YUWEI, XIA

Denver, CO, relocatable in US

720-***-**** adjdm4@r.postjobfree.com

EDUCATION

University of Colorado Denver Denver, CO

Master of Science in Business Analytics Jan 2019 – May 2020 Relevant Coursework: Statistics, Predictive Analysis, Text Data Analytics, Data Visualization, Network modeling Master of Science in Information Systems, Emphasis in Business Intelligence Aug 2017 – Dec 2018 Relevant Coursework: Business Intelligence System and Analytics, Data Warehouse, IT Project Management Northeast Forestry University Harbin, China

Bachelor of Science in Information Systems Sep 2013 – Jun 2017 Relevant Coursework: Management Information Systems, Data Structure, Java Programming TECHNICAL SKILLS

Analytics Tools: SQL, SAS, Python, R, Tableau, Power BI, Excel Statistical Modeling: Clustering Analysis, Predictive Modeling, Forecasting, Data Mining EXPERIENCE

University of Colorado Denver Denver, CO

Graduate Assistant, ECON 4030/5030 Data Analysis with SAS Sep 2019 – May 2020

• Responsible for grading 9 lab assignments for a class with two sections of 50 students. RELEVANT PROJECTS

Suicide Rates Visualization Report Tableau, R

• Cleaned data, analyzed, and conducted exploratory analysis among 12 variables.

• Visualized data and findings using Tableau to create an intuitive presentation. o Insights: The suicide rate in Russia is relatively highest among these countries within statistical data overview 1985 to 2016; The more work and life pressure, the easier it is to commit suicide; The number of suicides in the 35-54 age range is the highest. Healthcare Readmission and Quality Model SQL, Excel, R

• Collaborated with three group members to collect a large and real-world database of information on around 3,000 hospitals in the United States including statistics about their readmission rates for various procedures through the CMS website.

• Analyzed the factors that contribute to excessive readmissions will allow hospitals to reduce errors and increase insurance reimbursements and will improve quality of care for patients.

• Built two models to predict excessive readmission rates: one to predict heart failure readmission rates and one to predict pneumonia readmissions by using a best subsets approach.

• Heart failure model with 68% of the variation in readmission rates, and pneumonia model with 73% of the variation in readmission rates.

o Insights: National ratings are not actually a good indicator of patient outcomes; Higher ratings are associated with worse readmission rates.

Machine Learning in Conversion Rate Python

• Cleaned data and conducted descriptive statistics.

• Implemented Random Forest to construct prediction model and validated by OOB.

• Achieved pretty good prediction accuracy of 97% for the Random Forest, which beat all the other models, including Logistic Regression, RuleFit.

ADDITIONAL INFORMATION

Certification

• SAS Certified Base Programmer for SAS 9

• SAS Certified Professional: Advanced Programming Using SAS 9.4

• Tableau Desktop Specialist

Contact this candidate