Аналитик (SQL, Looker Studio, PowerBI) в IT стартап

Описание

ED UCATIO N BS in B ig D ata A naly sis , A sta n a IT U niv ersit y •H old er o f e d u ca tio n al s c h ola rs h ip o f t h e M in is tr y o f E du ca tio n a n d S cie n ce o f th e R ep u bli c o f K aza k h sta n •G PA 3 .5 0 9/2 0 20 – 0 6/2 0 23 A sta n a, K aza k h sta n P R O FE SSIO N AL E X PE R IE N CE Ju n io r D ata A naly st, Y esse n & C om pan y •P erfo rm ed d ata c le a n in g a n d p re p ro ce ssin g u sin g P yth on •B uilt d ash board t o m on it o r E m plo ye e p erfo rm an ce 0 2/2 0 23 – 0 6/2 0 23 A sta n a, K aza k h sta n •B uilt f in an cia l r e p ortin g f o r b u dgetin g a n d f o re ca stin g D ata A naly st I n te rn , J S C H RD C •Perfo rm ed d ata c le a n in g a n d d ata p re p ara tio n •P re p are d s a le s a n aly sis w it h s ta tis tic a l i n te rp re ta tio n o f t a b le s, c h arts 0 5/2 0 22 – 0 8/2 0 22 A sta n a, K aza k h sta n •C re a te d d ata v is u ali z a tio n o n k ey m etr ic s a n d p erfo rm an ce m ea su re s P R O JE C TS Fin an cia l R ep ortin g •D esig n ed a n d b u ilt d ash board i n P ow er B I t o m on it o r s a le s p erfo rm an ce •Im ple m en te d D AX c a lc u la tio n s t o i m pro ve d ata m od el 0 5/2 0 23 – 0 6/2 0 23 U se r B eh avio r A naly sis o n Socia l M ed ia •I m ple m en te d S V M , K N N m od els f o r u se r b eh avio r c la ss if ic a tio n •B uilt p ro d u ct r e co m men datio n s y ste m 0 3/2 0 23 – 0 5/2 0 23 K an ban Perfo rm an ce D ash bo ard •Im ple m en te d i n G oog le D ata S tu dio •C on ta in s a c h ain o f g oals a ch ie ve d , t im eli n es, w ork c y cle t r a ck in g a n d em plo ye e's a tte n dan ce m on it o rin g 03/2 0 23 – 0 4/2 0 23 H um an A ctio n R eco gn it io n •Im ple m en te d L S T M m od el u sin g S m artp h on e A cce le ro m ete r d ata •T ra in ed m od el l e a rn s c o m ple x f e a tu re s a u to m atic a lly f r o m t h e s e q u en tia l d ata t o p re d ic t t h e c la ss l a b el 0 1/2 0 23 – 0 2/2 0 23 N azg u l N ugm etu lli n a Data A naly st nazg u ln yg m etu lli n a@ gm ail. c o m | + 770 74 459 774 | w ww.li n ked in .c o m /in /n azg u l- n ugm etu lli n a CER TIF IC ATE S Am azo n W eb S erv ic e s (A W S) D ata E ngin eerin g C ou rse C ertif ic a tio n A m azo n W eb S erv ic e s (A W S) C lo u d Fo u n datio n C ou rse C ertif ic a tio n C IS C O N etw ork in g A ca d em y IT E sse n tia ls C ou rse C ertif ic a tio n S K IL L S Soft S kills ( C om mun ic a tio n , P ubli c S pea k in g, O pen – m in ded n ess, T ea m w ork , T im e m an ag em en t) P ro gra m min g L an gu ag es ( P yth on , S Q L) M ach in e L ea rn in g ( L in ea r R eg re ssio n , L og is tic R eg re ssio n , K N N, R an dom F o re st, S V M ) Pack ag es ( N um Py, P an das, S cik it - L ea rn , M atp lo tli b , S ea b orn , J u pyte r L ab ) D ata V is u ali z a tio n ( P ow er B I, T ab le a u , L ooker S tu dio , E xce l, G oog le S h eets ) L A N GUAG ES Kaza k h K azte st C 1 C ertif ic a te E ngli s h IE LT S B 2 C ertif ic a te S pan is h E le m en ta ry p ro fic ie n cy R ussia n F lu en t T urk is h In te rm ed ia te I N TE R EST S Volle yb all | B ach ata D an ce | L ea rn in g L an gu ag es | N etw ork in g | To g yz k u m ala k | Y o ga