How Healthcare can Benefit from IoT?
The past year has presented the medical industry with new challenges that humanity has hardly faced before. The situation in the world has accelerated the rapid pace of s...
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Pros and Cons of using Python for Web Development
Python is the world’s third most popular programming language in February 2021, according to TIOBE’s index. It grew over 1.52% since January. Its massive demand in th...
Maximizing Data Use and Reducing Pitfalls: Putting the Right Data Preparation Process in Place for AI
Welcome to the first installment in our series on ModelOps. This blog focuses on the importance of Data Gathering and Preparation in support of your AI projects and avoid...
Feature Engineering At a glance
Data Science Lifecycle revolves around using various analytical methods to produce insights and applying Machine Learning techniques to do the predictions from the colle...
What is DataOps?
DataOps is the use of agile development practices to create, deliver, and optimize data products, quickly and cost-effectively. DataOps is practiced by modern data teams,...
Unlocking e-commerce growth for CPG with data and analytics
CPG opportunities in the new normal The COVID-19 pandemic has compelled businesses to shift to virtual marketplaces and CPG has been no different. Consumers are increasin...
Unleashing the Business Value of Technology Part 1: Framing the Challenge
My recent journeys have brought me before both technology vendors and technology customers, and interestingly enough, both are asking the same question: “How do we unle...
Six use cases in Blockchain Analysis
Blockchain analysis is the process of inspecting, identifying, grouping, modeling, and visually representing the data contained in the blockchain. As we know, blockchain ...
The Bayesian vs frequentist approaches (Part 3) parametric vs non-parametric models
This post continues our discussion on the Bayesian vs the frequentist approaches. Here, we consider implications for parametric and non-parametric models In the previous ...
Why I think the Potential Outcomes Theory is woefully incomplete without Pearl’s enhancements to it
Judea Pearl (left) and Donald Rubin (right) taken in 2014. Full disclosure: I am a big fan of Judea Pearl and his contributions to Bayesian Networks (bnets) and Causal ...