This year, 2017, has been quite a busy year for artificial intelligence and data science professionals. In some ways, this is the year when AI truly began to be debated and discussed, from frameworks and technologies to ethics and morality. This is the year when opportunities for AI-driven improvement in businesses began to be examined […]Read more "Key Data and AI trends in 2017"
Recently, I had the opportunity to finish Stanford SCPD’s XINE 217 “Empathize and Prototype” course, as part of the Stanford Innovation and Entrepreneurship Certificate, which emphasizes the use of design thinking ideas to develop product and solution ideas. It is during this course, that I wrote down a few ideas around the use of data […]Read more "Some Ideas on Combining Design Thinking and Data Science"
As of mid-2017, I’ve spent almost two years in the big data analytics and data science world, coming from 13 years of diverse work experience in engineering and management prior. Starting from a professional curiosity, it has taken me a while to develop some data science and engineering skills and hone key skills among these […]Read more "Pervasive Trends in Big Data and Data Science"
Over the past year and a few months, I’ve had a chance to lead a few different data science teams working on different kinds of hypotheses. The engineering process view that the so-called agile methodologies bring to data science teams is something that has been written about. However, one’s own experiences tend to be different, […]Read more "Lessons from Agile in 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"
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"