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Data Science Stack with MongoDB and RStudio

In this talk by Winston Chen, Lead Software Engineer at Fliptop, discover how to build an efficient data science platform using MongoDB and RStudio. Learn about predictive lead scoring, integrating CRM and social data, and employing over 3000 signals to develop revenue models. Explore practical solutions for data processing and insight extraction using R’s capabilities with MongoDB. We’ll discuss the limitations of conventional big data frameworks and demonstrate effective methods for leveraging R and RStudio in data science applications.

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Data Science Stack with MongoDB and RStudio

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  1. Data Science Stack with MongoDB and RStudio Building up an easy data science platform with RStudio server on top of your MongoDB Winston Chen – Lead Software Engineer

  2. What does Fliptop do? • Predictive Lead Scoring, using data science • Pull opportunity/lead/contact data from CRM • Aggregate company data and social data from various data sources and the internet • Over 3000 signals • Build conversion/revenue model • Predict lead conversion and revenue

  3. OurPlatformStack • Java/Scala • Liftweb • JMS/Storm • MongoDB/MySql

  4. OurMachine Learning Stack • Python • Numpy/Scipy/Pandas • Bottle (RESTful Server)

  5. So, where is R then? • Problem: • Data is stored in MongoDB • Sales Lead Data • Sales Opportunity Data • Sales Contact Data • It’s hard to view/digest/process data on the fly using MongoDB console • (X) Text processing for insight extraction? • (X) Prototype cool machine learning algorithms on the fly? • Solution: • R and Rstudio Server • Why not scala? • Why not python/ipython

  6. MongoDB Console & Query

  7. Rstudio Server

  8. Pull MongoDB data into R data frame • rmongodb (https://github.com/gerald-lindsly/rmongodb) Transform Into a R data-frame

  9. 1 – Get the total count of your data set

  10. 2 – Construct Vectors for each column

  11. 3 – Loop through curser and insert values Where are my apply functions?- Too bad. We are using mongo cursor :P

  12. 4 – Go into sub bson block to extract data (optional)

  13. 5 – Construct data frame and return We now have a data frame to play with from MongoDBbson. You are able to get the full example code here: http://goo.gl/tlyyXp

  14. This is NOT a BIG DATA Stack • It takes around 1 min to process 900Mb+ of bson from Mongo. • BIG data stack – Data should fit into the ram • Most of the data in the world is not big anyways. • It works fine for us (m1.large machine in AWS) • CRM data is never big, not even after we pull in 3000+ additional signals. • The term ‘Big-Data’ is seriously overrated, ‘Data Science’ however, is the key term here.

  15. @ Fliptop, we now use Rstudio to do • Data Insight Extraction • Algorithm prototyping

  16. If you REALLY want BIG Data • Look into: HDFS + Pig/Hive + Hue(any other suggestion from the audience here?)

  17. QA • Winston Chen • Personal Blog: http://winston.attlin.com/ • Twitter: @wingchen83 • winston@fliptop.com • Fliptop is hiring Data Scientists. Please email to:winston@fliptop.com

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