Education

Feiyang

Chen

(***)

***- ****

ac7xuh@r.postjobfree.com

www.linkedin.com/in/chenfeiyang/ Master

of

Science

in

Business

Analytics,

University

of

California,

San

Diego 12/2018

• Program

Focus:

Data

Mining,

Data

Cleansing,

Data

Visualization,

Statistics,

Mathematics,

Economics;

GPA:

3.6 Bachelor

of

Science

in

Accounting,

University

of

Nebraska- Lincoln 08/2017

• Dean’s

List;

University

of

Nebraska- Lincoln

Scholarship;

GPA:

3.5 Experience

Data

Analyst

Summer

Intern,

Ford

Motor

Company 06/2018

-

09/2018

• Acquired

data

sources

from

multiple

teams,

maintained

and

improved

data

infrastructure

and

quality,

transformed variables

and

input

them

into

the

predictive

models.

• Conducted

in- depth

market

research

to

emerging

trends

in

the

PV

(Passenger

Vehicle)

industry,

visualized

the seasonality

and

trends

of

sales

volume

and

identified

growth

opportunities.

• Built

and

modified

the

time

series

model(ARIMA)

to

forecast

PV

Sales

Volume

in

the

next

6

months.

• Provided

on- going

tracking

and

monitoring

of

PV

industry

volume

and

presented

market

strategies

to

the

Analytics Team

Lead.

Graduate

Teaching

Assistant,

University

of

California,

San

Diego 03/2018

-

06/2018

• Provided

assistance

and

resources

on

learning

R

to

students

and

assisted

them

with

the

capstone

project.

• Demonstrated

how

predictive

models

work,

and

illustrated

models

use

with

examples

from

the

textbook.

• Organized

tutoring

environment

and

assisted

students

in

web

scraping,

text

mining,

and

particular

algorithms.

• Guided

class

discussions

and

supervised

laboratory

work

and

maintained

data

entry

of

online

course

information

and collaborated

with

the

professor

in

evaluating

and

grading

examinations,

assignments,

and

papers. Operations

Analyst

(Capstone

project),

Rady

Children’s

Hospital,

San

Diego 03/2018

-

06/2018

• Improved

existing

predictive

model

to

predict

daily

patient

volumes

for

Emergency

Department

and

decreased

volume prediction

deviation

by

68%.

• Identified

bottleneck

to

improve

operational

efficiency

and

allocated

physician

resources

more

effective

which

led

to an

expected

cost- benefit

of

7%

and

the

increased

customer

satisfaction.

• Analyzed

patient- level

data

to

refine

the

working

process

to

reduce

the

patient

left- without- being- seen

rate

by

24%.

• Established

R

Shiny

dashboard

and

front- end

decision

supporting

tool

within

the

company

environment

to

visualize resource

allocation

and

simplified

the

process

by

cutting

down

about

20%

process

time. Merchandise

Analyst

Intern,

eBay 01/2015

-

08/2015

• Analyzed

existing

customer

purchase

pattern

and

built

predictive

models

to

forecast

future

e- commerce

market.

• Wrote

SQL

queries

to

extract

product

sales

data

and

visualized

quantitative

data

in

Tableau

to

figure

out

which

key factors

influenced

sales

revenue.

• Collaborated

with

team

members

in

preparing

monthly

reports

and

providing

strategic

insights

to

decision

makers.

• Built

executive- facing

dashboards

to

monitor

inventory,

track

the

progress

of

KPIs

and

develop

reports

automatically. Business

Analytics

Projects

Humana

Case

Competition

Team

Leader,

University

of

California- San

Diego 10/2017

-

11/2017

• Utilized

R(ggplot)

to

investigate

data,

examine

the

histograms

and

scatterplots

of

patient

records.

• Plotted

monthly

trend

lines

for

different

categorical

variables

to

figure

out

influential

factors

of

patient

admission- rate and

help

the

healthcare

insurance

company

to

make

data- based

decisions.

• Built

linear

regression

models,

and

set

up

various

time

lengths

to

analyze

the

change

in

admission- rate

which

may

be affected

by

a

seasonal

event.

• Explored

correlations

between

patient

admission- rate

and

certain

causes

by

constructing

correlation

tables

and

models and

applied

SVM

to

develop

models

to

predict

future

trends

of

patient

admission

and

readmission. Specialized

Skills

• Languages/Coding:

R

(Advanced),

Python/Pandas,

SQL

• Operating

Systems:

Microsoft

Office

(Excel,

PowerPoint),

Google

Analytics,

VBA,

Tableau,

QlikView,

SAS,

SPSS,

AWS

• Machine

Learning:

SVM,

OLS,

Random

Forest,

KNN,

XGBoost;

NLP;

K- means

Clustering;

Experimental

Design;

A/B

testing

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