Daniel Gerlanc

Daniel Gerlanc has worked as a data scientist for more than decade and written software professionally for 15 years. He spent 5 years as a quantitative analyst with two Boston hedge funds before starting Enplus Advisors. At Enplus, he works with clients on data science and custom software development with a particular focus on projects requiring an expertise in both areas. He teaches data science and software development at introductory through advanced levels. He has coauthored several open source R packages, published in peer-reviewed journals, and is active in local predictive analytics groups.


Academic Statistical Consulting

  • Critchfield AC, Paulus JK, Farez R, Urato AC. “Abnormal Analyte Preeclampsia”: Do the second trimester maternal serum analytes help us to differentiate different types of preeclampsia? [Submitted to Pregnancy in Hypertension]
  • Paulus JK, Switkowski KM, Preston IR, Hill NS, Kari E. Roberts KE. Initiation of a Case-Control Study of Pulmonary Arterial Hypertension in Women. Poster presented at American Thoracic Society Annual Meeting, May 2012.

Invited Talks

  • Programming with Data: Python and Pandas. ODSC East, May 2018.
  • Round Trip Client-Side COPY for High Volume Postgres Inserts, PGConf 2017 NYC. November, 2017.
  • Programming with Data: Python and Pandas - Boston Data Festival, Fall 2016
  • Introduction to R, Open Data Science Conference, June 2015.
  • Open Source Finance with R, Boston Data Mining, December 2013.
  • Hands on Machine Learning, Boston Predictive Analytics Machine Learning Workshop, December 2012.
  • Predicting Customer Conversion with Random Forests, New England AI Meetup, October 2012.
  • Random Forests Lightning Talk, Predictive Analytics World, October 2012.
  • Intermediate Regression Topics, Boston Predictive Analytics Meetup, July 2012.


  • Genetic Association Studies - Teaching Assistant - Tufts University Medical School - 2013
  • Computation for RNA Sequencing - Teaching Assistant - Tufts University Medical School - 2013
  • Introduction to Data Science and Machine Learning – General Assembly Boston – July, 2013
  • Introduction to Python and Pandas for Data Analysis - Corporate Training - 2016 - Present

R Packages

  • bootES: Bootstrap Effect Sizes is a free, open-source software package for R which is a language and environment for statistical computing. BootES computes both unstandardized and standardized effect sizes (such as Cohen’sd, Hedges’s g, and Pearson’s r) and makes easily available for the first time the computation of their bootstrap confidence intervals (CIs).
  • portfolio: Classes for analysing and implementing equity portfolios.
  • backtest: The backtest package provides facilities for exploring portfolio-based conjectures about financial instruments (stocks, bonds, swaps, options, et cetera).


  • New England AI Meetup: Organizer. Responsible for coordinating speakers for NEAI monthly events.
  • Boston Data Mining: Co-Founder and Organizer
  • Venture Cafe: Volunteer