What is Data Science?

Data science is the field of study that extracts relevant insights and develops strategy from data for business and industry. Combining 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 extrapolated and used for planning and decision-making.

Data science's multidisciplinary approach for analyzing large amounts of data blends principles and practices from mathematics, statistics, business, artificial intelligence, and computer engineering.

Understanding the Data Science Process Cycle

Data Science is a structured approach to extracting valuable insights from data, and it involves several key stages to ensure success. Let's explore each phase in detail:

  1. Data Capture: In this initial step, data is gathered from diverse sources such as databases, online platforms, or manual entry. It's crucial to collect relevant and accurate data to lay a solid foundation for analysis.
  2. Data Storage and Maintenance: Once collected, the data undergoes cleaning and normalization to remove errors and inconsistencies. This ensures that the data is organized and stored efficiently for further processing.
  3. Data Processing: During this phase, advanced techniques like machine learning and statistical analysis are applied to the prepared data. The goal is to uncover patterns, trends, and relationships that can provide valuable insights for decision-making.
  4. Data Analysis: In this stage, the processed data is analyzed using various methods such as regression, predictive analysis, and qualitative techniques. These analyses help in understanding past trends, making future predictions, and identifying areas for improvement.
  5. Communication: The insights derived from data analysis are communicated to stakeholders through reports, dashboards, and visualizations. Clear and concise communication is essential to ensure that decision-makers understand the findings and can take appropriate actions.

By following this structured process cycle, organizations can leverage data effectively to drive innovation, improve operations, and gain a competitive edge in today's data-driven world.

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 challenges across a variety of industries.

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.
  • Environmental Data Scientist: Environmental data scientists explore interrelationships related to the natural environment, discover drivers of ecosystem processes, and use tools to understand data that describes land, water, air, and biodiversity. They gain a foundation of ecological science, as well as the computational and analytical skills needed to manage data and draw inferences that will address environmental challenges.
  • 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.
  • Collect, visualize, and analyze geospatial data using mapping 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, sustainability, 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 U.S. Bureau of Labor Statistics, data scientists held about 168,900 jobs in 2022. The largest employers of data scientists were:

  • 13%
    computer systems design and related services
  • 9%
    insurance carriers and related activities
  • 8%
    management of companies and enterprises
  • 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 mean entry-level salary for data scientists is $86,906 (Payscale), according to payscale.com. The U.S. Bureau of Labor Statistics reports a mean annual wave for data scientists of $119,040 (BLS) while the highest 10 percent earn $184,090 (BLS).

Salaries for Careers with a Data Science Degree
Career Mean Entry-Level Salary (Payscale) Mean Annual Wage (BLS) Top 10 Percent (BLS)
Database Administrator $60,788 $104,810 $157,710
Database Architect $85,612 $137,030 $195,000
Data Scientist $86,906 $119,040 $184,090

Figures from payscale.com, accessed May 2024.

Figures from U.S. Bureau of Labor Statistics (BLS), dated May 2023.

See additional computing salary information.

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, ArcGIS Pro, 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, develop sustainable solutions, design and develop new products, and better market their products.

Data Science at Michigan Tech

The world is changing fast. With big data getting even bigger, tomorrow needs skilled and agile data science professionals. At Michigan Tech, we're ready for what tomorrow needs. We prepare students to create the future in the field of data science through innovative and interdisciplinary undergraduate and graduate degree programs on our campus in Houghton, Michigan.

Undergraduate Degree Programs

  • Data Science (BS)
    The Data Science Bachelor of Science program delivers a broad-based education in data science fundamentals, data mining, predictive analytics, communication, and ethics. You'll will gain a competitive edge through a technical focus area in software engineering, cybersecurity, statistics, or business technology. And you'll have the freedom to explore and develop your own interests in one or more domains.
  • Environmental Data Science (BS)
    Put your passion for the environment to use in the Environmental Data Science Bachelor of Science program. Gain the foundational knowledge of ecosystem processes, sustainable business solutions, and the interrelationships related to the natural environment while using scientific processes and data analysis in one of four environmental data science concentrations—global change science, environmental statistics, geospatial information science, or genetic applications in data science. Use your skills to create climate solutions in Michigan Tech's College of Forest Resources and Environmental Science

Graduate Programs

  • Data Science (MS)
    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. This interdisciplinary master's degree brings together faculty from colleges and departments across the University, including business, computer science, mathematical sciences, and computer engineering. 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.
  • Data Science (Graduate Certificate)

    A smaller investment in time and money than a master's, the 9-credit-hour Graduate Certificate in Data Science Foundations builds competency in data science techniques including predictive modeling, data mining, information management, and data analytics. The 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.

Accelerated Master's Program

Our accelerated master's in data science program allows you to count up to 6 senior-level credits toward both a Bachelor of Science and a Master of Science in Computer Science, Cybersecurity, Data Science, and many more majors. This 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.

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.


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