition, and healthcare use.
  • Apply sampling techniques to determine groups to be surveyed or use complete enumeration methods.
  • Clean and manipulate raw data using statistical software.
  • Compare models using statistical performance metrics, such as loss functions or proportion of explained variance.
  • Create graphs, charts, or other visualizations to convey the results of data analysis using specialized software.
  • Deliver oral or written presentations of the results of mathematical modeling and data analysis to management or other end users.
  • Design surveys, opinion polls, or other instruments to collect data.
  • Identify business problems or management objectives that can be addressed through data analysis.
  • Identify relationships and trends or any factors that could affect the results of research.
  • Identify solutions to business problems, such as budgeting, staffing, and marketing decisions, using the results of data analysis.
  • Propose solutions in engineering, the sciences, and other fields using mathematical theories and techniques.
  • Read scientific articles, conference papers, or other sources of research to identify emerging analytic trends and technologies.
  • Recommend data-driven solutions to key stakeholders.
  • Test, validate, and reformulate models to ensure accurate prediction of outcomes of interest.
  • Write new functions or applications in programming languages to conduct analyses.

Where Do Data Scientists Work?

According to the US Bureau of Labor Statistics, data scientists held about 113,300 jobs in 2021. The largest employers of data scientists were:

  • 15%
    computer systems design and related services
  • 10%
    management of companies and enterprises
  • 9%
    insurance carriers and related activities
  • 7%
    management, scientific, and technical consulting services
  • 5%
    scientific research and development services

How Much Do Data Scientists Earn?

Data science professionals are rewarded for their highly technical skill set with competitive salaries and great job opportunities at big and small companies in most industries. Data science professionals with the appropriate experience and education can make their mark in some of the world’s most forward-thinking companies.

The median annual wage for data scientists was $100,910 in May 2021, according to the US Bureau of Labor Statistics. Employment of data scientists is projected to grow 36 percent from 2021 to 2031, much faster than the average for all occupations. About 13,500 openings for data scientists are projected each year, on average, over the decade.

Further, Glassdoor.com estimates the average base salary for data scientists is $104,014. The employment website predicts a 28 percent increase in demand for data scientists by 2026, to fill 5,971 job openings. Glassdoor rates data scientist as the No. 3 Best Job in America (2022); and data scientist has been placed as a top three best job every year since 2016.

What Skills and Qualifications Do Data Scientists Need?

Data science professionals are well-rounded, data-driven individuals with high-level technical skills. They are capable of building complex quantitative algorithms to organize and synthesize large amounts of information to answer questions and drive strategy in their organization.

At the crossroads of several disciplines, data science positions require programming skills, mathematical and/or statistical knowledge, and business domain expertise.

Data scientists need to be curious and result-oriented with exceptional industry-specific knowledge and communication skills for explaining highly technical results. Because data science involves the use of algorithms and statistical techniques, students need extensive study in mathematics and statistics. High school students interested in becoming data scientists should take classes in subjects such as linear algebra, calculus, and probability and statistics.

At the college level, courses in computer science are important in addition to math and statistics. Students must learn data-oriented programming languages as well as statistical, database, and other software for presenting analyses. Data scientists typically need at least a bachelor’s degree, but some jobs require a master’s or doctoral degree. Common fields of degrees earned by data scientists include mathematics, statistics, computer science, business, and engineering.

Gaining specialized skills within the data science field can distinguish data scientists even further. For example, machine learning experts use high-level programming skills to create algorithms that continuously gather data and automatically adjust their function to be more effective.

The following skills are required for most jobs within the data science field. The extent to which particular skills are used on a day-to-day basis depends upon the position’s requirements.

  • Data management: Collecting, organizing, cleaning, and manipulating data.
  • Coding: Using a variety of languages such as SQL, Python, or R; sometimes also Java, C++, etc.
  • Programming: Writing computer programs and analyzing large data sets to uncover answers to complex problems.
  • Data visualization: Using BI tools such as Tableau, Power BI, Looker, etc.
  • Database modeling: Understanding how databases work.
  • Statistical analysis: Applying data analysis to gain insights, identifying patterns in data, and developing a keen sense of pattern detection and anomaly detection.
  • Mathematical knowledge: Applying mathematical knowledge to data analysis to calculate metrics.
  • Machine learning: Implementing algorithms and statistical models to enable a computer to automatically learn from data.
  • Computer science: Applying the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.
  • Data storytelling: Communicating actionable insights using data, often for a nontechnical audience.

Data scientists also need soft skills, including:

  • Business intuition
  • Analytical thinking
  • Critical thinking
  • Logic
  • Curiosity
  • Teamwork
  • Communication
  • Problem-solving

The Future of Data Science

Innovations in the methods for analyzing, visualizing, and interpreting data, and collaborating around data with diverse stakeholders, have become key to data-intensive discovery in nearly every field. As a result, data science is rapidly becoming a new paradigm for research and discovery, integrating approaches from computer science, statistics, applied mathematics, visualization and communication, and many application domains.

The need for data scientists shows no sign of slowing down in the coming years. Employment growth for data scientists is expected to stem from an increased demand for data-driven decisions. The volume of data available and the potential uses for that data will increase over the previous decade. As a result, organizations will likely need more data scientists to mine and analyze the large amounts of information and data collected. Data scientists’ analyses will help organizations make informed decisions and improve their business processes, design and develop new products, and better market their products.

Data Science at Michigan Tech

Master of Science in Data Science

At Michigan Tech, business, computer science, mathematical sciences, and computer engineering are the foundations for our STEM-accredited, interdisciplinary master's degree in data science. Faculty and courses for the graduate program come from colleges and departments across the University.
The Master of Science in Data Science provides a comprehensive, course-based education in data mining, predictive analytics, cloud computing, data science fundamentals, communication, and business acumen. Plus, you'll gain a competitive edge through domain-specific specialization in disciplines of science and engineering, giving you the space to explore and develop your own interests in one or more domains.

The data science master’s degree requires successful completion of 30 approved credits: 12 credits of core courses and 6-12 credits of approved domain electives. And while the degree program is a course-based curriculum, you’ll get involved in many hands-on, real-world activities and projects in these courses. The skills and knowledge you’ll gain are building blocks on your path of lifelong learning in the field of data science.

Graduate Certificate in Data Science

The 9-credit-hour Michigan Tech Graduate Certificate in Data Science Foundations builds competency in data science techniques including predictive modeling, data mining, information management, and data analytics.

A graduate certificate can be completed as a stand-alone credential, although you may be able to stack your certificate coursework toward a master’s degree. Similar to a master’s degree, a graduate certificate is more narrowly focused on a specialized field. A smaller investment in time and money than a master’s, the Michigan Tech Graduate Certificate in Data Science Foundations entails completion of one required course and two of three allowed electives.

Accelerated Master’s in Data Science

Current Michigan Tech undergraduate students can fast-track their careers with the accelerated master's degree in data science (BS-MS, BA-MS) program. The accelerated program provides a pathway to earn both a bachelor's and master's in data science in just five years of full-time study.

The accelerated master's in data science program is open to all high-achieving undergraduate students at Michigan Tech. Students may double count up to six courses toward both the bachelor’s and the master’s degree. Students with an overall GPA of 3.0 or higher can apply for admission to the accelerated MS in Data Science program any time upon attaining junior class standing, but must apply prior to being awarded their bachelor’s degree.

Data Science at Michigan Tech

Only at Michigan Tech

Career Fair

Michigan Tech graduates are in high demand. The University hosts two Career Fairs annually, each attracting hundreds of employers from multiple industry sectors, all hiring for internships, co-ops, and permanent positions. As many as 7,000 student interviews are available to students during each Career Fair.  The semi-annual Career Fair is preceded by CareerFEST, a series of casual networking events and career development workshops to help students prepare.

Research

Michigan Tech faculty and students are engaged in cutting-edge data science research. And while the data science master’s degree program is course-based, students have many opportunities for graduate and lab assistantships, internships, and other real-world activities and projects.

The College of Computing

Founded in 2019, the College of Computing is one of the first colleges in the nation—and the only college in Michigan—to focus solely on computing. Digital transformation has morphed every discipline into a computing discipline, and industries like manufacturing, criminal justice, marketing, and healthcare are all being reinvented by digital technologies. The College of Computing is making sure that today's and tomorrow's employers have the computing talent they need to thrive in this brave new world.

"I chose Michigan Tech for its coursework that is aimed at providing practical knowledge in computing with well-designed courses for programming, statistics, and data analysis. Also, the professors are easy to connect with and always make time to assist students."Shalaka D. Gaidhani, Graduate Student, Data Science
student working on a desk with data on several computer screens and papers on the desk