I turn messy human behaviour into clear, testable insights.
I’m a Psychology senior at Özyeğin University (English-medium BA), focused on behavioural & consumer research with a strong interest in data science. I enjoy building studies, cleaning and analysing data, and communicating results in a way that feels practical and decision-ready.
Current focus
Behavioural Data Science
research → analysis → insightTools
SPSS · R · Python · SQL
and Qualtrics for data collectionResearch style
Experimental + psychometrics
with clean reporting & visualsLanguage
English (C1) · Turkish
IELTS 7.5 (2025)Elif Parıldar
Psychology (BA) · Özyeğin University
Interested in consumer behaviour, choice architecture, and computational social science.
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What I’m looking for
internships / graduate roles
Consumer Insights · Behavioural Analytics
Roles where data + psychology directly shape product, marketing, or user decisions.
Structured, measurable impact
I’m most motivated when outcomes can be measured, improved, and communicated clearly.
About
My path: psychology → analytics → behavioural data science
My academic interest started with a simple question: why do people behave the way they do?
Over time, I realized I’m especially interested in questions that require data to answer — how decisions form
under complexity, how choice environments shape behaviour, and how small design cues can reduce cognitive burden.
A key turning point was designing and running studies end-to-end, including survey/experimental design,
data cleaning, and analysis. I enjoy combining behavioural theory with tools like SPSS, R,
Python, and SQL to produce results that are both meaningful and actionable.
Education
2022–present
Özyeğin University
BA in Psychology (English-medium) · Senior year
Honors: 50% Scholarship
Academic direction
Interdisciplinary MSc programs bridging behavioural science and data science.
Selected Projects
Click any card for details (methods, sample, results).
Skills
Tools I use to move from question → data → insight
Research & Analytics
method + interpretation
Study design
Between-subjects designs, pre/post designs, attention checks, scale construction and validation.
Statistics
Descriptives, ANOVA, MANOVA (planned/used), reliability (Cronbach’s α), EFA, correlation and reporting.
Data workflows
Cleaning, feature creation, exploratory modelling, reproducible notebooks, structured queries for analysis.
Tools
what I’m comfortable with
Reporting style
I aim for outputs that a non-technical stakeholder can still trust: clean tables, readable plots, and short, evidence-based conclusions.
Working strengths
Detail-oriented, structured thinking, teamwork, and always trying to produce a concrete final deliverable.
Certifications & Training
Completed + ongoing learning
Completed
Coursera
Data Science Math Skills
Core foundations for quantitative work: algebra, functions, and mathematical intuition for data science.
SQL for Data Science
Writing analytical queries, joins, filtering, aggregation, and turning questions into structured SQL logic.
SQL Problem Solving
Practice-focused SQL problem solving for analysis workflows.
Ongoing
Europe Coding School
Project Management (PMP Training)
Structured project planning mindset: scope, timelines, risk, stakeholder communication, deliverables.
Data Science Specialization
Strengthening end-to-end analysis skills and practical data science workflows.
English proficiency
Comfortable communicating research and technical work in English.
Contact
Let’s connect (and build something measurable)
If you’re looking for someone who can handle both the behavioural thinking and the data execution — from designing the study to reporting results — I’d love to talk.
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Quick facts
at a glance