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Data Science (DS)

da·ta sci·ence

The extraction of actionable knowledge from rich and varied datasets to quantify and address the pressing concerns of a modern society.

Also Known As: Data Scientist, Software Engineer, Software Developer, Statistician, Machine Learning Scientists, Business Analytic Practitioners, Software Programming Analysts, Digital Analytic Consultant, Quality Analyst, +10,000 more


  • No. 7

    institutional ranking in CS at

  • 96

    award-winning faculty

  • $24.8M+

    in research funding

  • 4

    U-M alumni have received the AM Turing Award, considered the "Nobel Prize" of computing

Technical rigor and relevance

Unravel key answers in the biggest data sets

Graduates highly sought in a field of rapid growth

What do Data Scientists do?

We are a new class of experts who extract actionable knowledge from rich, varied, and large datasets in order to find new associations that provide insight into current trends and big challenges. Using data that includes text, audio, video, and streaming and social data, we help to make discoveries in areas such as precision medicine, sustainability, space exploration, economics, and intelligent systems. We like big challenges!


  • Biological Sciences

    Help to understand the natural order through bioinformatics, biostatistics, and computational biology. Investigate protein chemistry, genomics, systems biology, bioengineering, and environmental sciences.

  • Business and Industry

    Develop techniques for data collection, differentiation, and personalization, and intelligent systems that allow your company to offer more competitive, more calibrated, and better targeted offerings.

  • Government

    Uncover trends and make connections in data that can shed new insight on matters of policy or increase the efficiency and effectiveness of government operations.

  • Precision Health

    Collect and analyze data on individuals, populations, and environments that can be used to develop targeted and intelligent care regimes for patients.

  • Security

    Collect and analyze data from multiple sources that can reduce security risks, from video and physical proximity data through digital fingerprints. Develop defend against intelligent agents, botnets, and hackers.

  • Social Networks

    Collect and analyze data from user habits, preferences, devices, locations and interactions to develop more personalized and relevant experiences.

  • Sustainability

    Develop solutions that allow enterprises to minimize their environmental footprint through supply chain management, natural resource management, carbon reduction strategies, distribution strategies, and health and safety initiatives.

  • Transportation

    Develop smarter transportation systems that use data from vehicles, pedestrians, and fixed locations along with data about why transport is needed to create more efficient and sustainable solutions.

  • Areas in which a student, through the use of technical and free electives and extracurricular activities, could apply their major.

Areas in which a student, through the use of technical and free electives and extracurricular activities, could apply their major.

Graduate receiving hood during ceremony

Sequential Undergraduate/Graduate Studies Program (SUGS)

Complete your bachelor’s and master’s degrees in only five years with SUGS by taking some graduate-level classes during your undergraduate years, so you can save yourself one semester and complete the masters with only two additional semesters.

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Sample Course List



  • Fall Semester
    • CoE Core Calculus I (Math 115)
    • CoE Core Engineering 101
    • CoE Core Chemistry (125/126 and 130 or 210 and 211)
    • Elective Intellectual Breadth
  • Winter Semester
    • CoE Core Calculus II (Math 116)
    • CoE Core Engineering 100
    • Major Requirement Discrete Mathematics (EECS 203 or Math 465)
    • Major Requirement Programming and Elementary Data Structures (EECS 280)

Sophomore Year

Sophomore Year

  • Fall Semester
    • CoE Core Calculus III (Math 215)
    • CoE Core Physics (140 and 141)
    • Major Requirement Data Structures and Algorithms (EECS 281)
    • Elective General Elective
  • Winter Semester
    • CoE Core Linear Algebra (Math 214 or 217)
    • CoE Core Physics (240 and 241)
    • Major Requirement Introduction to Probability and Statistics (STATS 412)
    • Elective Intellectual Breadth

Junior Year

Junior Year

  • Fall Semester
    • Major Requirement Applied Regression Analysis (STATS 413)
    • Major Requirement Machine Learning (EECS 445) or Data Mining (STATS 415)
    • Major Requirement Flexible Technical Elective
    • Elective Intellectual Breadth
  • Winter Semester
    • Major Requirement Advanced Technical Elective
    • Major Requirement Databases & Applications (EECS 484 or 485)
    • Major Requirement Technical Communication (TCHNCLCM 300)
    • Elective Intellectual Breadth
    • Elective General Electives

Senior Year

Senior Year

  • Fall Semester
    • Major Requirement Major Design Experience Professionalism (EECS 496)
    • Major Requirement 400-Level Technical Communication
    • Major Requirement Data Science Capstone Course
    • Major Requirement Flexible Technical Elective
    • Elective General Elective
  • Winter Semester
    • Major Requirement Advanced Data Science Technical Elective
    • Major Requirement Data Science Applications Elective
    • Major Requirement Flexible Technical Elective
    • Elective General Elective
    • Elective General Elective

Individualized schedules will be made by students in consultation with an advisor who will tailor their classes to better fit the student's needs.

Practice Your Purpose

Apply the skills you are learning in class to the real world.

Student Design Teams

MDST - Michigan Data Science Team
A room full of students using laptops and wearing headphones
Wolverine Soft - Video Game Development
A drone with 4 propellers floats in the air with a pyramid shaped center with a white box on tip and wires sticking out
MAAV - Michigan Autonomous Aerial Vehicles
An electric racecar labeled with a large “Michigan” with a student driver wearing a full-face motorcycle helmet
Michigan Electric Racing
2 team members wipe the completed maize and blue solar car. The car has a sleek design and half covered in solar panels.
Solar Car Team
4 students wearing MRover shirts smile while carrying the rover, a machine platform with 4 tires and a robotic arm.
MRover - Michigan Mars Rover
A small vessel made up of two boxes sits in the NERS Fountain. The bottom box has a painted shark face
UM::Autonomy - Autonomous Boat
A student wears a powered exoskeleton. It is worn like a backpack and has metal pieces that extend down to the feet.
STARX - Strength Augmenting Robotic eXoskeletons
Students For Exploration And Development of Space
Students for the Exploration and Development of Space
A student in a full-face motorcycle helmet sits nearly horizontally as he rides the maize electric motorcycle named “Chronos”
SPARK - Electric Racing
A woman atop a roof wearing a hard hat and holding a power tool in front of a set of solar panels.
Grid Alternatives

Professional Development

HKN Logo
Eta Kappa Nu - Honor Society
Girls in EECS
An aerial view of students with laptops gathered around a table
Michigan Hackers
M Sail Logo
MSAIL - Michigan Student Artificial Intelligence Laboratory
IEEE - Institute for Electrical and Electronics Engineers


Reducing carbon footprint of AI training
Clinicians could be fooled by biased AI
Enabling sustainable cloud computing

Alumni Biographies

Each of these alumni are real people who were once in your shoes, deciding a major. Explore their path and how a Michigan education set their life in motion.

  • Allie Cell headshot
    • Allie Cell
    • Next Insurance
    • Alexandria (Alex) Pawlik
    • Sight Machine
  • Danny Vargovick
    • Danny Vargovick
    • Detroit Tigers
Allie Cell headshot

    Allie Cell

    Next Insurance

    Alexandria (Alex) Pawlik

    Sight Machine

Danny Vargovick

    Danny Vargovick

    Detroit Tigers

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Industries & Occupations

  • Data processing and analysis
  • Federal government
  • Finance
  • Industrial consulting and management
  • Intelligent and assistive systems
  • Logistics
  • Precision medicine and drug discovery
  • Scientific research
  • Software industry
  • Transportation
The aerial view of an intersection in Ann Arbor with lights blurring as the cars speed through


  • Accenture
  • Amazon
  • Capital One
  • Deloitte
  • Epic
  • Facebook
  • Ford Motor Company
  • Goldman Sachs
  • Google
  • IBM
  • JPMorgan Chase
  • Massachusetts Institute of Technology
  • Microsoft
  • Nike
  • Northrop Grumman
  • Oracle
  • Tesla
  • University of Michigan
  • U.S. Defense Intelligence Agency
  • Walmart
  • Zoom


Allie Cell headshot

Allie Cell

  • Next Insurance
  • Sr. Data Scientist, Machine Learning

University of Michigan, BSE Data Science Engineering '18
Career Summary

Since joining NEXT Insurance in March 2021, Allie has played a pivotal role in driving innovation and efficiency within this company in the insurtech sector. As a data scientist, she leads end-to-end projects focused on enhancing the profitability of insurance coverage for small businesses. Notable achievements include the development and deployment of advanced risk models, resulting in substantial cost savings and revenue growth for the company. Allie’s technical expertise extends to building text models and exploring novel data sources to inform strategic decision-making.

Prior to her role at NEXT Insurance, Allie gained valuable experience as a full-stack data scientist at Shift, which priced, acquired, and sold automobiles. During her tenure, she led projects aimed at optimizing pricing models and improving acquisition quality, leading to tangible improvements in operational efficiency and revenue streams.

Reflection on Time Spent at U-M

During my time at Michigan, a lot of the data science curriculum was elective-based; this can allow you to start shaping your career by choosing to specialize in statistics or machine learning.

Advice to Students

Getting real data project experience is super valuable, so studying data science at a school with as many resources and opportunities as the University of Michigan (including the Michigan Data Science Team, Michigan Sports Analytics Society, Multidisciplinary Design Projects, research, data hackathons, etc.) is ideal.

Getting a job with the title of data scientist without an advanced degree can be hard, and data science as a field is constantly evolving. Be prepared to complement your education after graduating, which could be by attending conferences or taking online courses. Roles within data science vary widely — some are just analytics, some just machine learning, some do deep learning; I’d recommend either trying to figure out what aspects are your favorite (or least favorite) in college and then looking for jobs true to that, or to look at positions (maybe at start-ups!) that allow you to work on a range of disciplines, even if the title isn’t Data Scientist.

Alexandria (Alex) Pawlik

  • Sight Machine
  • Data Scientist

BSE Data Science '22
Career Summary:

Sight Machine is the first and only company I’ve worked at since undergrad. We are a SaaS start-up that helps manufacturing plants to convert their unstructured data into a solid data foundation that’s ready for analysis. I was hired as a Support Analyst, which helped me get familiar with the Sight Machine platform and the nuances of the backend. I did classical customer support and addressed support tickets for 6 months before transitioning to the role of Data Scientist (my current position). 

My day-to-day involves analysis of customer data and (data science-y) platform improvements. The analysis that I do is very similar to the data analyses that I did for my degree – building models and comparing them to real-world context to yield useful insights. On the other hand, the platform improvements I contribute to are very in line with the computer science portion of my data science degree – they require a solid understanding of our platform, which means being able to piece through and interpret a complex codebase. 

Advice to Students:

Above all, trust your gut. The best thing I did for myself at U of M was choosing to pursue Data Science instead of Computer Science. I chose it on a whim because I missed taking math/statistics classes and I haven’t had a single regret.

Don’t just join the data science team because you’re a data science major. Try out new hobbies, especially technical ones. For example, I learned a lot and had many unique opportunities because I was part of the blockchain club during undergrad. It helped me hone my development skills, gave me opportunities like speaking at a conference and doing a blockchain PoC for an insurance company, and most importantly it gave me a better idea of what I did and didn’t want to do with the rest of my career.

Danny Vargovick

Danny Vargovick

  • Detroit Tigers
  • Assistant Director, Baseball Research & Development

BSE Data Science '17
Engineering Honors Program
Career Summary

Career paths in baseball don’t closely resemble others in corporate America. It’s standard to take a year-long internship after graduation — which in 2017 paid minimum wage. Since I was within driving distance of Detroit and didn’t have class on Fridays during my final semester, I started in January and went to bed early on Thursdays — while everyone else was in the Rick’s line. I expected I’d get a “normal” job after my Associate year, but I managed to get lucky and land a full-time job with the Tigers at a time when the Analytics department was still young.
I’ve been with the Tigers for seven years now. During that time, I’ve focused on predictive models for use in player evaluation and acquisition. Since I started, the department has grown by a factor of ~10. Because I joined early, there was no obvious predefined career path that more senior employees had followed. But in reality, that was a blessing since I was quickly given responsibility and room to grow. I was recently promoted to Associate Director of R&D and have three other employees reporting to me. I now spend slightly less time working on my own models and more time talking to others about how they can tackle the challenges in their projects, but that’s the most enjoyable part of the job.

How have you used your engineering degree in your job?

I use my engineering degree every day. The coding classes through EECS 281 provided the technical foundation I needed, and EECS 445, Machine Learning, was both the most difficult and useful class I took at Michigan. More broadly, I use the problem solving skills I developed throughout the entire engineering curriculum to try to determine what the right questions to ask are and what tools in our toolkit we can use to answer those questions.  

What led you to choose your engineering major(s) at UofM?

Data Science Engineering became a major at the start of my junior year. Before then, I was a CS & IOE double major — essentially trying to splice a DS major together — but that included classes I was completely uninterested in like Ergonomics in IOE. Data Science was the perfect fit for me to be an analyst. I was so excited when it became a major that when I went to declare it during Welcome Week of my junior year, my advisor told me that he thought I was the first person to declare it at Michigan — though he said he wasn’t certain.

How did your passions influence your degree and career choices?

I’ve always been passionate about baseball and sports in general. I was actually a student beat reporter for The Michigan Daily covering the baseball team in 2015. I’m lucky that my passion for baseball pushed me into the perfect major for me — Data Science. If I didn’t work in baseball, I’d want to be an analyst in a different industry where I could combine subject matter expertise with tech skills. If not for my passion for baseball, I’m not sure if I would have realized my passion for Data Science more broadly. 

What additional advice can you offer today’s students as they are thinking about majors?

I love baseball and love my job. I’m extremely lucky to be able to say that. I certainly endorse the idea that you should figure out what you’re passionate about and then try to make a career out of it, but coming from me, this advice contains some survivorship bias! Still, if you financially have the ability to take a risk after graduation, I think it’s generally a good idea since the payoffs can be immense. That’s what your 20s are for! And during college, take risks and explore different disciplines. Life isn’t a race, and undergrad certainly isn’t a race … maybe even drag your feet a bit. You’ll be much better spending an extra year figuring out what you’re passionate about and where you can excel than sprinting down the first path you see.

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