Pandas for Large Data: Examples and Tips
13 Aug 2017 In order to successfully work with large data on Pandas, there are some ways to reduce memory usage and make sure you get good speed performance.
Read More ›Suggestions on how to make LinkedIn more relevant
04 Aug 2017 LinkedIn is a nice platform for connecting to professional peers but its real value lies, in my opinion, in its potential to the the global professional rating system. But it needs some improvement.
technology reminder hierarchy clustering
Quick Reminder: Clustering
29 Jul 2017 Quick reminder on key points regarding clustering (hierarchical and otherwise)
Read More ›Install NodeJS and NPM on Ubuntu
28 Jul 2017 Installing the latest NPM + NodeJS on Ubuntu
Read More ›Python Pickle: examples and reference
12 Jul 2017 Pickle is a well-known Python tool for saving arbitrary variable contents into file. Here are a couple of examples and tips on how you can use it to make your data science work more efficient and easily reproducible.
Read More ›technology codebuild codepipeline docker beanstalk continuous-integration continuous-deployment
Using AWS CodePipeline to Automatically Deploy and Build your App Stored on Github as a Docker-based Beanstalk Application
07 Jul 2017 A full guide on how to set up a continuous deployment pipeline using GitHub and AWS CodePipeline, in order to deploy a Docker-based Beanstalk Application.
Scala Testing with Scalatest: Reference and Examples
06 Jul 2017 Simple examples using ScalaTest to test your Scala projects. Full introduction and examples of all major testing styles supported by this framework.
Debugging NullPointerException in Apache Spark
04 Jun 2017 A lot of things can go wrong when writing distributed code in Spark. Here's a couple of ways to work around/debug NullPointerExceptions.
Read More ›7z: 7zip Usage Examples on Linux
03 Jun 2017 Examples on how to use 7zip to extract and compress data on Linux.
Read More ›machine-learning data-science best-practices
Machine Learning and Data Science: Generally Applicable Tips and Tricks
18 May 2017 A couple of general, practical tips and tricks that may be used when dealing with data science and/or machine learning problems.