The applied practice of theoretical data science.

Who Am I?

I'm Mark Scully, a consultant specializing in Data Science. Since Data Science lacks an agreed upon definition, I'll give my definition as applying scientific principles and techniques from math and computer science to making use of data. I've worked on projects in Machine Learning, Data Mining, Predictive Analytics, Natural Language Processing, Computer Vision, Machine Vision, Bioinformatics, Neuroinformatics, Biomedical Imaging, and Data Fusion, among others. I've done everything from algorithm development to low level optimized implementations and big data infrastructure to web applications. I'm the Data Science equivalent of a full-stack web developer.

I most often work with python and C++ but have experience with High Performance Computing, High Throughput Computing, Hadoop, C, Postgres, and CouchDB. That said, I pick up new technologies fast, and am no stranger to R, Matlab, Hive, Pig, Mahout, Vowpal Wabbit, OpenCV, and many more. I recieved my Masters in Computer Science, focused on Machine Learning, from the University of New Mexico.

My Approach To Data Science

My approach is an applied one. Data Science and Machine Learning are most interesting when they are applied to other domains and many fields already have Data Science at their heart. I appreciate all the work done on pure theory and make use of it often, but I prefer to apply that knowledge to real world problems. When doing so I follow these principles: