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  • 1 min read

Coding Standards for Jupyter Notebooks

Jupyter Notebook has become incredibly popular among data scientists and general users of Python and R. While the Jupyter framework is liberal and lets you be creative, it would benefit you, your team, and your readers if you define a structure and follow it. Based on my experience as developer evangelist and the author of public-facing notebooks for the last three years, I share in this article the patterns I recommend for writing data science samples using Jupyter Notebook. Read more here

  • 1 min read

House hunting — the data scientist way

At some point in time, each of us would have went through the process of either renting or buying a house. Whether we realize or not, a lot of factors we consider important are heavily influenced by location. In this article, we apply the data wrangling capabilities of scientific Python ecosystem and geospatial data visualization & analysis capabilities of the ArcGIS platform to build a model that will help shortlist good properties (houses). Read more here

  • 4 min read

A short tour of Open Geospatial Tools of the scientific Python ecosystem

One of my favorite statistics is that about 80% of data in the world, contain some element that is spatial. For instance, take the list of gas stations in a city or restaurants in a city, revenue from medical industry, there is always some element of this data that can be categorized as being spatial - be it locations, routes the products take, cost variations in gas prices etc.

  • 2 min read

Imagery in ArcGIS ecosystem

ArcGIS apps give you access to work imagery data from a variety of file formats. The goal is to unify the differences in image characteristics (spatial, spectral, temportal resolutions), file formats (local - different types of image formats, mosaic and web). However, it is useful to understand the basics. This page does not teach you remote sensing or spatial analysis, it just gives you a roadmap to navigate the software.

  • 5 min read

Building data science projects using Azure-ML stack

This wiki covers the steps involved in building a data science project using Azure Machine Learning Workbench product. This also covers the steps involved in productionizing the model as a web service and accessing it over HTTP using its REST API.

Azure ML consists of two major parts - Azure ML portal - Azure ML Workbench