Civil Engineer Data
Resume:
LI, YUEWEI
**** *** **** **** ***.***A, Houston, TX
******.**@*******.***
OBJECTIVE
Civil engineer with 3+ years’ experience of CAD, Geo-technical, surveying and design in Chengdu Surveying Geo-technical Research Institute co., ltd which is a Chinese national company. Seeking a full-time job or internship as Data Scientist, Data Analyst, Statistician, Civil Engineer. SKILLS
Python, Java, R, SAS, SQL, Microsoft Excel, UML, MATLAB, CAD, PHP, MINITAB, MySQL, Weka. WORKING EXPERIENCE
Civil Engineer July 2012-December 2015
Chengdu Surveying Geo-technical Research Institute co., ltd, Chengdu, China
Designed and managed 2 big projects and over 15+ small projects in 3+ years. EDUCATION
Master of Science, Statistics, University of Houston-Clear Lake, GPA:3.6/4.0 Spring 2016-Fall 2018
One year and half of computer science classes and one year and half of statistics classes.
Working as Teaching Assistant in two semesters.
Familiar with data mining, machine learning and data analytical skills. Bachelor of Science, Civil Engineering, Chengdu University of Technology, GPA:3.5/4.0 SELECTED PRESENTATIONS/PROJECTS
Statistical Research:
USA crime analysis(SAS/Python, Java) Spring 2018
Established a goal, collected data, cleaned data, separated different states of crime data samples,educational proportion and personal income by Java to SAS/Python.
Using Neural Networks, MANOVA, Mann-Whitney to test the relationship between each moth by SAS/Python.
Using Multinomial Logistic Regression to find out the relationship among crime, educational proportion and personal income. And Time-Series Analysis, Clustering Analysis to analyse crime data set. Whether the factors that affect housing price and police station affect crime rate in Houston(R, Weka, SQL) Fall 2018
Separated Houston data samples by SQL, then imported into R (GIS)to map Houston crime location, and cut it into 40 times 40 cells.
Imported data of housing price from relational database and police station data set into R and combined crime data by address.
Using Correlation Coefficient, Linear Regression, Backward Stepwise Regression, Factorial Design to find the influencing factors by R.
Using Weka to perform one decision tree and best model to support the result from R. And the other decision tree to figure out which crime type does affect police station distribution.
Using Dendrograms, K-means Clustering, ANOVA, Chi-square test, Wilcoxon Rank Sum Test to show the relationship between clusters which means different house locations and police stations.
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