Many students who pursue the data science minor hope to put their data science skills to practice before graduation. Whether you are interested in researching a data science topic, or simply want to put your skills to practice, undergraduate research can be highly beneficial experience.
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Data Science as a Research Topic
Data science as a research topic involves investigating data science itself and contributes to the knowledge and understanding of data science methods and principles. In other words, researchers are exploring questions, problems, or topics related to data science. This can involve theoretical or empirical research on data science methodologies, algorithms, tools, best practices, or emerging trends. Research topics in data science might include:
- Development and optimization of machine learning algorithms.
- Evaluation of data mining techniques for specific applications.
- Studies on data preprocessing and feature engineering.
- Ethical considerations in data science and AI.
- Investigations into the social, economic, or policy implications of data science and big data.
Researchers working on data science as a research topic aim to advance the field itself, contribute to the knowledge base, and often publish their findings in academic journals or conferences specific to data science and related disciplines.
Data Science as a Skill for Research
Data science as a skill for research involves using data science techniques and tools as a means to conduct research in other fields, applying data science methods to answer questions or address problems within a specific discipline. Researchers use data science techniques to collect, analyze, and derive insights from data that are relevant to their primary area of study. Data science skills are considered a means to an end in this context, rather than the main focus of research. Examples include:
- A biologist using data science methods to analyze genetic data.
- An economist using data science for economic modeling and forecasting.
- A sociologist using data science for social network analysis.
- A psychologist using data science for analyzing behavioral data.
- A public health researcher using data science to analyze epidemiological data.
In these cases, data science skills are applied to enhance the quality and depth of research within a specific discipline. The primary goal is to answer research questions or address issues in the primary domain of study, and data science is a tool to achieve that goal.
There are two primary pathways for undergraduate students to get started in research - independently reaching out to a mentor or getting started through an organized program. Independent outreach is the most common way students get involved in research. This involves identifying a research mentor/faculty whose research aligns with your interests and reaching out to them through email. Students can also apply to an organized program to get involved in research. Organized programs have specific application processes, deadlines, and expectations for each program. To learn more, visit the Office of Undergraduate Research How to Get Started page or read on for more information specific to data science minors.
Office of Undergraduate Research How to Get Started
Undergraduate students engage in reseach at all points of the research process. Some may be interested in researching their own question and engaging in a full research cycle (following a project from start to finish), while others may be interested in working on a project already in motion or at a particular point in the research process. Both independent outreach and organized programs can provide students with opportunites that fit their desired research experience and goals. Many structured/organized undergraduate research programs support students who are working extremely independently.
It is important to reflect on why you are interested in research and your goals for the experience. Before you start exploring opportunities, reflect on the following:
- Independence: How much independence are you looking for - both in the research experience and the research topic/project? Some experiences may offer a higher degree of independence in choosing the research topic, designing experiments, and conducting research. Do you want to experience a full research cycle (working on a project from start to finish) or be part of a particular step in the research process? Do you want to be part of a larger research program with defined goals and objectives?
- Flexibility: How much flexibility are you looking for - both in terms of time committment and research topic? Students working 1:1 with a faculty member may have more flexibility in terms of the research project's focus, timelines, and methodologies. They may be able to explore areas of personal interest. Structured programs may provide students with specific program timelines (i.e. summer experiences) and hour requirements. How much time each week do you have to dedicate to research? What other committments do you have?
- Faculty Mentor: All students will work with a faculty mentor who guides and advises them throughout the research process. The mentor provides support, reviews progress, and offers expertise. What characterics do you want in a research mentor? What questions could you ask a faculty member to better understand their mentorship philosophy?
- Credit-Based: Many students hope to earn academic credit for their work. Is this something that is important to you? Do you hope to use this credit to count for major or minor requirements?
- Duration: The duration of research projects can vary widely, from a single quarter to multiple years, depending on the student's goals and availability. Some structured programs may have a set duration, such as a summer research program, which typically lasts for a few months. Sometimes it is possible for a student to work with a faculty member for multiple years as an undergrad, and go on to work in a full time capacity in the lab. Reflect on your long term goals for the work and communicate those with potential mentors.
- Resources/Getting Paid: Different research opportunities will have access to different types of research facilities and resources. Do you want to get paid? Independent outreach may lead to more frequent unpaid opportunities compared to structured/funded programs, but there are still many ways to fund your work. Structured programs often provide more resources, including funding, equipment, and facilities.
- Research Setting: What type of setting are you interested in? A lab, field work, library, virtual, hospital, community organization, non-profit, clinic, etc? If you don’t know what you may like, how could you gain more insight?
- Research Topic: Is there a particular academic topic or question you are hoping to investigate? Are you open to the topic and more interested in the research skill you might get to practice?
- Publication and Presentation: Are you interested in presenting and publishing your work? Talk with your faculty mentor to explore options in this area. All students are encouraged to present at the Undergraduate Research Symposium.
- Personal Goals: What other goals do you have for getting involved? What skills are you hoping to practice or learn? Reflecting on your personal goals will help your faculty mentor help you get the most out of your experience.
PI: Principle Investigator (person leading the research)
Research Center/Institute
- Founded and funded for doing research, group of labs/projects
- Exists at universities, hospitals, non-profits, government, think tanks, etc.
- Multidisciplinary, works across disciplines
- Research centers tend to engage in long-term, ongoing research efforts, often spanning multiple projects
Research Lab/Group
- A research lab is a smaller, more specialized facility or unit within a research center, university department, or organization
- They may be run by a principal investigator or lead researcher and consist of a team of researchers, technicians, and students
- Research labs are more project-oriented and often have a defined timeline for their research efforts
Research Project
- A research project is a specific, time-limited endeavor with a well-defined research question, objectives, and scope
- It can be conducted within a research lab, research center, or as an independent effort
- Research projects have a specific duration and are designed to answer a particular research question or solve a specific problem
Often the most time consuming part of undergradaute research is learning what opportunities exist. It's time to put on your researcher hat and spend some time online doing research into faculty whose work aligns with your interests and what structured progrIn addition to the resources below, the Office of Undergraduate Research has a great Getting Started page.
How to Find Research Opportunities
Office of Undergraduate Research
UW Research Centers
Departmental Websites
- Most departmental websites will have a research section that shares current and past projects. Search UW "department name", then look for a research tab at the top.
- Example: Psychology, Econ
College of Arts and Sciences Research
Structured Undergraduate Research Programs
Data Science as a Research Topic
Additional Data Science Programs (may be non-research)
Reaching Out to a Faculty Mentor about Research Opportunities
Once you have identified a faculty whose work you are intersted in, it's time to reach out. Reaching out can be intimitating. The Office of Undergraduate Research has a website to support you in this process.
Reach Out to a Mentor
Data Science Research Skills
Data science skills are highly valuable in research across various disciplines. These skills help researchers collect, analyze, and interpret data, allowing them to draw meaningful insights and make evidence-based conclusions. Prior to engaging in research, reflect on the skills you have experience with, the skills you hope to practice during the research experience, and the skills you hope to gain. This reflection will help your faculty mentor best support you. Intrapersonal, communication, and critical thinking skills are just, if not more, important as the technical skills. Demonstrated curiosity in the research process can not be overstated.
Statistical Analysis
- Researchers need a strong foundation in statistics to perform hypothesis testing, regression analysis, and other statistical techniques to extract patterns and relationships from data.
Data Collection
- The ability to gather, clean, and preprocess data is crucial. This includes skills in data extraction, data cleaning, and data transformation.
Data Visualization
- Data visualization skills help researchers communicate their findings effectively. Proficiency in tools like Matplotlib, ggplot2, or Tableau can make complex data more accessible.
Machine Learning
- Machine learning algorithms are used to build predictive models and classify data. Researchers may need to apply techniques like decision trees, support vector machines, or neural networks.
Data Mining
- Researchers can use data mining techniques to discover patterns, anomalies, and relationships within large datasets.
Programming
- Proficiency in programming languages like Python or R is essential for data manipulation, analysis, and scripting.
Database Management
- Knowledge of database systems, including SQL, is necessary for working with structured data and querying databases for research purposes.
Big Data Technologies
- Familiarity with big data tools is important for handling and processing large datasets.
Data Ethics
- Ethical considerations are crucial in research, especially when handling sensitive or personal data. Researchers should be aware of privacy regulations and ethical data practices.
Data Storytelling
- Researchers need to communicate their findings effectively, which includes the ability to create compelling narratives from data.
Domain/Industry Knowledge
- Understanding the specific industry or field of research is essential for framing research questions, selecting relevant data sources, and interpreting results accurately.
Experiment Design
- In experimental research, knowledge of experimental design principles is essential for planning, conducting, and analyzing experiments.
Time Series Analysis
- Time series analysis helps researchers understand the underlying causes of trends or systemic patterns over time.
Geospatial Analysis
- Geospatial analysis is used to add timing and location information to traditional types of data and to build data visualizations.
Text Analysis
- For research involving text data, natural language processing (NLP) and text mining skills are valuable for extracting insights from textual content.
Data Security
- Researchers need to ensure the security and integrity of research data, especially in studies involving sensitive or confidential information.
Version Control
- Using version control systems help track and manage changes to software code, ensuring reproducibility and transparency in research
Collaboration and Communication
- Effective communication and collaboration skills are essential for sharing findings, collaborating with peers, and presenting research results.
