I am a data scientist. Data science is an emerging field commonly described as “the practice of deriving valuable insights from data,” and this thread runs through all of my work. Subfields of interest to me include network science, applied statistics, sabermetrics, sports analytics, statistical modeling, analysis of algorithms, combinatorial optimization, data visualization, graph theory, and combinatorics. My Erdös number is 3, as I have co-authored a paper with Amotz Bar-Noy, who has co-authored a paper with Noga Alon, who has co-authored a paper with Paul Erdös.

My background is unusually diverse, in that my undergraduate degree is in economics, my doctorate is in mathematics, my thesis advisor is in computer science, and my professional experience is in statistics. As such, my research tends to be interdisciplinary, with an emphasis on applying available techniques from any discpline to address the question of interest.

In 2012, I completed my Ph.D. in Mathematics at the Graduate Center of the City University of New York, where my advisor was Amotz Bar-Noy, also of Brooklyn College. Previously, I earned an M.A. in Applied Mathematics from the University of California, San Diego, and a B.A. in Economics from Wesleyan University.

Please see my C.V. for complete details on my work.

Contemporary data science uses both statistical modeling and computer programming to extract meaning from data. It requires a tight integration of knowledge from statistics, computer science, mathematics, and a domain of application. This book, which is intended for readers with some background in statistics and modest prior experience with coding, helps them develop and practice the appropriate skills to tackle complex data science projects. Most of the examples are done in R, but SQL, Python, and other cutting-edge tools are discussed as well.

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Since leaving the Mets, I’ve written a book, entitled *The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball*, with leading sports economist Andrew Zimbalist. We examine the evolution of sabermetrics in baseball and other sports since the publication of *Moneyball*, summarize the current state of sabermetric thinking, and address the question of whether there is any evidence that sabermetrics has actually worked. The book will be published by the University of Pennsylvania Press and is scheduled for a December 2013 release.

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I am currently collaborating on a introductory Statistics with R sequence of courses for DataCamp, an interactive platform to learn R and data science. Mine Cetinkaya-Rundel (Duke), Andrew Bray (Reed), and Jo Hardin (Pomona) are working with me on these courses.

`etl`

is an R package to facilitate Extract - Transform - Load (ETL) operations for **medium data**. The end result is generally a populated SQL database, but the user interaction takes place solely within R.

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[1] B. S. Baumer, “Lessons from between the white lines for isolated data scientists,” *PeerJ Preprints*, vol. 5, p. e3160v1, Aug. 2017 [Online]. Available: https://doi.org/10.7287/peerj.preprints.3160v1

[2] R. D. De Veaux, M. Agarwal, M. Averett, B. S. Baumer, A. Bray, T. C. Bressoud, L. Bryant, L. Z. Cheng, A. Francis, R. Gould, A. Y. Kim, M. Kretchmar, Q. Lu, A. Moskol, D. Nolan, R. Pelayo, S. Raleigh, R. J. Sethi, M. Sondjaja, N. Tiruviluamala, P. X. Uhlig, T. M. Washington, C. L. Wesley, D. White, and P. Ye, “Curriculum guidelines for undergraduate programs in data science,” *Annual Review of Statistics and Its Application*, vol. 4, no. 1, pp. 1–16, 2017 [Online]. Available: http://www.annualreviews.org/doi/abs/10.1146/annurev-statistics-060116-053930

[3] B. S. Baumer, D. T. Kaplan, and N. J. Horton, *Modern Data Science with R*. Chapman; Hall/CRC Press: Boca Raton, 2017 [Online]. Available: https://www.crcpress.com/Modern-Data-Science-with-R/Baumer-Kaplan-Horton/9781498724487

[4] B. S. Baumer and P. **Badian-Pessot**, “Evaluation of batters and base runners,” in *Handbook of statistical methods and analyses in sports*, J. Albert, M. E. Glickman, T. B. Swartz, and R. H. Koning, Eds. Chapman; Hall/CRC Press: Boca Raton, FL, 2016, pp. 1–37 [Online]. Available: https://www.crcpress.com/Handbook-of-Statistical-Methods-and-Analyses-in-Sports/Albert-Glickman-Swartz-Koning/p/book/9781498737364

[5] A. Bar-Noy, B. Baumer, and D. Rawitz, “Set it and forget it: Tighter approximation bounds for RoundRobin in a restricted lifetime model,” *Algorithmica*, vol. 76, no. 2, pp. 1–19, Oct. 2016 [Online]. Available: http://link.springer.com/article/10.1007/s00453-016-0198-8

[6] B. S. Baumer, Y. **Wei**, and G. S. Bloom, “The smallest non-autograph,” *Discussiones Mathematicae Graph Theory*, vol. 36, no. 3, pp. 577–602, 2016 [Online]. Available: http://www.discuss.wmie.uz.zgora.pl/gt/index.php?doi=10.7151/dmgt.1881

[7] A. Bar-Noy, B. Baumer, and D. Rawitz, “Changing of the guards: Strip cover with duty cycling,” *Theoretical Computer Science*, vol. 610, pp. 135–148, 2016 [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0304397514006549

[8] B. Baumer, “A data science course for undergraduates: Thinking with data,” *The American Statistician*, vol. 69, no. 4, pp. 334–342, 2015 [Online]. Available: http://amstat.tandfonline.com/doi/abs/10.1080/00031305.2015.1081105

[9] J. Hardin, R. Hoerl, N. J. Horton, D. Nolan, B. Baumer, O. Hall-Holt, P. Murrell, R. Peng, P. Roback, D. Temple Lang, and others, “Data science in statistics curricula: Preparing students to ‘think with data’,” *The American Statistician*, vol. 69, no. 4, pp. 343–353, 2015 [Online]. Available: http://www.tandfonline.com/doi/abs/10.1080/00031305.2015.1077729

[10] B. S. Baumer, S. T. Jensen, and G. J. Matthews, “OpenWAR: An open source system for evaluating overall player performance in Major League Baseball,” *Journal of Quantitative Analysis in Sports*, vol. 11, no. 2, pp. 69–84, 2015 [Online]. Available: http://www.degruyter.com/view/j/jqas.2015.11.issue-2/jqas-2014-0098/jqas-2014-0098.xml

[11] A. Bar-Noy and B. Baumer, “Average case network lifetime on an interval with adjustable sensing ranges,” *Algorithmica*, vol. 72, no. 1, pp. 148–166, 2015 [Online]. Available: http://link.springer.com/article/10.1007/s00453-013-9853-5

[12] B. Baumer, G. Rabanca, A. Bar-Noy, and P. Basu, “Star search: Effective subgroups in collaborative social networks,” in *Proceedings of the 2015 iEEE/ACM international conference on advances in social networks analysis and mining 2015*, 2015, pp. 729–736.

[13] N. Horton, B. S. Baumer, and H. Wickham, “Setting the stage for data science: Integration of data management skills in introductory and second courses in statistics,” *CHANCE*, vol. 28, no. 3, pp. 40–50, 2015 [Online]. Available: http://chance.amstat.org/2015/04/setting-the-stage/

[14] B. Baumer and D. **Udwin**, “R Markdown,” *Wiley Interdisciplinary Reviews: Computational Statistics*, vol. 7, no. 3, pp. 167–177, 2015.

[15] B. Baumer, M. Çetinkaya-Rundel, A. Bray, L. **Loi**, and N. J. Horton, “R Markdown: Integrating a reproducible analysis tool into introductory statistics,” *Technology Innovations in Statistics Education*, vol. 8, no. 1, 2014 [Online]. Available: http://escholarship.org/uc/item/90b2f5xh

[16] B. S. Baumer and G. J. Matthews, “There is no avoiding WAR,” *CHANCE*, vol. 27, no. 3, pp. 41–44, 2014 [Online]. Available: http://chance.amstat.org/2014/09/avoiding-war/

[17] B. S. Baumer, “Analyzing baseball data with R by Max Marchi, Jim Albert,” *International Statistical Review*, vol. 82, no. 2. Wiley Online Library, pp. 313–315, Aug-2014.

[18] B. Baumer and A. Zimbalist, “Quantifying Market Inefficiencies in the Baseball Players’ Market,” *Eastern Economic Journal*, vol. 40, no. 4, pp. 488–498, December 2014 [Online]. Available: http://www.palgrave-journals.com/eej/journal/vaop/ncurrent/full/eej201343a.html

[19] B. Baumer and A. Zimbalist, *The Sabermetric Revolution: Assessing the Growth of Analytics in Baseball*. University of Pennsylvania Press, 2014, p. 240.

[21] P. Bogdanov, B. Baumer, P. Basu, A. Bar-Noy, and A. K. Singh, “As strong as the weakest link: Mining diverse cliques in weighted graphs,” vol. 8188. Springer, pp. 525–540, 2013.

[22] A. Bar-Noy, B. Baumer, and D. Rawitz, “Brief announcement: Set it and forget it - approximating the set once strip cover problem.” ACM, pp. 105–107, 2013.

[23] B. S. Baumer, “Sensor strip cover: Maximizing network lifetime on an interval,” PhD thesis, City University of New York, 2012 [Online]. Available: http://proquest.umi.com/pqdweb?did=2677679131&sid=1&Fmt=2&clientId=29054&RQT=309&VName=PQD

[24] A. Bar-Noy, B. Baumer, and D. Rawitz, “Changing of the guards: Strip cover with duty cycling,” vol. 7355. Springer, pp. 36–47, 2012.

[25] B. Baumer, J. Piette, and B. Null, “Parsing the relationship between baserunning and batting abilities within lineups,” *Journal of Quantitative Analysis in Sports*, vol. 8, no. 2, pp. 1–17, 2012.

[26] A. Bar-Noy and B. Baumer, “Maximizing network lifetime on the line with adjustable sensing ranges,” in *ALGOSENSORS*, 2011, vol. 7111, pp. 28–41.

[27] B. Baumer, P. Basu, and A. Bar-Noy, “Modeling and analysis of composite network embeddings,” in *MSWiM*, 2011, pp. 341–350.

[28] B. Baumer, “Using Simulation to Estimate the Impact of Baserunning Ability in Baseball,” *Journal of Quantitative Analysis in Sports*, vol. 5, no. 2, pp. 1–16, 2009.

[29] B. Baumer, “Why On-Base Percentage is a Better Indicator of Future Performance than Batting Average: An Algebraic Proof,” *Journal of Quantitative Analysis in Sports*, vol. 4, no. 2, pp. 1–11, 2008.

[30] S. **Stoudt**, L. **Santana**, and B. Baumer, “In pursuit of perfection: An ensemble method for predicting march madness match-up probabilities,” in *JSM proceedings*, 2014.

[31] B. Baumer and P. Terlecky, “Improved Estimates for the Impact of Baserunning in Baseball.” American Statistical Association, Alexandria, VA, August-2010.

[32] B. Baumer and D. Draghicescu, “Mapping Batter Ability in Baseball: A Study in Spatial Modeling.” American Statistical Association, Alexandria, VA, August-2010.

[33] B. Baumer, A. **Galdi**, and R. **Sebastian**, “A Survey of Methods for the Statistical Evaluation of Defensive Ability in Major League Baseball.” American Statistical Association, Alexandria, VA, August-2009.

[34] B. Baumer, “In a Moneyball world, a number of teams remain slow to buy into sabermetrics,” in *The great analytics rankings*, R. Webb, Ed. ESPN.com; ESPN.com, 2015 [Online]. Available: http://espn.go.com/espn/feature/story/_/id/12331388/the-great-analytics-rankings#!mlb

[35] B. Baumer, “Applied mathematics at the ballpark: The life of one sabermetrician,” *Math Horizons*, vol. 22, no. 1, pp. 18–20, 2014.

[36] R. Gould, B. Baumer, M. Çetinkaya-Rundel, and A. Bray, “Big data goes to college,” *AMSTAT News*, no. 444, pp. 17–19, 2014.