Data Science

Data Science is an interdisciplinary field that uses processes, algorithms, and systems to extract insights from structured and unstructured data. It sits at the intersection of statistics, mathematics, computer science, and domain expertise.

Today, data is being created faster than ever, making people who can organize and make sense of information highly valuable. Data science is primarily a job function — describing what you do — rather than a single industry, making it applicable across essentially every sector.

Data scientists use techniques like machine learning and AI for a wide range of applications, including:

  • Prediction (e.g., forecasting sales)
  • Recommendations (e.g., streaming service suggestions)
  • Classification (e.g., spam detection)
  • Anomaly Detection (e.g., fraud identification)

When looking for data science opportunities, be sure to look for varied titles, such as Data Scientist, Data Analyst, Business Analyst, Statistician, and Quantitative Analyst.

Necessary Skills
  • Core Technical Skills: Proficiency in Python and R is highly valued for their extensive use in data analysis, machine learning, and statistical modeling.
  • Data Management: Strong skills in SQL (Structured Query Language) are essential for extracting, manipulating, and querying data from databases.
  • Other Valuable Tools & Languages: Familiarity with other programming languages and tools such as SAS, MatLab, SPSS, Java, Scala, Julia, and big data technologies like Hadoop and Spark, as well as data visualization tools like Excel and Tableau, can also be highly beneficial depending on the specific role and industry.
  • Foundational Skills:
    • Computer science fundamentals, statistics, and/or mathematics.
    • Analytical thinking ability and attention to detail (for solving numerical problems, organizing numbers, categorizing concepts, understanding spatial relationships, and making decisions based on logic).
    • Communication skills (for collaborating with other data scientists, understanding business challenges and customer needs, and communicating complex issues to non-data and non-technical team members).
    • Intellectual versatility (for collaborating with experts in varied fields and devising innovative solutions).
What to Do Now to Prepare

Learn More (General Online Resources): Kaggle, Vault article, Analytics Vidhya.

Typical Entry-level jobs
  • Common Entry Job Titles: Data scientist, data science analyst, business analyst, statistician, researcher, developer, quantitative analyst, quantitative researcher, machine learning engineer, data engineer, analytics engineer, database engineer, data architect, data infrastructure engineer.
    • Note: The field of data science is dynamic, and new specialized titles are constantly emerging. It's crucial to read the position description carefully, not just rely on the job title, as responsibilities can vary significantly.
  • Suggestions for keywords to use on job boards: Data science, data scientist, data analyst, business analyst, statistician, quantitative analyst, data engineering, analytics engineering, machine learning.

How to search on Handshake: Handshake is where companies post opportunities specifically for U-M students, so every student should use it.

Internship and Job Essentials
  • Hiring timelines: Many companies recruit in early fall (September and October) for internships and full-time positions. Some companies continue recruiting in late fall (November) and early winter (January, February, March), and sometimes even later. Keep checking Handshake frequently.
  • Common Job boards for your track: Job/Internship posting sites should be a small part of your job search efforts, as a small percentage of positions are secured through job boards. However, they can be helpful for understanding active companies, position variety, and job descriptions. A multi-pronged approach is recommended: use Handshake, do some networking, and check job boards. You can also Google "data science jobs" or "business analysis" or "business analyst jobs".
  • Resume and interview tips:
  • Resumes/CVs: Check the University Career Center's resume resources page and cover letter information. Example data science-related resumes are available.
  • Interviewing:
    • Research: Research the company, its products/services, clients, and current projects. Google the company for recent news.
    • Reflect: Reflect on your skills and experiences and how they apply to the specific position and company.
    • Rehearse: Practice common data science and technical interview questions with friends and family, or schedule a mock interview at the University Career Center.
    • U-M Career Center Interview Resources are available.
Industry Trends
  • Importance of Data Science: Data is increasingly cheap and ubiquitous, and employers are digitizing old content and creating new data rapidly. Employers are accumulating new data faster than they know what to do with it. There's a big question for employers on how to effectively use their data, but new technologies are emerging to organize and make sense of this data.
  • Job Function vs. Industry: Data science is primarily a job function (what you do), but can also be considered an industry (organizations that only do data science for clients).
  • Applications of Data Science: Prediction, recommendations, classification, pattern detection and grouping, anomaly detection, recognition (image, text, audio, video, facial), actionable insights, automated processes and decision-making, scoring and ranking, segmentation, optimization, and forecasts.
  • General "Process" of Data Science: Acquire or collect raw data, clean and integrate data, investigate data to determine tools/models/algorithms, apply models and algorithms (e.g., machine learning, statistical modeling, AI), communicate results, make decisions based on results, and repeat to solve new problems.
  • Emerging Technologies: Briefly mention emerging technologies or sub-fields within data science, such as Generative AI, MLOps, or Explainable AI (XAI). Staying informed about these cutting-edge areas is crucial for a future-proof career.
  • How to stay on top of trends:
  • Add these media sources to your regular reading list: TBD
    • By signing up for this track