Statistics Student and Actuarial Candidate (3 Exams) seeking career involving
Actuarial Science, Data Science, Enterprise Risk Management, Underwriting, or Financial Analysis

Purpose Statement

According to the Bureau of Labor Statistics, from 2016 to 2026, employment in all four math occupations is projected to grow 28% (50,400 new jobs) as a whole, which is much faster than the average for all occupations. Statisticians top the list at 34% growth (12,600 new jobs), followed by Mathematicians at 30% growth (900 new jobs), Operations Research Analysts at 27% growth (31,300 new jobs), and Actuaries at 22% growth (5,300 new jobs). Among business and financial occupations, employment of Insurance Underwriters in the direct health and medical insurance carriers industry is projected to grow by 15%, and Financial Analysts by 11% (32,300 new jobs).

According to a report by The Economist Intelligence Unit published in December 2014, the projected demand for actuaries in the U.S. to 2020 will be strongest in the practice area of healthcare, with 27% growth from 2014 to 2020. This goes hand in hand with the aforementioned growth projected within the same industry for the similar occupation of insurance underwriters.

Among education occupations, employment of Postsecondary Mathematical Science Teachers is expected to grow by 9% (5,600 new jobs).

The doors to all seven of those in-demand careers would swing open for me by earning a master’s degree in statistics. On July 1, 2019, I applied to the online program of Texas A&M University to earn a Master of Science (MS) degree in Statistics. On July 12, 2019, I was notified that the admissions committee had recommended that I be admitted to the program, and that I will be formally admitted as soon as all of my transcripts are received.

Additionally, I am currently applying to entry-level employment opportunities involving actuarial science, data science, enterprise risk management, underwriting, or financial analysis, for which I am willing to relocate. Therefore, an online program to earn a master's degree in statistics is ideal because I can fit it around a full-time work schedule and complete the degree regardless of where I reside in the United States.

Key learning objectives that I hope to achieve through the master’s degree program in statistics include the analytical and technical skills for harnessing “big data”:
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  1. Data warehousing - Store large quantities of information through various database technologies
  2. Data mining - Synthesize data from heterogeneous sources by performing queries through various programming languages
  3. Exploratory analysis - Report the retrieved data visually through various applications
  4. Machine learning - Develop predictive models for arriving at decisions, or for developing products, based on intelligence derived from large data sets

Reports

2U, Inc. [cited June 29, 2019], "Careers that Require Analytical Skills," Master's in Data Science [online], Lanham, MD: Author. Available at https://www.mastersindatascience.org/careers/.
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Harris, J., Dunn, J., and Lacson, K. (2018), The Actuarial Talent Challenge: Combating the Mid-Level Gap (white paper), The Jacobson Group. Available at https://jacobsononline.com/uploadfiles/leader390.pdf.

Rieley, M. (2018), "Big Data Adds Up to Opportunities in Math Careers," Beyond the Numbers: Employment & Unemployment [online], 7, 8. Available at https://www.bls.gov/opub/btn/volume-7/big-data-adds-up.htm.

Royster, S. (2013), "Working with Big Data," Occupational Outlook Quarterly [online], Fall 2013. Available at https://www.bls.gov/careeroutlook/2013/fall/art01.pdf.

The Economist Intelligence Unit Limited. (2014), Actuarial Talent: Findings from an EIU Study: Executive Summary [online], London, UK: Author. Available at https://www.soa.org/globalassets/assets/Files/Soa/2015-eiu-actuarial-talent.pdf.​​

​U.S. Bureau of Labor Statistics. (2019), "Fastest Growing Occupations: 20 Occupations with the Highest Percent Change of Employment Between 2016-26," Occupational Outlook Handbook [online], last modified date: Friday, April 12, 2019. Available at https://www.bls.gov/ooh/fastest-growing.htm.

Wladawsky-Berger, I. (2014), "Why Do We Need Data Science When We’ve Had Statistics for Centuries?," The Wall Street Journal [online], May 2, 2014. Available at https://blogs.wsj.com/cio/2014/05/02/why-do-we-need-data-science-when-weve-had-statistics-for-centuries/.

Yerton, S. (2019), "Wanted: People Who Can Make Useful Sense of Vast Amounts of Data," Honolulu Civil Beat [online], May 6, 2019. Available at https://www.civilbeat.org/2019/05/wanted-people-who-can-make-useful-sense-of-vast-amounts-of-data/.

Statistical Software

Data Visualization
Database Management
Programming Languages
  • C++
  • Python
  • R
  • SAS (Statistical Analysis System)
  • SQL (Structured Query Language)
  • VBA (Visual Basic for Applications)

Spreadsheets
Not everything that can be counted counts, and not everything that counts can be counted.
—​William Bruce Cameron, 1963

Resources

Actuarial Science
​Data Science
Enterprise Risk Management
Investment Management
Mathematics
Statistics
Underwriting

Textbooks

Mathematical Statistics: Probability Theory, Statistical Inference, Statistical Methods, Data Analysis

Hogg, R. V., McKean, J. W., and Craig, A. T. (2012), Introduction to Mathematical Statistics (7th ed.), London, UK: Pearson.

Hogg, R. V., Tanis, E., and Zimmerman, D. (2014), Probability and Statistical Inference (9th ed.), London, UK: Pearson.

Mendenhall, W., Beaver, R. J., and Beaver, B. M. (2012), Introduction to Probability and Statistics (14th ed.), Boston, MA: Cengage.

Ott, R. L., and Longnecker, M. T. (2015), An Introduction to Statistical Methods and Data Analysis (7th ed.), Boston, MA: Cengage.

Rice, J.A. (2006), Mathematical Statistics and Data Analysis (3rd ed.), Boston, MA: Cengage.

Tamhan, A. C., and Dunlop, D. D. (1999), Statistics and Data Analysis: From Elementary to Intermediate (1st ed.), London, UK: Pearson.

​Wackerly, D., Mendenhall, W., and Scheaffer, R. L. (2008), Mathematical Statistics with Applications (7th ed.), Boston, MA: Cengage.

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Data Science

Bruce, P., and Bruce, A. (2017), Practical Statistics for Data Scientists: 50 Essential Concepts (1st ed.), Sebastopol, CA: O'Reilly Media.

Hastie, T., Tibshirani, R., and Friedman, J. (2016), The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.), New York, NY: Springer.
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James, G., Witten, D., Hastie, T., and Tibshirani, R. (2013), An Introduction to Statistical Learning: With Applications in R (1st ed.), New York, NY: Springer.

Provost, F., and Fawcett, T. (2013), Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking (1st ed.), Sebastopol, CA: O'Reilly Media.

​Wickham, H., and Grolemund, G. (2016), R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.), Sebastopol, CA: O'Reilly Media.
Reimagining My Formal Education from 7th Grade to the Present