Ever wonder what will happen when exabyte data stores are the norm, and even the parallelism of Hadoop can no longer provide the necessary processing power to address the data deluge? Quantum computing may hold the answer.
Bits are bits. Whether you are searching for whales in audio clips or trying to predict hospitalization rates based on insurance claims, the process is the same: clean the data, generate features, build a model, and iterate.
In this edition of insideBIGDATA’s Data Science 101 series, I’m going to offer up a short instructional video describing the use of the popular unsupervised learning algorithm, k-means clustering.
“Data Analytics Handbook” is a new resource meant to inform young professionals about the field of data science. Written by a group of students at UC Berkeley: Brian Liou, Tristan Tao, and Elizabeth Lin. Edition One of the book includes in-depth interviews with Data Scientists & Data Analysts.
An integral tool found in data science is Time Series Forecasting. Here is a useful instructional video on the subject from one of the authors of a free eBook available on OTexts – “Forecasting: Principles and Practice.” The presentation “Forecasting Time Series Using R” is made by Professor of Statistics Rob J Hyndman.
Richard Feynman, winner of the 1965 Nobel Prize in Physics and world renown “curious character,” gives us an insightful lecture about computer heuristics: how computers work, how they file information, how they handle data, how they use their information in allocated processing in a finite amount of time to solve problems and how they actually compute values of interest to human beings.
Stephen Wolfram, founder of Wolfram Research and creator of Mathematica, just announced the new Wolfram Programming Language. This new knowledge-based language could be a game changer in data science.