Author: Data Science Central

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. I’ve been reading the interesting and soul-searching (from a data scientist perspective) book from Cathy O’Neil titled “Weapons of Math Destruction”, or WMD as used in the book.  The book provides several real-world examples of how Big Data and Data Science – when not properly structured – can lead to ethically-wrong unintended consequences.  Chapter 3 “Arms Race: Going to College” describes how the college ranking system developed by “US News & World Report” in 1983 has created its own self-fulfilling, mis-aligned ecosystem. Because of the influence the “US News ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: AI. Mobile applications based on machine learning are reshaping and affecting many aspects of our lives. Implementing machine learning on mobile devices faces various challenges, including computational power, energy, latency, low memory, and privacy risks. In this article, we investigate the current state of implementing machine learning for mobile applications, providing an overview of five architectures commonly used for this purpose and the ways in which they address the given challenges. We also discuss their pros and cons, providing recommendations for each architecture. Additionally, we review recent studies, popular toolkits, cloud ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Sharmistha Chatterjee. Introduction and Motivation It is becoming increasingly common for organizations to collect very large amounts of data over time, and to need to detect unusual or anomalous time series. For example, Yahoo has banks of mail servers that are monitored over time. Many measurements on servers/IoT device performances are collected every hour for each of thousands of servers in order to identify servers/devices that are behaving unusually. Python library tsfeature helps to compute a vector of features on each time series, measuring different characteristic-features of the series. The features may include lag correlation, the strength ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Aileen Scott. Data scientist! The most awaited job for candidates who’ve got the affinity for statistics and mathematics. With data growing at an exponential rate, data science now becomes an appealing career path. Data science is super technical, interesting, and tough to get into. However, making an entrance as a data scientist may not be as challenging as you think. First, know that data is being generated in the blink of an eye. To be precise, according to the “2021 Robert Half Technology Salary Guide” data scientist touts to be one ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Let’s start by clarifying the difference between RPA (Robotic Process Automation) and IA (Intelligent Automation).  Then we’ll show why AI/ML inside Intelligent Automation is the secret sauce that really makes this work. As we’ve tracked the adoption of AI/ML across industries the application that always seems to lead is RPA (Robotic Process A..., sometimes also referred to as IA (Intelligent Automation).  The problem with giving credit for AI/ML adoption to RPA is that RPA historically has meant robotic process simplification.  That is, processes were indeed automated but it ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Some industries are a clear slam-dunk for AI/ML applications and some less so.  The legal, regulatory, and compliance businesses (law firms, internal legal departments, and the contract review and regulatory compliance departments of heavily regulated industries) fall in this last category.  This is a review of seven companies found by TopBots to be successful; pointing to opportunities others can follow. Remember just a few years ago when we were looking forward to now or a little beyond and imagining what applications AI/ML would have in different industries.  Some ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kurt Cagle. From the Editor's Desk One of the biggest impacts the pandemic has had is the complete turmoil that it has left of the educational system in most countries. Extended lockdowns and the need for social distancing have played havoc with the traditional mode of gathering students together into classrooms led by teachers working from blackboards. What has replaced it has been virtual classrooms - teachers reaching out to students at their homes, instructional materials morphed into web portals, interactive demos, zoom meetings, and tracking of progress through cloud-based data ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Kurt Cagle. Over the years, I've had people ask me how a taxonomy differs from an ontology. The answer (or at least a reasonably simple answer) is that "a taxonomy is a tree shaped ontology". It is worth digging  bit deeper to understand what that means, however: Way back in the early 18th century, a Swedish biologist by the name of Carl Linnaeus began a fairly ambitious project. He wanted to build a way of indexing animals and plants by their phenotypes - the ways that they are alike. His original ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Henrik Skogström. As you start incorporating machine learning models into your end-user applications, the question comes up: “When is the model good enough to deploy?”There simply is no single right answer. There is no clear-cut measure of when a machine learning model is ready to be put into production, but there are a set of thought experiments that you should go through for each new model. Identify the Goal of the Machine Learning Model When you are trying to decide if a machine learning model is ready for deployment, it is ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Lokesh. If are you an established DevOps engineer, or do you plan to enter DevOps?. Forrester, the leading consulting firm, labeled 2018 as 'the year of enterprise DevOps' and predicts DevOps being adopted by 50 percent of companies all over the world. How much time is required to learn DevOps? DevOps learning calls for constant commitment, desire and enthusiasm. The DevOps training will take between one week and several months. Learning is an ongoing cycle and you grow daily, but addressing this question is very arbitrary as to how long does it take someone to get on ... Read More