Data scientists are new age explorers. Their field of exploration is rife with data from various sources. Their methods are mathematics, linear algebra, computational sciences, statistics and data visualisation. Their tools are programming languages, frameworks, libraries and statistical analysis tools. And their rewards are stepping stones, better understanding and insights. The data science process for […]Read more "Hypothesis Generation: A Key Data Science Challenge"
A decade ago, Microsoft looked very different from the Microsoft we see today – it has been a remarkable transformation. One of the areas where MS have made a big push is machine learning and data analytics. Although the CRAN repository is going strong with >10,000 packages as of today, the MRAN repository (Microsoft’s Managed […]Read more "Azure ML Studio and R"
I’m given to spurts of activity on Quora. Over the past year, I’ve had the opportunity to answer several questions there on the topics of data science, big data and data engineering. Some answers here are career-specific, while others are of a technical nature. Then there are interesting and nuanced questions that are always a […]Read more "Quora Data Science Answers Roundup"
As a data science consultant that routinely deals with large companies and their data analysis, data science and machine learning challenges, I have come to understand one key element of the data scientist’s skill set that isn’t oft-discussed in data science circles online. In this post I hope to elucidate on the importance of domain […]Read more "Domain: The Missing Element in Data Science"
Data products are one inevitable result and culmination of the information age. With enough information to process, and with enough data to build massively validated mathematical models like never before, the natural urge is to take a shot at solving some of the world’s problems that depend on data. Data Product Maturity There are some […]Read more "Insights about Data Products"
This may sound weird, but one sure way to not have perspective about the business in an innovative and constantly changing industry is to bury yourself within regular work. This is the meaning of the title – which comes from a book of the same name. By regular work, I mean work in which you […]Read more "Data Perspectives: “Orbiting The Giant Hairball”"
Being data-driven in organizations is a bigger challenge than it is made out to be. For managers to suspend judgement and make decisions that are informed by facts and data is hard, even in this age of Big Data. I was spurred by a set of tweets I posted, to think through this subject. Decision […]Read more "“Small Data”and Being Data-Driven"