I am a government professional with leadership, open data external engagement, website content, project management, editing/writing and patent examination experience. I graduated in May 2018 with a Master’s of Science in Data Science from Indiana University. This blog covers a wide range of data topics presented with a non-technical audience in mind.

Laura H. Kahn

lkahn@indiana.edu | @LauraHKahn  | GitHub


United States Patent and Trademark Office (2004 – present)

Data product advisor and customer service liaison; Led and managed a team of technical librarians; Conducted research and presented findings as a biomedical patent examiner.

Indiana University (2016-2018)
Data science project experience in graduate program including data cleaning, exploratory data analysis with univariate statistics, data manipulation and mining (Python Numpy and Pandas libraries); feature selection; machine learning predictions including Natural Language Processing (NLTK and scikit-learn library); Data Visualization with Matplot library, Pyplot library, D3 JavaScript Library, Tableau and QGIS; Beginner-level Spark cluster computing system (ML library) analysis.


Indiana University, M.S. Data Science Candidate, January 2016 – May 2018
North Carolina State University, B.S. Textile Engineering and B.A. Spanish, August 1999 – May 2004
Universidad de Santander – Study Abroad, Spanish, September – December 2003
Universidad Technológica del Perú – Study Abroad, Spanish, June – August 2001




Used multilayer perceptron neural networks to classify World Bank household survey features.



Predicted daily coffee futures closing prices using Decision Tree Regression and Ridge Regression algorithms.



Quantified and visualized the correlation between coffee rust, weather variables, production and futures prices in Brasil, Colombia and Papua New Guinea using regression techniques.



Predicted 2018 USAID economic aid disbursements with support vector machine, decision tree, Naive Bayes and k-NN classifiers; Poster presented at Jupyter Conference.



Used phrase filtering and natural language processing techniques including Snowball stemmer, custom stopwords and Spanish corpus for annotating 1.32 million Tweets from Caracas, Venezuela and predicting the neighborhood where the Tweet originated; Research selected for SciPy Conference

Energy Use in the Middle East
Data munging, interpretation and visualization of energy consumption.

Data Science in 90 Seconds – YouTube monthly series that explains key data science principles in plain language for a general audience.



Kahn, L. Spatiotemporal twitter analysis of the Venezuelan food crisis. Journal of Food Processing Technology, 8:5. DOI: 10.4172/2157-7110-C1-062, p. 51. Abstract presented at the 2nd International Conference on Food Security and Sustainability. https://www.omicsonline.org/conference-proceedings/2157-7110-C1-062-011.pdf



The findings, interpretations and conclusions expressed herein are those of the author. All content provided on this blog is for informational purposes only.

The author does not make representations as to the completeness of any information on this site or found by following any link on this site. The author will not be liable for any errors or omissions in this information nor for the availability of this information.

Many of the links on this blog will take you to sites operated by third parties. The author does not endorse these sites, their opinions, or any products they may offer. These third party links are offered to stimulate discussion and thinking on topics related to open governance, including, transparency, accountability, citizen participation and technology and innovation.

All of the content on this blog is intended for the personal, non-commercial use of our users.


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