#Deeplearning is one of the most important skills data scientists can have these days. That said there are numerous other skills that get passed over in the search for good data scientists. I’ll attempt to discuss some of them in this series of tweets. — Rajesh S (rexplorations) (@rexplorations) July 18, 2018 This post stems […]Read more "Achieving Explainability and Simplicity in Data Science Work"
Data scientists come in many shapes and sizes, and constitute a diverse lot of people. More importantly, they can perform diverse functions in organizations and still stand to qualify under the same criteria we use to define data scientists. In this cross-post from a Quora answer, I wish to elucidate on the different kinds of […]Read more "Different Kinds of Data Scientists"
This is a really interesting question for me, because I really enjoy discussing data science and data analysis. Some reasons I love data science: Discovering and uncovering patterns in the data through data visualization Finding and exploring unusual relationships between factors in a system using statistical measures Asking questions about systems in a data context […]Read more "Why Do I Love Data Science?"
Although the data science and big data buzzwords have been bandied about for years now, and although artificial intelligence has been talked about for decades, the two fields are irrevocably inter-related and interdependent. For one thing, the wide interest in data science started just as we were beginning to leverage distribute data storage and computation […]Read more "The Expert System Anachronism in the Data Science and AI Divergence"
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"
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”"
The insights we get from data depend on the quality of the data itself, and as the saying goes, “Garbage In, Garbage Out”. The volumes of data don’t matter as much as the quality of the data itself. Data quality and data quality assurance are therefore of growing importance in today’s Big Data arena. With the […]Read more "Quality and the Data Lifecycle"