Nabila Sultana
Applied Statistician · ML Research Mentor
Making your analysis reproducible, not just correct
Dhaka, Bangladesh
PhD candidate in statistics with 5 years of experience mentoring students on quantitative and machine-learning research. I focus on getting your analysis reproducible and your results defensible under questioning.
Areas of expertise
- Hypothesis formulation
- Experimental design
- Survey sampling strategy
- Python (pandas)
- R (tidyverse)
- Data cleaning pipelines
- Regression & classification
- Time-series analysis
- Model validation
- Git & version control
- R Markdown / Jupyter reports
- Code documentation
- Results visualization
- Journal formatting
- Preprint strategy
How mentoring works
Turn a vague idea into a testable hypothesis.
Decide between primary collection, public datasets or APIs.
Build a clean, documented pipeline in Python or R.
Fit and validate models without overfitting your story.
Turn output into figures and prose a reader trusts.
Format and submit to the right quantitative venue.
Achievements
- 5+ years mentoring quantitative research
- 12 papers co-authored across ML & applied statistics venues
- Built reproducible-research workshops for 3 universities
- Mentored 80+ students on thesis data analysis
Research interests
Tools & software
Who can work with me
- Undergraduate & Master's students
- PhD scholars in quantitative fields
- Data-focused NGO teams
- Independent researchers
What you'll learn
- Formulating testable hypotheses
- Sampling & survey statistics
- Data cleaning in Python / R
- Regression & classification modelling
- Time-series analysis basics
- Model validation & avoiding overfitting
- Reproducible reporting (Jupyter / R Markdown)
- Data visualization for papers
- Version control with Git
- Preparing a manuscript for submission
Mentoring style
Ready to start your research journey?
Book a paid 1:1 session with Nabila Sultana and get step-by-step guidance toward publication.