I work with clients on defining, implementing and managing data analysis and data management projects. My expertise lies at the intersection of Statistics, Computer Science and Business acumen. Specific areas of interest to me are education, healthcare, finance, real estate, and energy. Analysis: R (statistics), Python (data munging), Java+Scala (machine learning / big data), VB.NET/Excel (financial modelling) ** Apache Hadoop, Hive, Spark with MLlib (big data ecosystem) ** MongoDB, Neo4j (NoSQL) + MySQL, Monetdb (SQL) ** Gephi (networks/social graph processing) ** Weka (machine learning) Visualization: Tableau (rapid dashboarding), Shiny (R package for interactive statistical dashboards) Environments: Linux, Windows, and Mac (least experience) Productivity: Git (source code ... control) Web Applications: Play 2 Framework (modern Java/Scala-based, reactive websites) I believe the key qualification to be a Data Science practitioner is an analytic and curious mindset. That said, many hiring decisions seem to hinge on specific skills. Let me know if there's a tool or technology you don't see on my list, and I'll let you know if I've worked with it before.
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|Name||Score (out of 5)||Time to Complete|
|Venture Capital Test||4.25||Top 20%||27 mins|
|Microsoft Excel 2010 Test||4.40||Top 10%||33 mins|
|Data Interpretation Test||4.00||Top 10%||41 mins|
|MS Excel 2007 Test||4.60||Top 10%||27 mins|
|Email Etiquette Certification||4.45||Top 10%||13 mins|