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Articles 31 - 34 of 34
Full-Text Articles in Other Computer Sciences
Data Science In Statistics Curricula: Preparing Students To “Think With Data”, J. Hardin, R. Hoerl, Nicholas J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, M. D. Ward
Data Science In Statistics Curricula: Preparing Students To “Think With Data”, J. Hardin, R. Hoerl, Nicholas J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, M. D. Ward
Statistical and Data Sciences: Faculty Publications
A growing number of students are completing undergraduate degrees in statistics and entering the workforce as data analysts. In these positions, they are expected to understand how to use databases and other data warehouses, scrape data from Internet sources, program solutions to complex problems in multiple languages, and think algorithmically as well as statistically. These data science topics have not traditionally been a major component of undergraduate programs in statistics. Consequently, a curricular shift is needed to address additional learning outcomes. The goal of this article is to motivate the importance of data science proficiency and to provide examples and …
As Strong As The Weakest Link: Mining Diverse Cliques In Weighted Graphs, Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, Ambuj K. Singh
As Strong As The Weakest Link: Mining Diverse Cliques In Weighted Graphs, Petko Bogdanov, Ben Baumer, Prithwish Basu, Amotz Bar-Noy, Ambuj K. Singh
Statistical and Data Sciences: Faculty Publications
Mining for cliques in networks provides an essential tool for the discovery of strong associations among entities. Applications vary, from extracting core subgroups in team performance data arising in sports, entertainment, research and business; to the discovery of functional complexes in high-throughput gene interaction data. A challenge in all of these scenarios is the large size of real-world networks and the computational complexity associated with clique enumeration. Furthermore, when mining for multiple cliques within the same network, the results need to be diversified in order to extract meaningful information that is both comprehensive and representative of the whole dataset. We …
Maximizing Network Lifetime On The Line With Adjustable Sensing Ranges, Amotz Bar-Noy, Ben Baumer
Maximizing Network Lifetime On The Line With Adjustable Sensing Ranges, Amotz Bar-Noy, Ben Baumer
Statistical and Data Sciences: Faculty Publications
Given n sensors on a line, each of which is equipped with a unit battery charge and an adjustable sensing radius, what schedule will maximize the lifetime of a network that covers the entire line? Trivially, any reasonable algorithm is at least a 1/2-approximation, but we prove tighter bounds for several natural algorithms. We focus on developing a linear time algorithm that maximizes the expected lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an average-case approximation ratio of almost 0.9. Most of the algorithms that we consider come from a family based …
Parsing The Relationship Between Baserunning And Batting Abilities Within Lineups, Ben S. Baumer, James Piette, Brad Null
Parsing The Relationship Between Baserunning And Batting Abilities Within Lineups, Ben S. Baumer, James Piette, Brad Null
Statistical and Data Sciences: Faculty Publications
A baseball team's offensive prowess is a function of two types of abilities: batting and baserunning. While each has been studied extensively in isolation, the effects of their interaction is not well understood. We model offensive output as a scalar function f of an individual player's batting and baserunning profile z. Each of these profiles is in turn estimated from Retrosheet data using heirarchical Bayesian models. We then use the SimulOutCome simulation engine as a method to generate values of f(z) over a fine grid of points. Finally, for each of several methods of taking the extra base, we graphically …