David Springate – https://www.linkedin.com/in/daspringate

I am interested in extracting meaningful insights from large, messy and complex data. I have worked across various domains: market research, health informatics, ecology, genetics, agriculture and bioinformatics.

► Statistics expert: Survival analysis, multiple and mixed-effect regression modelling, simulation and bootstrapping, meta-analysis, predictive analytics. Teaches biostatistics to undergraduate and masters students. Fellow of the Royal Statistical Society. Further experience in clustering and classification techniques (randomForests, SVM, PCA, boosting), time series analysis, text-mining and natural language processing.

► Developer: Programming seriously since 2008. Expert in R and Python. Fluent in Javascript, Clojure, HTML/CSS, SQL, Lisp, C, Processing. Comfortable within functional, object orientated and declarative programming paradigms. Involved in teaching programming at university. Full-stack Web development with Django-Postgres-JS-jquery-bootstrap-nginx. Experienced with Linux/bash, git, Emacs, Latex.

► Data manipulation and visualisation: Expert with all core R-based data munging techniques and state of the art tools such as dplyr, reshape2, data.table, sqldf. Expert with R visualisation, particularly ggplot2, Rmarkdown. Experience with ggvis, d3, rickshaw, Processing.

► Databases: Frequent user of Postgres, SQLite, Redis, InfluxDB. Python/R ORMs. Also familiar with MongoDB and Hadoop.

► Communication: Spoken at several national and international conferences. Author on around 20 scientific articles. Presented tutorials at R user groups and teaching seminars.


Specialities: data science | statistics | R | Python | machine learning | databases | Web development | functional programming | Internet of Things | data mining | eHealth | PostgreSQL | data visualisation | text mining | predictive analysis | Electronic Health Records | social network analysis

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