This rubric covers the use of statistical tools working on large datasets to create models and derive inferences, as well as coverage of the field in its entirety. This differs from machine learning primarily in that the latter focuses on functional gradient analysis or neural networks (kernels) to derive models.
The number of devices connected through the Internet of Things (IoT) is increasing rapidly. Statista estimates that there will be about 50 million IoT-connected devices… Read More »Data Science and Cloud: A Perfect Match for your Data Analytics needs
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 blog… Read More »The Bayesian vs frequentist approaches (Part 3) parametric vs non-parametric models
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… Read More »Why I think the Potential Outcomes Theory is woefully incomplete without Pearl’s enhancements to it
We exist in a world where data is everywhere, not just data but an augmented data. Digging through this data is challenging but enforceable with… Read More »Why Augmented Data Analytics is the Future of Business Intelligence?
Source: here There are numerous examples in machine learning, statistics, mathematics and deep learning, requiring an algorithm to solve some complicated equations: for instance, maximum… Read More »An Easy Way to Solve Complex Optimization Problems in Machine Learning
Perhaps most organizations already know what to do with the data gathered. The data is used to make better decisions at work, right? But do… Read More »Top 6 Regression Techniques a Data Science Specialist Needs to Know
Big data is becoming a much-talked-about phenomenon across different fields, from marketing to IT. There’s an abundance of information on mining data resources, but the… Read More »9 Tips to Effectively Manage and Analyze Big Data in eLearning
This blog is the second part in a series. The first part is The Bayesian vs frequentist approaches: implications for machine le… In part one,… Read More »The Bayesian vs frequentist approaches: implications for machine learning – Part two
When you launch an analytics solution within your enterprise, you are probably concerned about getting your business users to adopt that solution. If you can’t… Read More »Give Your Business Users Simple Augmented Analytics
Whether your business is a small, single location brick and mortar enterprise or a large, multi-facility organization that spans the global market, you need access… Read More »Plug n' Play Predictive Analysis for Business User Data Prototyping
