Kexin Cha | Product Design & UX/UI Portfolio

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Stanford Data Ocean

by Stanford University

Zero-to-one product design of an AI-powered learning platform for health data analysis.

Year

2022-2026

My Roles

UX/UI & Graphic Design

Instructional Design

Product Management

Team

Engineer (1)

Stakeholder (1)

Videographer (1)

Data Scientists (rotating)

Life Scientists (rotating)

Industries

B2C Education

Health

Overview

Build career-ready skills in health data analysis.

Stanford Data Ocean delivers hands-on biomedical data analysis courses that empower underprivileged learners to understand human health through data.

Since 2023, we’ve launched three Stanford-certified programs with 31 learning modules and an AI tutor, with 97% graduates of reporting skills that advance their careers and communities.

Impact

$3.2 million

Tuition saved

97 %

Career growth

85 %

Alumni recommended

77 %

AI Tutor satisfaction

Startup Founder

Philippines

Build a company

He applied what he learned from the course to build his company, while reflecting on how scarce bioinformatics education and resources remain in the country.

Research Scientist

United States

Search for a job

What began as a strategic move to sharpen her professional edge quickly transformed into a career breakthrough.

Computer Scientist

United States

Mentor next generation

Originally from Nigeria, he plans to mentor upcoming data scientists and explore the cutting-edge intersection of AI and precision medicine.

Scientist

Pakistan

Upskill on the job

Realizing that her bachelor's degree had left a void in her professional toolkit, she took the initiative to master the interdisciplinary skills that her university don't provide.

Startup Founder

Philippines

Build a company

He applied what he learned from the course to build his company, while reflecting on how scarce bioinformatics education and resources remain in the country.

Research Scientist

United States

Search for a job

What began as a strategic move to sharpen her professional edge quickly transformed into a career breakthrough.

Computer Scientist

United States

Mentor next generation

Originally from Nigeria, he plans to mentor upcoming data scientists and explore the cutting-edge intersection of AI and precision medicine.

Scientist

Pakistan

Upskill on the job

Realizing that her bachelor's degree had left a void in her professional toolkit, she took the initiative to master the interdisciplinary skills that her university don't provide.

Startup Founder

Philippines

Build a company

He applied what he learned from the course to build his company, while reflecting on how scarce bioinformatics education and resources remain in the country.

Research Scientist

United States

Search for a job

What began as a strategic move to sharpen her professional edge quickly transformed into a career breakthrough.

Computer Scientist

United States

Mentor next generation

Originally from Nigeria, he plans to mentor upcoming data scientists and explore the cutting-edge intersection of AI and precision medicine.

Scientist

Pakistan

Upskill on the job

Realizing that her bachelor's degree had left a void in her professional toolkit, she took the initiative to master the interdisciplinary skills that her university don't provide.

Problem

PROBLEM

Cost, time, and training make health data analysis skills inaccessible.

Unlocking health data insights requires expertise in biology, data science, coding, and ethics- the kind of interdisciplinary training typically reserved for professional degrees.

Yet most people can’t afford it, many schools lack the data and teaching infrastructure, and those making ends meet simply don’t have the time.

"…in-depth educational materials at the intersection of AI and healthcare are rarely curated in Africa, so gaining access to them is usually from the global north. These courses are priced based on the standard of the global north."

Painpoints

73 %

Cannot afford courses

21 %

Few interdisciplinary curricula

6 %

Limited time outside of work

Easy to start

Jump into hands-on learning without friction.

DESIGN GOAL #1

DESIGN GOAL #1: EASY TO START

Jump into hands-on learning without friction.

Most beginners quit before analyzing any data because they get stuck installing tools and fixing errors to set up the cloud environment for analysis.

So we removed setup completely and let learners open a ready-to-use workspace instantly. They could focus on the core user flow:

Most beginners quit before analyzing any data because they get stuck installing tools and fixing errors to set up the cloud environment for analysis.

So we removed setup completely and let learners open a ready-to-use workspace instantly. They could focus on the core user flow:

1. Open a Jupyter notebook. → 2. Load datasets. → 3. Analyze data.

1. Open a Jupyter notebook.

2. Load datasets.

3. Analyze data.

Open access

DESIGN GOAL #2

DESIGN GOAL #2: OPEN ACCESS

No cost for low and middle-income learners.

In a survey we distributed, 73% learners cannot afford to pay for the courses, so we provide a code to learners earning under $70,000 USD per year, allowing them to unlock the full course at no cost.

Scale metrics

5,000 +

Global learners

90 %

Learners study for free

106

Countries

Good comprehension

DESIGN GOAL #3

Complex made simple with stories & visuals.

I partnered closely with data scientists and life scientists to create 31 beginner-friendly learning modules, removing dense technical language and replacing it with clear analogies and engaging illustrations so learners could grasp complex ideas quickly.

Growing completion

DESIGN GOAL #4

DESIGN GOAL #4: GROWING COMPLETION

Learning roadmap turns curriculum into momentum.

I designed the course as a clear roadmap. Each module unlocks the next, and milestone badges make progress visible.

Graduation rates reached 50.5% (self-paced) and 85.7% (cohort-based), far above typical MOOCs at 5–15%.

I designed the course as a clear roadmap. Each module unlocks the next, and milestone badges make progress visible.

Graduation rates reached 50.5% (self-paced) and 85.7% (cohort-based), far above typical MOOCs at 5–15%.

Graduation rate

51 %

Our self-paced programs

86 %

Our cohort-based programs

5-15 %

Other self-paced programs

Evaluate learning

DESIGN GOAL #5

DESIGN GOAL #5: EVALUATE LEARNING

Measure learning outcomes and confidence growth.

To ensure real learning, I added short quizzes, a final certification exam, and pre- and post-program surveys to measure both skill improvement and confidence growth.

Learning outcomes metrics

70 %

Assessment accuracy

48 %

Confidence gain on skills

97 %

Career advancement

Scalable support

DESIGN GOAL #6

DESIGN GOAL #6: SCALABLE SUPPORT

AI Tutor: Scaling student support without scaling cost

With limited budget to hire instructors across time zones, I designed a GPT-4–powered AI Tutor so learners could get instant help anytime. The goal is to reduce drop-off, keeping students supported, and maintaining a high graduation rate without expanding the teaching team.

Performance metrics

98 %

Answers are accurate

77 %

Learners are satisfied

100 %

Guardrail is effective

Want to learn more about my design process? Let’s talk.

Want to learn more about my design process?

Let’s talk.