7 Data Analysis Skills Every UCSD Bioengineering Major Should Master: Daniel’s Story

The term “data analysis” refers to a broad set of procedures that facilitate the gathering, cleansing, structuring, understanding, and interpretation of data. Data analysis techniques are widely used across dozens of academic fields; they are particularly foundational, however, in STEM disciplines like bioengineering, in which researchers must collect and process huge datasets to draw meaningful conclusions. 

The undergraduate bioengineering program at UC San Diego aims to train students in data analysis skills for biotechnology, biosystems, and bioinformatics. The broader goal of the program is to bridge biology and technology, preparing graduates to solve complex healthcare and engineering problems. 

To learn more about how students use data analysis skills on a daily basis inside of the bioengineering department, we reconnected with Polygence alum Daniel Markarov. Daniel is a current undergraduate student at UCSD majoring in bioengineering. He is also a research assistant in cellular and molecular medicine; his previous projects have applied machine learning to cancer research.

Daniel outlined for us 7 essential data analysis skills that bioengineering majors at UCSD should focus on to maximize their learning and success. Later on in this article, we’ll list and discuss those 7 core skills.

Why Data Analysis Skills Are Crucial for UCSD Bioengineering Majors

Data analysis techniques are used in nearly all cutting-edge research and innovation in healthcare and biotechnology. Bioengineers and biomedical scientists use machine learning (ML) and other computational tools to process and analyze medical data, improve data models, and improve understanding. In Daniel’s case, he and his research program mentor used ML to streamline the processing of large datasets. Daniel fed multiple forms of datasets (including images and data tables) into an ML model and evaluated its effectiveness compared to traditional data processing techniques.

Common Challenges in Developing Data Analysis Skills

Data analysis skills are highly valuable for aspiring bioengineers, but learning to work with complex biological datasets can be challenging. Many students struggle to bridge the gap from the theoretical knowledge taught in lecture courses to their hands-on lab experiments and computational work. Others fall behind when adapting to fast-paced, university-level coursework. This balance becomes especially challenging when students begin to take on additional research responsibilities outside of class.

Mastering Core Data Tools for Bioengineering

In today’s research environment, bioengineers are expected to be comfortable not only with laboratory techniques but also with data analysis software and programming languages. Beyond Python and R, which Daniel highlighted, students can also benefit from learning:

• SQL – to query and organize large biological and clinical databases.
  Excel (advanced functions and pivot tables) – still one of the most widely used tools for quick data exploration and reporting.

• Tableau or Power BI – to build dashboards and communicate findings visually to collaborators, professors, or research teams.

Proficiency in these tools helps bioengineering students bridge the gap between raw experimental data and actionable insights, a critical skill both in academia and industry.

Communicating Science Through Data Storytelling

One skill often overlooked in STEM fields is data storytelling. Bioengineering students who can translate their findings into clear, accessible narratives stand out in labs, classrooms, and future job applications.

For example, imagine running a machine learning model on cancer datasets. The technical results are valuable—but being able to present those results in a way that both a lab partner and a non-technical audience can understand is even more powerful.

Strong storytelling means:

• Framing the research question clearly.

• Visualizing data so trends are immediately clear.

• Explaining outcomes in terms of their real-world impact (e.g., “This model could reduce diagnostic time for breast cancer.”).

This skill is especially important for bioengineers collaborating with medical professionals or pitching innovative biotech ideas.

To guide incoming students on how to invest their time and energy, we’ll go over the 7 most valuable data analysis skills specifically for bioengineering students.

Daniel’s 7 Essential Data Analysis Skills for Bioengineering Students

Data Collection and Cleaning

Gathering and cleansing data is a top skill for undergraduate bioengineering students to practice, according to Daniel. The process of collecting datasets can vary depending on the project. Daniel says that for his Polygence project, he actually used existing datasets. “Working with my mentor,” he says, “we collected datasets and analyzed breast cancer tumor data to predict histologies and malignant tumors.” 

For large datasets, properly cleaning the data is critical. Data cleaning typically begins with a data audit to identify anomalies, highlight duplicate or irrelevant data points, and pinpoint missing values. Once duplicate and irrelevant data are removed, the analyst addresses structural errors in the set, filters outliers, and handles missing data based on best practices.

Statistical Analysis

A strong foundation in statistics is a must-have for bioengineering students, who frequently rely on biostatistical methods to analyze and interpret large datasets. After collecting and cleaning data, biomedical engineers and bioengineering students must apply their biostatistics skills to understand distributions, test hypotheses, and determine the significance of their results.

Programming for Data Analysis

Data analysis for biology and biomedicine is typically done using Python or R, both of which allow for fast data manipulation and modeling by scientists and engineers. Basic fluency in one or more of these programming languages is a necessary skill for bioengineering students at UCSD. By the end of the program, students are expected to be fully functional and working towards mastery. For reference, the department recommends that students transferring from 2-year programs take the “highest level of introductory computer programming language course offerings.”

Machine Learning Fundamentals

Daniel also highlights machine learning as one of the most valuable skills for bioengineering students to develop. Machine learning is a statistical technique that is frequently used in biomedical and bioinformatics applications to classify data and predict outcomes. Daniel was initially exposed to ML techniques during his Polygence project: “I got to apply ML to real-world problems in healthcare,” he says, “which motivated me to dive deeper into this field.”

Data Visualization

The goal of data visualization is to communicate findings and meaningful insights gleaned from data that has been collected, cleaned, and analyzed. Data visualization tools and skills allow analysts to create clear charts and graphs to deliver their findings to other researchers. One of the goals of UCSD’s program is to prepare graduates with the “ability to communicate effectively with a range of audiences.” This is an indispensable step for bioengineering students and for professional practicing engineers in fields like biomedical instrumentation, biomedical computation, biosystems engineering, and more, many of whom regularly communicate with non-technical stakeholders. 

Critical Thinking and Problem Solving

Critical thinking and problem solving are soft skills that directly contribute to students’ success across engineering disciplines. In the bioengineering major at UCSD, students are expected to critically interpret data-driven findings to guide biomedical research and biotechnological design decisions. After graduation, practicing engineers continue to depend on these soft skills to direct their research in postgraduate academic programs and related industry projects.

Collaboration and Communication

Bioengineering is an interdisciplinary field that requires individuals with highly specialized knowledge to collaborate across teams and labs to solve complex engineering problems. For example, specialists in biomechanics frequently work closely with medical professionals, prosthetics designers, biomedical device developers, biotribologists, computational biologists, neuromechanics specialists, and more. The ability to clearly communicate, share data, and effectively collaborate across teams and with experts in diverse fields is viewed as a valuable soft skill that bioengineering students should aim to master.

How Daniel Balances Lab Work and Data Analysis at UC San Diego

While pursuing his B.S. in Bioengineering at UCSD, Daniel also works in the Zhou Lab supporting research on DNA replication and damage response mechanisms. “As a freshman undergrad,” he says, “I handle a lot of lab maintenance like autoclaving and preparing petri dishes, but I also work under master’s students on experiments related to DNA damage repair. These experiments range from chromatin assays to spotting assays and survivor testing. It’s a really interesting field, and I’m excited to be part of it.” 

UCSD is home to many of the world’s leading scholars in biology and bioengineering. Working in an advanced research lab is a valuable opportunity for undergraduate students to apply the theories, statistical techniques, and data analysis skills learned through coursework to practical experiments.

 Career Relevance and Job Market Demand

Data analysis isn’t just a helpful skill—it’s a career accelerator. According to industry research, demand for professionals with data analysis and bioinformatics expertise is growing rapidly in biotechnology, pharmaceuticals, healthcare, and academic labs.

For UCSD students, that means:

• Building these skills now positions you for competitive internships in biotech firms and research institutes.

• Employers value hands-on project experience, like Daniel’s Polygence work with cancer datasets, as much as classroom learning.

• These abilities also open doors beyond traditional lab work, into roles like clinical data analyst, bioinformatics specialist, or healthcare consultant.

By investing in technical and analytical training early, students set themselves up for both academic and professional success.

Advice for Incoming UCSD Bioengineering Students

Incoming students in the Bioengineering major are highly advised to seek out opportunities to engage with ongoing research at the university. To maximize their practical data skills, students should take advantage of all opportunities to gain hands-on experience with large datasets through projects and internships, which can take place on a part-time basis during the academic year or during the summer.

For new students who are trying to balance coursework and research, Daniel says, “One really important thing is to know how to advocate for yourself. There are so many resources here—supplementary instruction, tutoring, study groups—but you have to seek them out. Don’t be afraid to ask for help or take advantage of what’s available. That’s how you’ll get the most out of your experience here.”

Finally, try to embrace collaboration. Although group projects can be particularly challenging (and, at times, frustrating), working in groups enhances learning and amplifies research impact.

Looking Ahead: From Classroom Skills to Research Innovation

Data analysis techniques like the ones Daniel uses drive scads of breakthrough discoveries in bioengineering. Daniel’s story illustrates how intentionally developing a solid foundation in basic data analysis skills and ML techniques can enable even first- and second-year undergraduate researchers to participate in cutting edge research projects at UCSD and beyond. These research experiences prepare students to spearhead innovative projects after graduation, whether in bioengineering, bioinformatics, biotechnology, or another related STEM field.

Students interested in bioengineering and bioinformatics can conduct independent projects through a research mentorship program or, alternatively, through beginner-friendly courses like Polygence Pods. Current Pods topics include:

• Data analysis methods for research 

• AI in STEM

• Precision Medicine

• Tissue Engineering

• and more

Apply now to learn how Polygence can help you build and strengthen data analysis skills for college and beyond!