HONG HUANG

adbrs2@r.postjobfree.com 918-***-**** ***** E 44th St, Tulsa, OK

SUMMARY

Seeking to use my data analytic, mathematical methods and computer programming skills in entry-level opportunity as an business/data analyst. Experienced two years of business analytic program with distinguished performance at University of Tulsa. Earned degrees in both Business and Mathematics. EDUCATION

The University of Tulsa, Tulsa, OK

Master of Science in Business Analytic Dec 2019

Bachelor of Science in Mathematics (Minor: Computer Science) May 2018 RELEVANT EXPERIENCE

University of Tulsa Student Academic Support,Tulsa, OK, Tutor May 2015-Dec 2019

Provided additional support for students in Partial Differential Equation,Statistics, Discrete Mathematics, Linear Algebra Matrix Theory, and business courses

Developed study plans for students and kept track of students’ grade Chengda Material and Decoration Engineering CO., LTD,Shanghai, China, Intern Jun - Aug 2017

Coordinated parties to collect data and invoice by Excel

Prepared and created target market reports for sales team

Data visualization on client’s data by Power BI and Tableau AAON INC,Tulsa, OK, Supply Chain management Intern May 2019 –July 2019

Optimized the 3-year inventory data to utilize cost efficiency for supply chain team

Forecasted the amount of order quantity in next three years with @Risk

Analyzed the inventory levels to determine the reorder levels

Developed two inventory models using Ss trigger and Fixed Order interval model SELECTED PROJECTS

Wine Quality Analysis (SAS EM,R)

Created correlogram and scatter plots to understand data

Rescaled the “Alcohol” and “Density” to avoid collinearity

Used Filter node to eliminate the missing value and outliers for data cleaning

Model selection such as “Forward regression” “Stepwise regression” and “Pruned decision tree”

Prioritized the factors in the model and make recommendations based on model Predictive analytic Modelling for Restaurant with Regression (SAS)

Executed among 60 restaurant into data preparation by checking “normality” and

“auto-correlation”

Selected the most important variables by “Lasso” and “Elastic net” methods to run the model

Compared the model with logistic regression and discriminant analysis

Chose the best model based on accuracy (93.5%) which is logistic regression

Applied the best model to test data and produced the final prediction QUALIFICATIONS PROFILE

Skilled coding with Python, R, SAS, data mining, machine learning, big data, SQL and MySQL

Advanced knowledge in business analysis, Excel, Tableau and Power BI

Experienced in multiple mathematical methods and modeling techniques

Versed in operation management and supply chain management

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