G ădina u, Giani Ionel; Dinu, Vasile; Ro a u, Că ălin-Lau ențiu; Toma, And eea
A icle
The de elopmen o educa ional compe ences o
Romanian s uden s in he con ex o he e olu ion o da a
science and a i icial in elligence
Am i ea u Economic
P o ided in Coope a ion wi h:
The Bucha es Uni e si y o Economic S udies
Sugges ed Ci a ion: G ădina u, Giani Ionel; Dinu, Vasile; Ro a u, Că ălin-Lau ențiu; Toma, And eea
(2024) : The de elopmen o educa ional compe ences o Romanian s uden s in he con ex o
he e olu ion o da a science and a i icial in elligence, Am i ea u Economic, ISSN 2247-9104, The
Bucha es Uni e si y o Economic S udies, Bucha es , Vol. 26, Iss. 65, pp. 14-32,
h ps://doi.o g/10.24818/EA/2024/65/14
This Ve sion is a ailable a :
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AE
The De elopmen o Educa ional Compe ences o Romanian S uden s
in he Con ex o he E olu ion o Da a Science and A i icial In elligence
14 Am i ea u Economic
THE DEVELOPMENT OF EDUCATIONAL COMPETENCES FOR ROMANIAN
STUDENTS IN THE CONTEXT OF THE EVOLUTION OF DATA SCIENCE
AND ARTIFICIAL INTELLIGENCE
Giani Ionel G ădina u1, Vasile Dinu2
,
Că ălin-Lau ențiu Ro a u 3
and And eea Toma4
1)Bucha es Uni e si y o Economic S udies, Bucha es , Romania
and Ins i u e o Na ional Economy, Romanian Academy,
Bucha es , Romania
2)Bucha es Uni e si y o Economic S udies, Bucha es , Romanian and Academy
o Scien is s, Bucha es , Romania
3)4)Bucha es Uni e si y o Economic S udies, Bucha es
Please ci e his a icle as:
G ădina u, G.I., Dinu, V., Ro a u, C.L. and Toma, A.,
2024. The De elopmen o Educa ional Compe ences
o Romanian S uden s in he Con ex o he
E olu ion o Da a Science and A i icial In elligence.
Am i ea u Economic, 26(65), pp. 14-32.
DOI: h ps://doi.o g/10.24818/EA/2024/65/14
A icle His o y
Recei ed: 17 Sep embe 2023
Re ised: 6 No embe 2023
Accep ed: 7 Decembe 2023
Abs ac
The s udy explo es key academic compe encies and p o essional skills in da a science in he
con ex o he de elopmen o a i icial in elligence, highligh ing hei impo ance in he
business en i onmen . Using he “2022 S ack O e low Annual De elope Su ey” da ase
and machine lea ning me hods such as p incipal componen analysis, K-means clus e ing,
and logis ic eg ession, p o essional skills in science a e analysed he da a. The esea ch
a ge s he dis ibu ion o jobs in he ield, he le el o expe ience, he languages and
analysis p og ams used, he suppo o e ed by companies, and he dynamics o da a
science eams, as well as he impac ha a i icial in elligence has on he ield. Wi h hei
help, a comp ehensi e unde s anding o he impac o academic aining on ca ee
oppo uni ies in he ield o da a science is p o ided, con ibu ing o he de elopmen o he
p o ile o he quali ied specialis in his ield. The esea ch also p o ides ele an poin e s
and ecommenda ions o enhancing he skills equi ed in da a science in o de o ou line a
skilled p o ile and ul il he demands o he business en i onmen in a wo ld domina ed by
da a analy ics and a i icial in elligence. By including academic skills in he p ocess o
Co esponding au ho : Giani Ionel G ădina u – e-mail: giani.g adin[email p o ec ed]
This is an Open Access a icle dis ibu ed unde he e ms o he C ea i e Commons
A ibu ion License, which pe mi s un es ic ed use, dis ibu ion, and ep oduc ion in any
medium, p o ided he o iginal wo k is p ope ly ci ed. © 2024 The Au ho (s).
Challenges o Compe ence-O ien ed Educa ion in he Con ex
o he De elopmen o A i icial In elligence Sys ems
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Vol. 26 • No. 65 • Feb ua y 2024 15
aining da a science specialis s, he esea ch b ings inno a ion and highligh s he skills
needed o be ained in he academic ield o acili a e he employmen o g adua es in
speci ic ields o da a science. This aspec is signi ican because, in p ac ice, i has been
obse ed ha mos specialis s wo king in da a science ely on independen lea ning a he
han skills acqui ed in he academic ield.
Keywo ds: da a science, a i icial in elligence, academic skills, p o essional skills.
JEL Classi ica ion: I23, J23, C49, M15.
In oduc ion
Da a science has become one o he mos exci ing ields in he age o echnology and
a i icial in elligence (AI). Wi h he ad ancemen o echnology and he inc ease in he
amoun o da a a ailable, he job ma ke has expe ienced a signi ican inc ease in he
numbe o da a science jobs. Uni e si ies ha e ecognised he impo ance o his ield and
s a ed building specialised p og ams o ain u u e da a scien is s. A he same ime,
specialised publica ions cons an ly and ac i ely w i e abou his opic, highligh ing he
p og ess and new ends in he ield.
Acco ding o Smaldone e al. (2022), he ole o a da a scien is usually in ol es he
iden i ica ion o business di ec ions, p o iding decision suppo o he equi emen s and
needs o he business en i onmen . Thus, communica ion skills ha e a c i ical ole, being
necessa y o media e he language used s a ing om he echnical analysis o he
in o ma ion ansmi ed o he business, managemen eam, whe he we e e o an
in elligen da a isualisa ion o no . Cu iosi y and c ea i i y a e key skills, because among
la ge da a se s, a da a science specialis has o sea ch, esea ch, es , and d aw a ious
hypo heses ha will be he basis o he p oposed solu ions.
In he con ex o he de elopmen o a i icial in elligence and da a science, academic skills
play an essen ial ole in he ad ancemen o hese ields. Bo h AI and da a science a e based
on he ounda ion o academic esea ch and educa ion o de elop inno a i e solu ions and
unde s and he complexi y o hese echnologies (Leal e al., 2023).
In he con ex o da a science and a i icial in elligence, academic skills a e c i ical o
collec , p ocess and in e p e da a. Da a scien is s mus unde s and he ma hema ics and
s a is ics behind analy ics and machine lea ning echniques o c ea e accu a e and obus
models. As s a ed by I iza y (2020) de eloping e ec i e AI algo i hms equi es a solid
knowledge base in p og amming, ma hema ics, da a analysis, and machine lea ning.
Unde s anding undamen al AI concep s such as neu al ne wo ks, deep lea ning, and
machine lea ning algo i hms enable esea che s o c ea e and op imise ad anced AI models.
In pa allel wi h he echnical aspec s, academic skills a e closely ela ed o he e hical and
social aspec s o AI and da a science (Chen e al., 2020). P o essionals in hese ields mus
be awa e o he impac ha de eloped echnologies can ha e on socie y, as a esul , i is
necessa y o conside p i acy issues and iden i y possible isks and biases. They mus
app oach he de elopmen o AI echnologies esponsibly and wo k o ensu e e hical and
ai use o hese echnologies.
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The De elopmen o Educa ional Compe ences o Romanian S uden s
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16 Am i ea u Economic
Specialis aining p og ams, cou ses, and academic p og ams ha e e ol ed simul aneously
wi h he labou ma ke , bu he b anches o he ield a e di e se. Whe he e e ing o da a
enginee s, who ensu e he p ope low o aw da a needed o analysis, o da a analys s,
who deal wi h iden i ying da a sou ces, cleansing, and quali y assu ance o he se , as well
as in elligen ly isualising he esul s, o o specialis s in da a science, which builds
p edic i e models, hey ep esen oles ha a e pa o he same ield. Howe e , each ole
equi es igo ous aining o become a specialis in he analy ics p ocess, om da a
accumula ion, cleaning, p ocessing, modelling, in e p e a ion, o e en p edic ion. In his
con ex , mos o he ime aining s a s in academic p og ams (I iza y, 2020). I is
highligh ed ha he connec ion be ween he p o essional and he academic en i onmen is
e y impo an , because in o de o be able o occupy he posi ion o specialis in da a
science in a ce ain ield, i is necessa y o p epa e and de elop he necessa y skills.
Wing (2019) s a es ha da a science appea s o be a as ield de ined as a se o
undamen al p inciples ha guide he ex ac ion and unde s anding o da a. I is o en
associa ed wi h e ms like BD (Big Da a), da a mining, and AI. To ully unde s and his
ield, i is necessa y o iden i y he unde lying p inciples and unde s and wha i o e s. This
p ocess p o ides cla i y in da a science equi emen s o bo h academic and p o essional
en i onmen s.
The aim o he cu en esea ch is o expose he i al link be ween he educa ional
compe encies and he p o essional skills equi ed in he ield o da a science, in he con ex
o he de elopmen o AI wi h he help o quan i a i e me hods used in machine lea ning
(ML - Machine Lea ning, a b anch o AI), such as componen analysis main me hods, K-
means Clus e ing (KM) and logis ic Reg ession (RL), and o de e mine he p o ile o he
specialis who can be conside ed quali ied o unquali ied in he ield o da a science.
In he i s phase, he esea ch ocuses on he p o essional skills sough in he ield o da a
science, including he echnical and analy ical skills needed o become a specialis in his
ield and o mee he demands o he business en i onmen . I will hen iden i y he ypical
p o ile o a da a scien is and highligh i s impo ance in he business en i onmen by
explo ing he ole, esponsibili ies, and added alue i b ings o decision-making and he
de elopmen o solu ions based on da a analysis. Finally, he esea ch ocuses on
iden i ying he essen ial academic skills o become a da a science specialis , by analysing
academic p og ams, cou ses, and ele an s udy subjec s ha con ibu e o he de elopmen
o hese skills, such as p og amming, da a managemen , and a i icial in elligence cou ses.
The p esen esea ch aims o explo e and highligh he key p o essional and educa ional
compe encies in da a science, alongside hei impo ance in he business en i onmen ,
p o iding a comp ehensi e unde s anding o he impac o academic aining on ca ee
oppo uni ies in his ield, as well as suppo ing he impo ance o AI as a key asse in he
de elopmen o he science p o ile o he da a.
1. Re iew o he scien i ic li e a u e
The use o s a is ical me hods and models plays a c ucial ole in he educa ion sec o , by
e olu ionising he way his ield iden i ies he skills and knowledge ha mus be
in oduced in o he educa ion cu iculum o he aining o da a science specialis s.
Challenges o Compe ence-O ien ed Educa ion in he Con ex
o he De elopmen o A i icial In elligence Sys ems
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Vol. 26 • No. 65 • Feb ua y 2024 17
Th ough he use o hese ad anced echniques, he need o align uni e si ies wi h he
demands o he da a science ma ke has been highligh ed.
Educa ional and esea ch p ac ices can be shaped by he AI – ML couple in he u u e,
leading o imp o emen s. As s a ed by Alqah ani e al. (2023) in esea ch, key applica ions
include ex gene a ion, da a analysis and in e p e a ion, li e a u e e iew, o ma ing, and
edi ing. In he educa ion sec o , he AI – ML couple can suppo pe sonalised lea ning,
assessmen , speci ic lea ning plans, pe sonalised ca ee guidance, and men al heal h
suppo . Despi e he ad an ages, he esponsible use o hese echnologies mus add ess
e hical and algo i hmic imbalance issues. Acco ding o Chassignol e al. (2018), AI is
e olu ionising educa ion by p o iding pe sonalised lea ning solu ions, adap ing eaching
me hods h ough he eme gence o indi idual guidance and e ec i e assessmen solu ions.
Al hough AI-powe ed pla o ms pe sonalise he con en , conce ns abou AI dominance s ill
linge . Howe e , esea che s s ill d aw a en ion o he impo ance o main aining human
men o ing and social in e ac ion in educa ion, a ac also emphasised by Tuba e al. (2023),
who claims ha AI ools do no con ibu e o he ull de elopmen o academic skills, no
ye ha ing he abili y o sys ema ise in o ma ion. Howe e , AI can in luence he u u e
oles o humans and hei ways o doing business. Pelău, Ene and Pop (2021) a gues ha
he de elopmen o he AI – ML couple ep esen s one o he main pa adigms o
con empo a y socie y, ha ing a signi ican impac on indi idual li e and socie y as a whole,
as well as on he economy. The use o AI in people’s daily ac i i ies and in he in e ac ion
be ween companies and consume s b ings many ad an ages, such as inc eased e iciency
and an engaging in e ac ion. Howe e , he e a e also conce ns abou he u u e
de elopmen o his ield. Amids i s abili y o s o e a la ge amoun o da a abou
indi iduals’ beha iou and p ocess his da a quickly, he e is a isk ha o ms o AI will
become sma e han humans and in luence hei decisions.
Pa icula ly in e es ing o scien i ic p og ess and indus y e olu ion is he apid ansi ion
o he AI – ML couple om a na ow a ea o esea ch in a limi ed numbe o academic
labo a o ies o a key opic in he business wo ld. This p ocess is accompanied by he
eme gence o o he new pa adigms, such as BD and he In e ne o Things (IoT). In
addi ion o hese, a new discipline called da a science g adually ook cen e s age as he
main b anch o knowledge, co e ing all ele an me hods and wo k p ocesses o ansla e
da a in o e ec i e business solu ions. Lea ning he main p inciples and capabili ies o his
discipline becomes a new academic and business challenge. Ano he cause is ep esen ed
by he eme gence o da a specialis s (specialis in da a science), who ha e become
mo i a ed ac o s o his ans o ma ion (Ko don, 2020).
The e m da a science was in oduced wo decades ago, b inging new opics o discussion
and analysis in a ious ields (Cao e al., 2016). Acco ding o G ossi e al. (2021), da a
science is an impo an sec o wi h applica ions in business, indus y, and educa ion,
encompassing a eas such as AI and ML, expe imen al design, and da a-d i en modelling.
Mo eo e , s a is ics becomes an essen ial discipline, p o iding ools and me hods in he
p ocess o iden i ying s uc u e, p o iding an o e iew o he ield o da a science. In his
con ex , ob aining eliable esul s, de eloping p edic i e models, and unde s anding da a
s uc u es a e con e ed by s a is ics. The idea ha s a is ics, along wi h ML, plays a
undamen al ole in da a science is ei e a ed by a g oup o leade s o he Ame ican
S a is ical Associa ion, which issued a s a emen on he special powe s o hese disciplines,
highligh ing as cen al elemen s o he cu en ield. Dyk e al. (2015) says ha , da a
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18 Am i ea u Economic
science is a se o undamen al p inciples ha suppo and guide he ex ac ion o
in o ma ion and knowledge om da a. A concep closely ela ed o his ield is da a mining,
he e ec i e ex ac ion o knowledge om da a h ough echnologies ha in eg a e
p op ie a y p inciples. The applicabili y o da a science is a ied, dis inguishing i sel in
business, ma ke ing, inance, sales, and mo e. The e o e, a success ul da a scien is mus be
able o app oach business p oblems om a da a-d i en pe spec i e and unde s and he
undamen al p inciples o da a analysis (P o os and Fawce , 2013).
I is necessa y o s a om he basics o s a is ics and pay special a en ion o he e olu ion
o da a science, in o de o achie e an exhaus i e p esen a ion o his ield (Donoho e al.
2015). In he con ex o AI de elopmen , da a a e oday conside ed aluable asse s, and
da a mining has become a e y impo an ac o in imp o ing he compe i i eness o
en e p ises. This apidly g owing ield encompasses in o ma ion managemen , social
sciences and compu e science. The domain o big da a is cha ac e ised by he eloci y,
olume and a ie y o da a, making i aluable o da a mining. Howe e , many companies
s uggle o ully le e age he alue o Big Da a amid a lack o skilled p o essionals and
unce ain y abou how o e ec i ely implemen big da a analy ics o deli e business alue.
Thus, o maximise he po en ial o p ojec s, o ganisa ions seek da a science p o essionals
and business expe s who ully unde s and he company’s business model (Xu e al., 2022).
The de elopmen o da a science is he main d i e o he new gene a ion o AI – ML
andem specialis s. These ields a e a ac ing inc easing in e es om s a es, companies,
and he educa ional sec o , enjoying impo an ini ia i es om a ious ac o s such as he
Uni ed S a es, China, and he Eu opean Union. Wing (2019) show ha he ield o da a
science in ol es a long p ocess, which encompasses bo h da a analysis, he wo k done
be o e and a e i , he issues ela ed o e hics and da a p i acy, as well as he impo ance o
da a collec ion, p ocessing, s o age, managemen , analysis, and isualisa ion, alongside he
alo isa ion o all by use s, o decision-making in a ious ields. The li e cycle o da a is a
complex one, consis ing o a ious s ages, s a ing om da a gene a ion o hei
in e p e a ion, wi h an applicabili y also a he le el o AI echniques. Fu he mo e, i is
necessa y o p omo e e hical esponsibili y and da a p o ec ion o each phase o he da a
li e cycle.
The con empo a y pe iod is cha ac e ised by a apid inc ease in he popula i y and u ili y o
da a science, due o i s use in business, indus y, and academia (Ley and Bo das, 2018). De
Veaux e al. (2016) p edic in hei s udies he need o hund eds o housands o jobs in he
ield o da a science in he nex decade, leading o an inc ease in s udy p og ams in his
ield wi hin uni e si ies. A he le el o he business en i onmen , companies om a ious
b anches ha e no iced he need o hi e mo e people in he da a science depa men . The
da a science job ma ke p o ides a de ailed lis o job i les such as Da a Science Specialis ,
Da a Analys , Da a Enginee , S a is ician, Da a and Business Analys , Business In elligence
Analys . In his sense, i is e y impo an o clea ly de ine he skills equi ed o his sec o ,
as well as hose speci ic o each unc ion. As o he academic b anch, he e is a apid
inc ease in he numbe o ins i u ions wishing o de elop p og ams o ain specialis s in
his ield. In addi ion o hese, he e is also a p omo ion o he ield o da a science, ca ied
ou by a ious publica ions, which p esen s his ca ee as an in e es ing and iable op ion
o he u u e (P o os and Fawce , 2013).
Rega ding he ole o a da a scien is , i was desc ibed by Cao (2019) as he mos a ac i e
wo kplace o he 21s cen u y. The ole o da a analy ics, da a science and he AI-ML
Challenges o Compe ence-O ien ed Educa ion in he Con ex
o he De elopmen o A i icial In elligence Sys ems
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Vol. 26 • No. 65 • Feb ua y 2024 19
couple in in o ma ion and communica ion echnology and in he disciplines o science,
enginee ing, and echnology is essen ial. Howe e , he quali ica ions and compe encies o a
da a scien is a e no ye clea ly de ined. In his sense, he e is a need o es ablish he du ies
o a da a science specialis who belongs o he new gene a ion, able o ans o m science,
echnology, inno a ion and he cu en and u u e economy. While he e is a no able
inc ease in he numbe o da a science cou ses helping o de ine his new p o ession,
employe s in his segmen , including en ep eneu s, salespeople, and la ge en e p ises,
epo he limi ed a ailabili y o quali ied da a specialis s o assis hem in s a egic
de elopmen and gi es hem compe i i e ad an ages in he u u e. Cao (2019) highligh ed
he disc epancies in exis ing p o essional and educa ional ma ke s, he lack o
s anda disa ion and acc edi a ion o da a science esponsibili ies and compe encies, and he
u gen need o s anda dise and imp o e da a science quali ica ions and educa ion.
In he con ex o he job ma ke , i is essen ial o de ine he skills equi ed o Business Da a
Analy ics and Da a Science specialis posi ions. Using a ex analysis by Rado ilsky e al.
(2018) on he Glassdoo .com pla o m, he mos sough -a e skills a e shown o be Py hon
- 72% (Familia i y wi h Py hon syn ax and ecosys em), R-64% (Knowledge o s a is ical
p og amming language R), SQL - 51% (Managing da abases using s uc u ed que ies),
Hadoop 39% (P ocessing Big Da a wi h Hadoop), Ja a - 33% (Familia i y wi h Py hon’s
syn ax and ecosys em), SAS - 30% (Knowledge o SAS s a is ical p og amming language),
Spa k - 27 % (P ocessing Big Da a wi h Spa k), Ma lab - 20% (Knowledge o nume ical
compu a ion and p og amming in Ma lab), Hi e - 17% (P ocessing Big Da a wi h Hi e),
and Tableau - 14% (Knowledge and concep s o s a is ical isualisa ion). As da a science
p o essionals a e inc easingly in demand and expe ienced ones a e s ill a e, he e o e, he
idea a ises ha heo e ical aining is no enough o excel in his ield. In addi ion o
knowledge o p og ams and algo i hms, p ac ical expe ience in sol ing eal business
p oblems plays a pa icula ly impo an ole (Be hold e al., 2019).
Technology and he ield o wo k ha e e ol ed and he cu iculum needs o adap o he
new equi emen s (Ha din e al., 2015). The li e a u e on Big Da a skills mainly ocuses on
he demand side (companies), he supply side (uni e si ies and s uden s), and he
ela ionship be ween hem. Following an analysis o ec ui men in o ma ion om
companies ( o iden i y he knowledge and skills equi ed o BD posi ions) and an
assessmen o uni e si y p og ams ( o see i academic aining aligns wi h indus y
expec a ions), i was de e mined ha he in oduc ion o a mo e sus ainable and e icien
sys em o aining and managing skills in his ield. In addi ion, s uden s’ pe spec i es
should be aken in o accoun , o be e unde s and hei le el o compe ence and o assess
he e ec i eness o uni e si y p og ams (Xu e al., 2022).
Cu en ly, acco ding o Bile Hassan e al. (2021), he e a e mo e han 530 p og ams in da a
science, analy ics, and ela ed ields, mos o which a e mas e ’s and ce i ica e p og ams,
o e ed bo h online and in adi ional ins i u ions. In his sense, an example o good p ac ice
is ep esen ed by he guide made o academic ins i u ions wishing o de elop bachelo ’s
p og ams in his ield, which desc ibes a cu iculum o he bachelo ’s p og am in he ield
o da a science. The cu iculum is de ailed and p esen s cou ses in ela ed ields,
emphasising he impo ance o collabo a ion be ween depa men s and acul ies in he
p ocess o de eloping such an in e disciplina y p og am. Along wi h hese, he e is a he
le el o he educa ional o e , and a se o cou ses ha suppo he aining o u u e
specialis s in da a science, which emphasise disciplines such as explo a o y analysis, da a
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se cleaning, and ans o ma ion, he Bayesian s a is ical model ha essen ially in ol es
lea ning om da a and p obabili ies, eg ession models, dimensionali y educ ion, ML,
model pe o mance, da a mining om online pla o ms, sen imen analysis, ela ional
da abases (Hicks and I iza y, 2018).
2. Resea ch me hodology
In acco dance wi h he specialised li e a u e, he esea ch will ocus on he ollowing
pilla s: Da a Analysis, Da a Collec ion, Da abase Adminis a ion, E hics, P edic ion,
P og amming and P ojec Managemen . In o de o ul il he objec i e ela ed o he
academic skills in da a science ha s uden s acqui e a e g adua ing om mas e ’s
p og ams in da a science in Romania, he discipline shee s o he main mas e ’s p og ams in
he ield o science we e aken om he websi es o he uni e si ies om Romania. Each
discipline was included in one o he main pilla s o he analysis:
The Facul y o Ma hema ics and In o ma ics (FMI), which includes he mas e ’s
p og ams “Da a Science o Indus y and Socie y”, “Da a science”, “BD - da a science”,
“Analy ics and Technologies”, which has h ee main pilla s o in e es : Da a Collec ion,
Da a Analysis, and P og amming, complemen ed by disciplines included in E hics and
P edic ion.
The Facul y o Au oma ion and Compu e s (FAC), wi h he “Machine Lea ning” and
“Da abase Adminis a ion” mas e ’s p og ams, which includes mos o he pilla s. The mos
subjec s ocus on P og amming and P ojec Managemen , ollowed by Da a Collec ion,
Da abase Adminis a ion, Da a Analysis, and E hics.
The Facul y o Cybe ne ics, S a is ics and Economic In o ma ics (FCSIE), wi h he
mas e ’s p og am “Applied S a is ics and da a science”, includes mos disciplines ha
belong o he main objec i e o Da a Analysis, as well as P og amming and E hics
ac i i ies.
The Facul y o Economics and Business Adminis a ion (FEAA) wi h he Mas e ’s Da a
Mining, as p e iously p esen ed, ocuses on Da a Analysis, Da a Collec ion, and E hics.
F om he isualisa ion o he da a o he pilla s add essed by each acul y, he assimila ed
academic skills o each indi idual pilla we e g ouped in o p o essional skills, which could
also lea e hei ma k on he p o essional ca ee . Thus, an associa ion o da a science oles
and academic compe encies pu sued by each acul y in he lis abo e has been iden i ied.
To mee he esea ch objec i es ela ed o p o essional skills in he ield o da a science, he
2022 S ack O e low Annual De elope Su ey (S ack O e low, 2022) da a se was used
in he cu en analysis. I is based on he ques ionnai e applied in 2022 by S ack O e low,
c ea ed wi h he aim o iden i ying he expe ience o specialis s in he echnologies used
and hei expe ience in p o essional en i onmen s, globally. A e il e ing he esponses
based on he job held by he esponden s, eco ds o pa icipan s wi h a da a science
backg ound we e e ained, wi h 11071 esponses. In his con ex , he da ase unde pins he
analysis o p o essional da a science skills and p o ides insigh s o hypo heses such as:
Wha is he dis ibu ion o jobs in his ield?
How many yea s o expe ience do he specialis s ha e?
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Wha a e he p og amming languages, analysis p og ams used?
Wha is he suppo o e ed by companies o employees?
Wha a e he dynamics o da a science eams in companies?
Th ough he use o desc ip i e s a is ics, he in o ma ion om he da a se was syn hesised, his
being p esen ed in igu e no. 1, o which he Powe BI isualisa ion p og am was used.
Figu e no. 1. Da a Science – P o essional Skills
Sou ce: Au ho s ep esen a ion in Powe BI based
on he “2022 S ack O e low Annual De elope Su ey”
The “2022 S ack O e low Annual De elope Su ey” (S ack O e low, 2022) p o ides
insigh in o he da a science job ma ke . The main jobs on he da a science labou ma ke
iden i ied, acco ding o he ques ionnai e, a e: Da abase adminis a o , da a science
specialis o machine lea ning specialis , Da a Enginee s and Da a/Business Analys . O he
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Clus e 3 ep esen s ea ly-ca ee da a scien is s. They ind up- o-da e in o ma ion and know
whe e o look, bu he p ocess o inding he in o ma ion hey need akes he longes . They
in es a lo o ime in answe ing hei wo k, especially du ing he onboa ding pe iod, and make
hea y use o lea ning esou ces p o ided by employe s o de elop hei p o essional skills.
Fo he second componen , he KM Clus e ing analysis was applied again, esul ing in he
i s clus e wi h 4555 esponden s, and he second wi h 6516. To cha ac e ise each clus e ,
able no. 6, in which only signi ican a iables wi h R-Squa e > 0.5 we e aken in o
accoun .
Table no. 6. Clus e Means
The name o he a iables
Clues e no. 1
Clues e no. 2
Time Sea ching
0.99
0.25
Time Answe ing
0.99
0.27
T ue/Fals_2
0.98
0.31
Sou ce: Au ho s’ own p ocessing using SAS S udio on based
on he “2022 S ack O e low Annual De elope Su ey”
Clus e 1 includes esponden s who ha e a long ime in he p ocess o sea ching o an
answe and who use he lea ning esou ces p o ided by employe s he mos . I is he g oup
o da a science specialis s in he p ocess o de eloping p o essional skills.
Clus e 2 includes he esponden s o whom inding solu ions in hei wo k akes he leas
ime and hey use he company’s lea ning esou ces in a small o medium pe cen age. Thus,
his is he clus e wi h specialis s who ha e comple ed he p ocess o de eloping
p o essional skills.
Also, o he wo componen s, he KM Clus e ing me hod was applied, wi h he aim o
obse ing which clus e o he wo each model belongs o. This was done o cons uc a
new bina y a iable ep esen ing he p o ile o he da a scien is and o be u he used as a
dependen a iable o p edic i e pu poses.
Logis ic eg ession is cons uc ed in o de o model and p edic he bina y dependen
a iable c ea ed abo e ( he p o ile o he quali ied/unquali ied specialis in da a science),
acco ding o he independen a iables, he wo componen s iden i ied in he p e ious
analyses: he i s ela ed o he echnical p o essional skills o he esponden s on he da a
science job ma ke and he second, which ep esen s he esponden s’ expe ience and
de elopmen on he da a science job ma ke .
The esul s ob ained in he p edic ion o he bina y dependen a iable (skilled/unskilled
da a scien is p o ile) we e:
Fo ca ego y 1 – specialis s wi h expe ience in he labou ma ke and de eloped
p o essional echnical skills, om he independen a iable componen 1 and o ca ego y 1
– specialis s in he p ocess o de elopmen , om he independen a iable componen 2, he
logis ic eg ession model p edic ed he alue 0 – quali ied p o ile.
Fo he same ca ego y 1 - specialis s wi h expe ience on he labo ma ke and de eloped
p o essional echnical skills, i ca ego y 2 is se o he second componen - specialis s who
ha e comple ed he de elopmen p ocess, he logis ic eg ession model p edic ed he alue
1 - unquali ied p o ile o a specialis om da a science.
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Fo ca ego y 2 – specialis s wi h an a e age le el o knowledge, om he independen
a iable componen 1 and o ca ego y 1 – specialis s in he p ocess o lea ning, om he
independen a iable componen 2, he model amed he p o ile o he specialis as a
quali ied one.
Fo ca ego y 3 – specialis s a he beginning o hei ca ee , om he independen
a iable componen 1 and o ca ego y 1 – p o essional skills in he p ocess o
de elopmen , om he independen a iable componen 2, he logis ic eg ession model
p edic ed he alue 0 – quali ied p o ile.
To alida e he logis ic eg ession model, he accu acy o he model was calcula ed o he
analysed da a se . The accu acy was 100% o he model included in he abo e analysis,
which shows ha he model co ec ly p edic ed he ou pu alues based on he inpu da a.
Following he logis ic eg ession model, he p o ile o he da a science specialis was
p edic ed acco ding o he cha ac e is ics o he wo independen a iables, and o be a
quali ied specialis he ollowing we e iden i ied: e en hough he le el o expe ience is
e y impo an , o be a specialis quali ied, con inuous and ac i e de elopmen is necessa y,
as a esul he ime and esou ces ha employe s p o ide a e impo an . Fo an a e age
le el o expe ience, by app ecia ing ca ee de elopmen oppo uni ies and in e es in
lea ning, employe s a e looking o such a p o ile. Fo specialis s a he beginning o hei
ca ee , he de elopmen o p o essional skills occupies a la ge pa o he cu en ac i i y.
Al hough he le el o expe ience is no as high, openness o lea ning is ecommended o
his ca ego y o employees.
By applying he logis ic eg ession model, he main conclusion was ha ega dless o he
expe ience le el o a da a scien is , i is he con inuous and ac i e de elopmen o
p o essional skills ha is ecommended and a ac s he a en ion o employe s. Thus, bo h
he lea ning oppo uni ies ha companies o e o hei employees, bu also he academic
skills acqui ed by specialis s, ha e a c i ical ole o hei p o ile.
Conclusions
The need o know he p o ile o he da a science specialis has become inc easingly
impo an , bo h o ou lining he academic p og am and o iden i ying he p o essional
skills needed in he labou ma ke . Thus, his esea ch ep esen s a use ul in o ma ional
suppo in o de o make decisions o adap ing academic p og ams o cu en equi emen s
and unde s anding he key skills o p ac ice in one o he oles unde he umb ella e m da a
science.
Conside ing he ac ha da a science ocuses on he knowledge o compu e p og ams,
algo i hms, and he accumula ion o expe ience in hei use, he i s pa o he esea ch
had as i s main objec i es he iden i ica ion o he p o essional skills needed on he labou
ma ke in his ield, bu also he ou line o he specialis s’ p o ile. The le el o expe ience,
echnical p o essional skills, and in e es in lea ning ou line he p o ile o he da a scien is .
Al hough he yea s o p o essional expe ience and he openness o he de elopmen o skills
will place a specialis in he ca ego y o hose quali ied and sough a e on he labou
ma ke , a signi ican ole o hei p o ile is played by he academic skills acqui ed by
specialis s, an idea ei e a ed by I iza y (2020).
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Comple ion o a bachelo ’s o mas e ’s p og am may be conside ed manda o y o p ac ice
da a science. In his sense, he educa ional plans o mas e ’s p og ams in Romania we e
analysed. One o he main esul s was o iden i y he associa ion o he academic skills
acqui ed by g adua ing om hese p og ams and he oles in he da a science labou ma ke .
Thus, FMI ou lines he academic skills equi ed o he oles o Da a Enginee s o Da abase
Adminis a o , FAC ou lines he di ec ion owa ds he ole o Da a Science Specialis , and
FEAA and FCSIE each and ain he skills equi ed o he ole o Da a Analys and
Business Analys .
Technical p o essional skills a e speci ic o each ole. On he one hand, knowledge in SQL,
Py hon o Shell Sc ip ing is no equi ed o a Da abase Adminis a o , Py hon, Ja a, o
C++ a e sough o DE. On he o he hand, o a DA o BA he equi emen s included a e
R, SAS, Py hon, SQL, Tableau o Powe BI and o he Da a Science Specialis ole skills
in Py hon, AI, SQL o R a e equi ed. Equally signi ican , he e a e also communica ion
skills, ime managemen , eamwo k, and analy ical hinking equi ed o all oles. The
p o ile o he da a scien is is de ined by he le el o expe ience, p o essional echnical
skills, and in e es in lea ning.
In conclusion, academic skills and p o essional skills depend on each o he . By aligning
s udy p og ams wi h he labou ma ke , da a science is a ield ha will ha e quali ied and
ained specialis s.
The esea ch is limi ed by he lack o da a o he yea 2023, as his is a yea in which AI
has de eloped a a apid pace, wi h da a science pa icipa ing in he de elopmen o
cha bo -like sea ch engines and helping o open up new oppo uni ies o da a scien is s. In
he u u e, i is necessa y o s udy he easibili y o in oducing cha bo - ype AI in he ield
o da a science in academic ins i u ions, e en mo e so in he con ex o inc easing labou
supply. As a esul , he e is a need o iden i y he main cha ac e is ics ha a specialis in
da a science should possess, wi h he aim o in oducing hem in o academic p og ams.
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