Data Analyst and Military Veteran with a diverse experience required for meticulous data-driven functions and data analysis. 8 + years of proven experience in the United States Marines and Private Sector. Accomplished measurable results while leading a team of 30 in a dynamic and fast-paced environment. Adept at evaluating credit data, preparing and analyzing financial reports and preparing loan agreements. Demonstrated ability to streamline business and operational analysis that drive growth and increase efficiency and bottom-line profit. Possesses a comprehensive background in data research, customer service, sales, operational analysis, financial analysis, and logistical analysis. Career supported by Google Data Analytics Certification, and a Bachelor of Science in Mathematics with a minor in Actuarial Science.
In this Professional Certificate learners developed and honed hands-on skills in Data Science and Machine Learning. Learners started with an orientation of Data Science and its Methodology, became familiar and used a variety of data science tools, learned Python and SQL, performed Data Visualization and Analysis, and created Machine Learning models. In the process they completed several labs and assignments on the cloud including a Capstone Project at the end to apply and demonstrate their knowledge and skills.
Successfully completed the Google Data Analytics Professional Certificate, developed by Google, that includes hands-on, practice-based assessments that are designed to prepare for introductory-level roles in Data Analytics. Learned competent skills in tools and platforms including spreadsheets, SQL, Tableau, and R. Able to prepare, process, analyze, and share data for thoughtful action.
USING ECONOMETRICS MODELS
ELECTRIC PRICE FORCASTING FOR THE ELECTRIC RELIABILITY COUNCIL OF TEXAS
Proposed a new price forecasting method based on SARIMA-GARCH models with the Skew-Normal Distribution as electricity spot prices exhibit large deviations. The model is constructed to simulate and compose the estimated components of the time series model to predict the future electricity price for the Electric Reliability Council of Texas. Finally, the obtained results from the proposed model are compared with the Normal Distribution Assumption. The result from the comparisons exhibits that the proposed method is far more accurate than the other forecast method.
Basic and Applied Research Journal, May 2020
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