Python, R, SQL,Hadoop

Bernard Fan

*** ***** **** ***** ******* NY-***** Cell: 917-***-**** **************@*****.*** linkedin.com/in/bernard-fan-a040456a

M.S. in Information Systems&Financial analysis with Undergraduate degree in Business Administration. Proficient in statistical, commercial and data analysis; problem solving; and modeling using R, Python, Tableau and mathematics. Technical skills include:

Data Analysis Tools: Python, R, Tableau, Machine Learning, Matlab, SPSS, Excel-Solver Platform, Google Analytics

Business Applications: SAP/ERP Systems(SD, PS, MM), Time Series analysis

Language & Databases: Mysql, Hadoop, hive, pig, Oracle Sql developer, MS Access

Education

Frank G. Zarb School of Business, Hofstra University Hempstead, NY

M.S. Information Systems GPA:3.33 May 2018

South China University of Technology Guangzhou, China

B.S. Business Administration June 2014

Technical Projects

Data Mining based on the Database from ratemyprofessor.com using Mysql, R, Tableau

• Imported Data sets into Mysql Workbench, created different table according to different keys and built connections among tables then generated ERR diagram

• Conducted regression analysis and EFA analysis, PCA analysis using R to find potential among parameters which constitute the score model for professors

• Connected Mysql, R with Tableau for data visualization

Warriors vs Cavaliers in NBA games using R

• Using Density based Spatial Clustering algorithm(DBSCAN) on R to do clustering on 30 teams according to top 5 indicators PTS, Three PP, REB, AST, TOV to reduce research targets

• Using Principal Components Analysis(PCA) to find how much variance is explained and how does every team perform on every principal components

• Built logistic regression model to simulate the parameter "WL" (win&lose) by TWOPM, ThreePM, FTM, REB, AST, TOV, STL, and BLK

• The result showed that TWOPM is the key parameter for success, ThreePM is the second one.

• For those teams who conducted assistances more than 30 times in each match averagely, they would win 49 games, reversely, only 14.

• As for rebound, for those teams got rebound more than 44 averagely, they would win 33 games, reversely, only 20.

“Obamacare vs Trumpcare” Data visualization and analysis using tableau and python

• Collected, sorted, cleaned and extracted healthcare relevant data from Labor Department, Counties' Government Reports and Financial Reports of leading health insurance companies, then imported into python and tableau

• learned and made kinds of diagrams to visualize the significant differences between two kinds of healthcare systems on expenditure, incidence of poverty, cost percentage, tendency analysis and turnout in election using pandas, numpy and pillow

• Explored the interpretation - maybe it is the reason or part of at least the swing states voted for Trump eventually

Multivariate Analysis of Gun Crime in US by using R

• Using principal component analysis (PCA) to reduce data dimensions of 48 states, created biplot and corresponding rankings according to the severity of gun crime

• Performed Exploratory Factor Analysis (EFA) on data to identify factors and classify states

• Created Dendograms and cluster plots to group states by doing cluster analysis

SAP Global Bike Inc. project management module using WBS

• Created and changed objects showing data and data targets from global bike Inc.

• Created and managed order within SAP, reported with element report, analyzed project structure

• Post activity confirmation, created single confirmation, displayed actual cost reporting and creditor invoice

Professional Experience

Hofstra University Resident Safety Hempstead

Resident Safety Shift Coordinator Aug 2017-May 2018

Provide supervision of Representatives of Resident Safety, report and assist manager with supervision, assignment, instruction, training and assessment

Assisted Operations Manager to perform departmental assignments like recruiting, shifts change and so forth

China Construction Bank China

Account Manager, Sales & Marketing Aug 2015- Jun 2016

Performed collection of feedbacks, ratings, needs from customer, focused on finding and identifying potential

opportunities to improve Banking performance using SPSS

Assisted to evaluate and assessed customers’ preferences on risk in loans review, financial planning and created sales dashboard displaying Key statistical indicators and trending analysis using Pivot table and formula

Australia and New Zealand Banking Group (ANZ) Australia

Sales Intern April 2015-Jun 2015

Handled customer inquiries about deposits, withdrawals, transfers and online banking

Assisted sales analyst review financial contracts and import data into customer information system

UNSW Business School, University of New South Wales Australia

Graduate Student Mar 2015-Aug 2015

Major in Financial Analytics

Contact this candidate