Y IFAN&WEI&

****$Ellendale$Pl,$Los$Angeles$CA****7$

Cell:$213*********$ $Email:$ac3fjp@r.postjobfree.com LinkedIn:$www.linkedin.com/in/yifan8wei$$

$

S UMMARY&&

Data$scientist$with$3+$years$hands8on$analytics/programming$experience.$USC$graduate$with$solid$academic$record$ and$excellent$sense$of$teamwork$and$critical8thinking.$Served$as$team$lead$several$times$with$proven$record$of$high$ quality$delivery.$Looking$for$data$scientist/analyst$opportunity

! Programming Python,$Java,$SAS,$R,$SQL,$MATLAB$

! Tools/Platforms:$$Excel/Office,$Linux,$OSX,$Windows$

! Skills Statistic,$Mathematic$modeling,$Machine$learning,$Analytics$tools$

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E DUCATION&&

University&of&Southern&California,$Los$Angeles,$CA Feb.$2016$–$August$2017$$ M.S.$in$Applied$Mathematics

• Relevant(Courses:(Analysis(of(Algorithms,(Data(Analysis,(Machine(Learning,(Applied(Probabilities,(Complex( Analysis,(Methods(of(Applied(Mathematics,(Numerical(Analysis(and(Computation( South&China&Normal&University, Guangzhou,$China Sept.$2010–June$2014& B.S.$in$Applied$Mathematics$(Dean's$Honor$List GPA$3.6/4.0$

• Relevant(Courses:(Mathematic(analysis,(Applied(regression(analysis,(Stochastic(process,(Discrete(mathematics,( Applied(time(series,(Multiple(regression(analysis,(Logistic(analysis,(Survival(analysis

$

E XPERIENCE&

Shenzhen&LanDYou&Technology,$Guangzhou,$China Data&Scientist&(Intern July&2016&–&August&2016&

• Tools$used:$$SQL,$Python,$Machine$learning$

• Worked$in$multiple$areas$(data$analysis$and$application,$automotive$IT$and$smart$cars$business$support)$

• Developed$software$that$could$automatically$classify$the$customer$reviews$into$negative$or$positive$categories$

• Wrote$Unit$Test$for$the$application,$increased$test$coverage$from$<50%$to$~80%,$maintained$and$improved$the$ existing$application$to$meet$new$business$demands$

• Apply$Bayesian&Theorem$to$do$word$segmentation$for$each$customer$review/evaluation$

• Built$positive$and$negative$dictionaries$by$analyzing$and$mining$big$data$

• Completed$the$project$four$days$ahead$of$deadline,$with$~83%$correctness$ratio$

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P ROJECTS &

Stock&Forecast&based&on&Machine&Learning&(Team$Lead Jan&2017&–&May&2017&

• Applied$program$to$forecast$the$stock$trend$using$four$machine$learning$algorithms:$decision&tree,&logistic& regression,&neural&network$and$support&vector&machine&

• Sampled$2000$listed$stocks$in$2016$US$stock$market,$selected$nice$indicators$for$each$stock$to$do$the$classification$

• Applied$training$and$testing$with$85%$and$15%$of$sample$data$correspondingly,$developed$forecast$with$Python$

• Evaluated$the$four$methods,$and$SVM$won$with$~76%$accuracy$ Linear&Regression&Analysis&for&Disease&Datasets Oct.&2016–Dec.&2016$

• Preprocessed$three$coronary$artery$disease$datasets$(each$has$3500$records$with$11$attributes)$from$research$ centers$in$California.$Completed$data$verification,$P&value$calculation,$variance&analysis,&TDtest$etc.$$

• Built$a$linear$regression$model,$cleaned$noise$points$and$evaluated$whether$to$add$spline$or$higher&order$term$in$ the$model,$$applied$model&selection$method$from$SAS$

• Performed$the$analysis&of&variance&and&covariance,$checked$confounder$possibility$in$the$model$ Data&Structure&and&Algorithm&Design$(Individual August&2016–Dec.&2016$

• Applied$greedy$algorithm,$dynamic$programming,$divide$and$conquer$algorithm$to$solve$the$sorting$problem,$$ solved$the$maze$and$other$classic$programming$problems$

• Used$and$compared$core$data$structure:$HashMap,&HashTable,&LinkedList,&ArrayList,&stack&in$Java$and$Python$ Numerical&Analysis&Course&Experiments$(Individual May2016–June&2016$

• Applied$numerical$approximations$to$differential$equations$by$the$Finite&Difference&Method$(e.g.$use$Poisson$ matrix$to$approximate$the$second$derivative$

• Utilized$the$GaussDSeidel$and$Chasing&Method$to$solve$linear$equations,$developed$the$program$in$MATLAB$

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