Author: Data Science Central

Feed: Featured Blog Posts - Data Science Central. Author: William Vorhies. Summary:  Artificial General Intelligence (AGI) is still a ways off in the future but surprisingly there’s been very little conversation about how to measure if we’re getting close.  This article reviews a proposal to benchmark existing AIs against animal capabilities in an Animal-AI Olympics.  It’s a real thing and just now accepting entrants.   Artificial General Intelligence (AGI) is something that many AI researchers have an opinion about but with surprisingly little consistency.  We believe broadly that achieving human-level AGI requires a system that has all of the following: ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by Louis Tiao.   In this series of notebooks, we demonstrate some useful patterns and recipes for visualizing animating optimization algorithms using Matplotlib.     We shall restrict our attention to 3-dimensional problems for right now (i.e. optimizing over only 2 parameters), though what follows can be extended to higher dimensions by plotting all pairs of parameters against each other, effectively projecting the problem to 3-dimensions. The Wikipedia article on Test functions for optimization has a few functions that are useful for evaluating optimization algorithms. In particular, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Richard Charles, PhD. One of my most memorable experiences during my college years was having an English professor who spoke entirely in complete sentences.There were no spoken sentence fragments, dangling thoughts or misuse of adjectives, verbs or nouns. When I first met him, I was stunned at his level of commitment to this principle. I often wondered how challenging this must be for him and whether or not the journey had instances where a thought started and had to be gracefully abandoned into a mundane compromise of a sentence. It was ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo.  Before I kick off this new blog, I’m happy to announce the release of my 3rd book” "The Art of Thinking Like A Data Scientist”. This book is designed to be a workbook – a pragmatic tool that you can use to help your organization leverage data and analytics to power your business and operational models. The book is jammed with templates, worksheets, examples and hands-on exercises, all composed to help reinforce and deploy the fundamental concepts of Thinking Like A Data Scientist.  I hope you enjoy it! The ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Ayumi Owada. Ecommerce sites generate tons of web server log data which can provide valuable insights through analysis. For example, if we know which users are more likely to buy a product, we can perform targeted marketing, improve relevant product placement on our site and lift conversion rates. However, raw web logs are often enormous and messy so preparing the data to train a predictive model is time consuming for data scientists.According to a recent Forbes article, “Data preparation accounts for about 80% of the work of data scientists”, but “76% ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Andrea Manero-Bastin. This article was written by ODSC - Open Data Science in Predict.   Machine learning is one of the most exciting new features used in technology today. However, it is undoubtedly controversial too. At the moment, this controversy does not really come from the prospect of ultra-intelligent robots dramatically taking over the human race; instead, it’s because powerful technology like this can be used just as negatively by criminals, as it can be used positively by those with good intention. In this piece, I would like to explore the ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Stephanie Glen. In part 1, I compared a few model evaluation techniques that fall under the umbrella of 'general statistical tools and tests'. Here in Part 2 I compare three of the more popular model evaluation techniques for classification and clustering: confusion matrix, gain and lift chart, and ROC curve. The main difference between the three techniques is that each focuses on a different type of result: Confusion matrix: false positives, false negatives, true positives and true negatives. Gain and lift: focus is on true positives. ROC curve: focus on true positives vs ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.   Featured Resources and Technical Contributions  Comparing Model Evaluation Techniques - Part 2: Classification and Clustering Data Science using your high school maths knowledge - Gradient Descent Linear and logistic regression in Excel and R - with RegressIt Free book: The Data Engineering cookbook by Andreas Kretz  Try our New Random Number Generator  Visualizing and Animating Optimization Algorithms with Matplotlib  Streamlining Predictive ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Robert Nau. Excel is often poorly regarded as a platform for regression analysis. The regression add-in in its Analysis Toolpak has not changed since it was introduced in 1995, and it was a flawed design even back then.  (See this link for a discussion.)    That’s unfortunate, because an Excel file can be a very good place in which to build regression models, compare and refine them, create high-quality editable tables and charts, share and present the results, and teach regression to those constituencies of students and practitioners for whom Excel is the ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Mkhuseli Mthukwane. Those of us who were data science practitioners before the ubiquity of buzz words like “Fourth Industrial Revolution”, remember a time when data science was being described as “utter hubris” by some of the worlds leading quants. All these buzz words, coupled with misplaced applications of data science have turned it into a fairly nebulous concept. Which is a problem. In facilitating my data science workshops I've realized there are two main groups of audiences and learners. Those who see it as pure wizardry and those who see it ... Read More