Today, I began compiling an annotated bibliography for my thesis. So far, I have 18 sources. A majority of my sources deal with math and statistics. The topic with the second most topics is data preparation. In my last blog post, I mentioned that mentor told me that data preparation will take the most time.
After formatting my sources into APA style, I added annotations to a few of them. I originally had more than 18 sources, but some of them did not help me as much as I thought they would. I deleted those ones and narrowed my bibliography down to 18 sources. I emailed my mentor for any recommendations of math, statistics, or anything that could help me with my project.
Disclaimer: The last source of the bibliography below is larger than the others when I previewed it. However, in the edit mode, it looks like the other sources. I do not know why it is doing it, but I am aware of it.
Sources Used for My Project So Far:
Chapman, P., Kerber, R., Clinton, J., Khabaza, T., Reinartz, T., & Wirth, R. (1999). The CRISP-DM Process Model [PDF file]. Discussion Paper. Retrieved from My Mentor.
Teetor, P. (2011). R Cookbook [PDF file]. Sebastopol, California: O'Reilly Media, Inc. Retrieved from My Mentor.
Press, G. (2016). Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says. Retrieved from https://www.forbes.com/sites/gilpress/2016/03/23/data-preparation-most-time-consuming-least-enjoyable-data-science-task-survey-says/#697d2c756f63
Stella, C. (2016). Data Preparation for Data Science [Powerpoint Slides]. Retreived from https://www.slideshare.net/HadoopSummit/data-preparation-of-data-science
Dunn, C., Grabski, S. (2001). Syntactic and Semantic Understanding of Conceptual Data Models [Abstract]. Association for Information Systems. Retrieved from http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1092&context=icis2001
Lohr, S. (2014). For Big Data Scientists, ‘Janitor Work’ is Key Hurdle to Insights. Retrieved from
https://www.nytimes.com/2014/08/18/technology/for-big-data-scientists-hurdle-to-insights-is-janitor-work.html?_r=0
Upadhyay, R. Master the Art of Data Preparation for Data Science. Retrieved from http://ucanalytics.com/blogs/master-art-data-preparation-data-science/
Combinatorial Identity. Retrieved from https://artofproblemsolving.com/wiki/index.php?title=Combinatorial_identity
Quaintance, J. (2010). Combinatorial Identities: Table 1: Intermediate Techniques for Summing Finite Serie [PDF file]. Retrieved from http://www.math.wvu.edu/~gould/Vol.4.PDF
Benjamin, A., Quinn, J. (2003). Proofs that Really Count [PDF file]. Retrieved from
http://people.qc.cuny.edu/faculty/christopher.hanusa/courses/636fa13/Documents/636fa13ch21.pdf
Fox. J. Lecture 1: Introduction [PDF document]. Retrieved from
http://math.mit.edu/~fox/MAT307-lecture01.pdf
Downey, A. (2012). Think Bayes: Bayesian Statistics Made Simple [PDF file]. Needham, Massachusetts: Green Tea Press. Retrieved from http://www.greenteapress.com/thinkbayes/thinkbayes.pdf
Downey, A. (2014). Think Stats: Exploratory Data Analysis in Python [PDF file]. Needham, Massachusetts: Green Tea Press. Retrieved from http://greenteapress.com/thinkstats2/thinkstats2.pdf
P Byrnes (2015, May 10). Multivariable Calculus Final Exam Review [Video file]. Retrieved from https://www.youtube.com/watch?v=l50iaaVDzLQ
Petersen, K., Pedersen, M. (2012). The Matrix Cookbook [PDF file]. Retrieved from http://www.math.uwaterloo.ca/~hwolkowi/matrixcookbook.pdf
Linear Algebra Review [Video file]. Retrieved from https://www.youtube.com/watch?v=6AP4IvfKmwg&list=PLnnr1O8OWc6boN4WHeuisJWmeQHH9D_Vg
Singh, R., Jaakkola, T., Mohammed, A. (2006). Machine Learning. Retrieved from https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/index.htm
Grimson, E., Guttag, J., Bell, A. (2016). Introduction to Computational Thinking and Data Science. Retrieved from https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/index.htm
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