Explore, analyze, and uncover answers in our Research Methods Program.
Learn how real research is done, from an initial question to publication-ready work. Enroll in a structured, hands-on program that introduces students to modern academic research practices, data analysis, and research communication. The curriculum is modeled after Stanford-style research training and emphasizes clear thinking, evidence-based reasoning, and independent inquiry. Open to middle school, high school, and college undergraduate students. Instruction is customized to each cohort’s level and goals.
2-6 Students
12 Weeks
Beginner Friendly
Live Online Instruction
Zero to Researcher Journey
Skills that travel far beyond
By the end of the Research Methods Lab, students will be able to:
✓ Write and present research at a university-level standard
✓ Communicate complex ideas clearly and confidently
✓ Apply modern data science & machine learning methods
✓ Design and execute an original research project
Who Is This Program Designed For?
✓ Students beginning their research journey
✓ Students with little or no coding experience
✓ Advanced students can incorporate more complex methods as appropriate
✓ Budding social science and interdisciplinary researchers
✓ Students interested in AI, data science, or applied research
✓ English second-language learners seeking structured academic support
One course opens several doors.
After completing our program Cura students got accepted into:
Students were awarded scholarships:
Some students went on to earn their Doctor of Philosophy (PhD) and received prestigious job offers. Our lab aims to prepare students with the data and research skills that college admissions and employers look for.
12-week curriculum
01Getting Started
Research Content
- What is research?
- Intro to literature review
Technical Content
- Development Environment setup
02Research Questions
Research Content
- Understanding the components of a research question
- Brainstorm research directions
Technical Content
- Intro to data analysis 1
- Python (Pandas)
03Feedback and Data
Research Content
- Present research question and gain feedback
- Summarizing key findings and identifying research gaps
Technical Content
- Intro to data analysis 2
- Data cleaning
- Exploratory data analysis
04Hypothesis
Research Content
- Hypothesis and experimental protocol
Technical Content
- Intro to statistical 1
- Descriptive statistics
05Introduction
Research Content
- How to write an introduction and motivation
Technical Content
- Intro to statistics 2
- Visualization methods
06Regression
Research Content
- Edit and feedback from analysis
- The role of theory
Technical Content
- Intro to Machine Learning 1:
- Regression
07Classification
Research Content
- Method section of your paper
- Conclusion
Technical Content
- Intro to Machine Learning 2:
- Classification
08Language and Drafts
Research Content
- Discussion & Acknowledgement
Technical Content
- Intro to Natural Language Processing
- Text processing
09Final Touches
Research Content
- Feedback and presentation tricks
Technical Content
- How to create citations with overleaf and putting visuals in LaTex
10Presentation
Research Content
- Presentation and feedback
Technical Content
- Advanced methods overview
11Presentation
Research Content
- Presentation and feedback
Technical Content
- Advanced methods overview
12Presentation
Research Content
- Presentation and feedback
Technical Content
- Advanced methods overview
What Makes the Research Methods Lab Unique
Modern Research Training
Students learn how research is actually conducted in universities and industry today:
How to ask meaningful research questions
How to analyze data using appropriate methods
How to interpret and communicate results clearly
Custom Labs Build
Created specifically to support students
Step-by-step Python notebooks for students with little or no coding background
Scaffolded exercises that focus on understanding, not memorization
Hands-on labs that reinforce concepts from lectures
Flexible lab extensions for advanced students who want deeper challenges
Modeled After Stanford
Research & Teaching Style
Inquiry-driven learning
Emphasis on reasoning, methods, and clarity
Focus on how researchers think, not rote procedures
Personalized Research Topics
Students develop research projects aligned with their interests, including:
AI, machine learning, and technology
Social science, economics, and policy
Psychology, education, and health
Culture, media, and interdisciplinary studies
Learn the tools researchers use.
Core Research Skills
Students will learn to:
Understand the fundamentals of academic research
Follow a complete research process:
Question → data → method → analysis → results → discussion
Read and synthesize academic literature
Think critically about evidence, validity, and conclusions
Machine Learning & Data Science Methods
All students are introduced to foundational research methods commonly used in academic work:
Linear Regression – modeling relationships between variables
Logistic Regression – classification and prediction
K-Means Clustering – pattern discovery and grouping
Natural Language Processing (NLP) – foundations of how large language models (LLMs) work, including how text is represented as data, basic text analysis, and how modern AI systems learn patterns from language
Data Analysis & Coding
Introduction to data analysis using Python
Custom Python notebooks designed specifically for beginners
Emphasis on conceptual understanding and research thinking
Gradual progression toward independent analysis
We use agents and modern agentic workflows
Academic Writing & Research Presentation
Students learn how to communicate research at a professional academic standard:
Paper structure mirrors top machine learning and AI conferences:
Abstract
Introduction
Related Work
Methods
Experiments / Results
Discussion and Limitations
Research Communication & Intellectual Confidence
Students learn how to communicate complex ideas clearly and confidently—a core skill in both academic and real-world research settings.
The program introduces students to scientific communication and dissemination, including:
Crafting clear elevator pitches for research ideas
Explaining the same project to different audiences (professors, admissions interviewers, peers, and non-experts)
Developing confidence in speaking about abstract or technical ideas
Students practice presenting their work through:
Short research talks
Conference-style presentations
Academic posters and research showcases
Student Testimonials
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“The most thing I like is code labs gave me a lot practices with machine learning, which helps me to understand how the models works and deepen my knowledges. I like the step of building logistic regression and K-means models, it helps me to practice applying machine learning to NBA data.” -Ziqian Wang
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“Writing papers with python is something I have never used before. I find it very interesting. This is my first time to come into contact with programming. The professor explained the knowledge about programming to me very patiently, carefully and clearly” -Manxi Wang
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“The professor is kind and patience, with intelligence.” -Wenkai Li
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“That it improves my critical thinking and opportunities to solve problems on my own.” -Abby Lee
