Ngokocwaningo olwenziwa yi-HeadHunter kanye ne-Mail.ru, isidingo sochwepheshe emkhakheni we-Data Science sidlula ukunikezwa, kodwa noma kunjalo, ochwepheshe abasha abakwazi njalo ukuthola umsebenzi. Siyakutshela ukuthi yiziphi izifundiswa ezilahlekile nokuthi zizofundela kuphi labo abahlela umsebenzi omkhulu ku-Data Science.
"Bafika bacabange ukuthi manje bazohola u-500k ngomzuzwana, ngoba bayawazi amagama ezinhlaka kanye nendlela yokuqhuba imodeli kuzo emigqeni emibili"
Emil Maharramov uhola iqembu lezinsizakalo ze-chemistry ye-computational ku-biocad futhi phakathi nezingxoxo ubhekene neqiniso lokuthi abazobhapathizwa abanakho ukuqonda okuhlelekile komsebenzi. Baqeda izifundo, beza ne-Python ne-SQL eqeqeshwe kahle, bangafaka i-Hadoop noma i-Spark ngemizuzwana emi-2, futhi baqedele umsebenzi ngokusho kwencazelo ecacile. Kodwa ngesikhathi esifanayo, asisekho isinyathelo sokuya eceleni. Yize kuwukuvumelana nezimo ezisombululweni abaqashi abazilindele kochwepheshe babo besayensi yedatha.
Kwenzekani emakethe Yesayensi Yedatha
Amakhono ongoti abasha akhombisa isimo emakethe yezabasebenzi. Lapha, isidingo sidlula kakhulu ukunikezwa, ngakho abaqashi abaphelelwe yithemba bavame ukulungele ukuqasha ochwepheshe abaluhlaza ngokuphelele futhi baziqeqeshe bona. Inketho iyasebenza, kodwa ifaneleka kuphela uma iqembu selinomholi weqembu onolwazi ozothatha ukuqeqeshwa kwabancane.
Ngokocwaningo olwenziwa yi-HeadHunter kanye ne-Mail.ru, ochwepheshe bokuhlaziya idatha baphakathi kwezidingeka kakhulu emakethe:
- Ngo-2019, kube nezikhala eziphindwe izikhathi ezingu-9,6 emkhakheni wokuhlaziya idatha, futhi izikhathi ezingu-7,2 ngaphezulu emkhakheni wokufunda ngomshini kunango-2015.
- Uma kuqhathaniswa no-2018, inani lezikhala zochwepheshe bokuhlaziya idatha likhuphuke izikhathi ezingu-1,4, futhi kochwepheshe bokufunda ngomshini izikhathi ezingu-1,3.
- U-38% wezikhala ezivulekile usezinkampanini ze-IT, u-29% ezinkampanini zomkhakha wezezimali, no-9% ezinsizakalweni zebhizinisi.
Lesi simo sigqugquzelwa izikole eziningi eziku-inthanethi eziqeqesha labo abancane abancane. Ngokuyisisekelo, ukuqeqeshwa kuthatha izinyanga ezintathu kuya kweziyisithupha, lapho abafundi bekwazi ukusebenzisa kahle amathuluzi abalulekile ezingeni eliyisisekelo: iPython, i-SQL, ukuhlaziywa kwedatha, i-Git ne-Linux. Umphumela uba umncane wakudala: angakwazi ukuxazulula inkinga ethile, kodwa akakwazi ukuqonda inkinga futhi enze inkinga ngokwakhe. Kodwa-ke, isidingo esikhulu sochwepheshe kanye ne-hype ezungeze lo mkhakha ngokuvamile kubangela izifiso eziphezulu kanye nezidingo zamaholo.
Ngeshwa, izingxoxo ku-Data Science manje ngokuvamile zibukeka kanje: umuntu ozobhalwa uthi uzame ukusebenzisa imitapo yolwazi embalwa, akakwazi ukuphendula imibuzo mayelana nokuthi ama-algorithms asebenza kanjani, bese ecela ama-ruble ayizinkulungwane ezingu-200, 300, 400 ngenyanga.
Ngenxa yenani elikhulu leziqubulo zokukhangisa ezinjengokuthi “noma ubani angaba umhlaziyi wedatha”, “ukufunda ngomshini oyinhloko ezinyangeni ezintathu futhi aqale ukwenza imali eningi” nokomela imali esheshayo, inqwaba yabakhandidethi abakha phezulu iye yatheleka kithi. insimu engenakho nhlobo ukuqeqeshwa okuhlelekile.
UVictor Kantor
I-Chief Data Scientist kwa-MTS
Balinde obani abaqashi?
Noma yimuphi umqashi angathanda ukuthi abancane bakhe basebenze ngaphandle kokugadwa njalo futhi bakwazi ukuthuthuka ngaphansi kokuqondiswa umholi weqembu. Ukuze wenze lokhu, oqalayo kufanele abe namathuluzi adingekayo okuxazulula izinkinga zamanje, futhi abe nesisekelo esanele sethiyori sokuphakamisa kancane kancane izixazululo zabo futhi abhekane nezinkinga eziyinkimbinkimbi.
Abasanda kuhlanganyela emakethe benza kahle kakhulu ngamathuluzi abo. Izifundo zesikhathi esifushane zikuvumela ukuthi uziqonde ngokushesha futhi uqale ukusebenza.
Ngokocwaningo olwenziwe yi-HeadHunter ne-Mail.ru, ikhono elifunwa kakhulu yiPython. Ishiwo ku-45% wezikhala zesayensi yedatha kanye no-51% wezikhala zokufunda ngomshini.
Abaqashi bafuna futhi abahlaziyi bedatha bazi iSQL (23%), i-data mining (19%), izibalo zezibalo (11%) futhi bakwazi ukusebenza ngedatha enkulu (10%).
Abaqashi abafuna ochwepheshe bokufunda ngomshini balindele ukuthi umuntu ozongenela ukhetho abe nekhono ku-C++ (18%), i-SQL (15%), ama-algorithms okufunda ngomshini (13%) kanye ne-Linux (11%) ngaphezu kolwazi lwePython.
Kodwa uma abancane benza kahle ngamathuluzi, abaphathi babo babhekene nenye inkinga. Iningi labaphothule izifundo abanalo ukuqonda okujulile ngalo msebenzi, okwenza kube nzima kumuntu osaqalayo ukuthuthuka.
Okwamanje ngifuna ochwepheshe bokufunda komshini ukuze bajoyine iqembu lami. Ngesikhathi esifanayo, ngibona ukuthi abantu abazobhapathizwa baye bafunda amathuluzi athile e-Data Science, kodwa abanakho ukuqonda okujulile kwezisekelo zethiyori ukuze bakhe izixazululo ezintsha.
Emil Maharramov
Inhloko yeComputational Chemistry Services Group, iBiocad
Isakhiwo kanye nobude bezifundo akukuvumeli ukuthi ujule ezingeni elidingekayo. Abathweswe iziqu bavame ukuntula lawo makhono athambile avame ukuphuthelwa lapho kufundwa isikhala somsebenzi. Nokho, empeleni, ubani phakathi kwethu ongasho ukuthi akanazo izinhlelo zokucabanga noma isifiso sokuthuthukisa. Kodwa-ke, maqondana nochwepheshe beSayensi Yedatha, sikhuluma ngendaba ejulile. Lapha, ukuze uthuthuke, udinga ukuchema okuqinile kwethiyori nesayensi, okungenzeka kuphela ngokutadisha isikhathi eside, isibonelo, eyunivesithi.
Okuningi kuncike kumuntu: uma isifundo esijulile sezinyanga ezintathu esivela kothisha abaqinile abanolwazi njengabaholi beqembu ezinkampanini eziphezulu siqedwa ngumfundi onesizinda esihle sezibalo nezinhlelo, sicubungula zonke izinto zokufunda futhi “amunce njengesipontshi. ,” njengoba besho esikoleni, kuzoba nezinkinga ngesisebenzi esinjalo ngokuhamba kwesikhathi No. Kodwa abantu abangu-90-95%, ukuze bafunde okuthile kuze kube phakade, badinga ukufunda izikhathi eziyishumi futhi bakwenze ngendlela ehlelekile iminyaka eminingana ilandelana. Futhi lokhu kwenza izinhlelo ze-master ekuhlaziyweni kwedatha kube inketho enhle kakhulu yokuthola isisekelo esihle solwazi, ongeke udinge ukuphoxeka ngaso kwinhlolokhono, futhi kuzoba lula kakhulu ukwenza umsebenzi.
UVictor Kantor
I-Chief Data Scientist kwa-MTS
Ungafundela kuphi ukuthola umsebenzi kuDatha Science
Kunezifundo eziningi ezinhle ze-Data Science emakethe futhi ukuthola imfundo yokuqala akuyona inkinga. Kodwa kubalulekile ukuqonda ukugxila kwale mfundo. Uma ikhandidethi esevele enesizinda esiqinile sobuchwepheshe, izifundo ezijulile yizo azidingayo. Umuntu uzokwazi kahle amathuluzi, afike endaweni futhi asheshe ajwayele, ngoba useyazi ukuthi acabange kanjani njengesazi sezibalo, abone inkinga futhi enze izinkinga. Uma kungekho isizinda esinjalo, khona-ke ngemuva kwesifundo uzoba ngumdlali omuhle, kodwa ngamathuba alinganiselwe okukhula.
Uma ubhekene nomsebenzi wesikhashana wokushintsha ubungcweti noma ukuthola umsebenzi kulokhu okukhethekile, khona-ke ezinye izifundo ezihlelekile zikulungele, ezimfishane futhi ezinikeza ngokushesha isethi encane yamakhono obuchwepheshe ukuze ukwazi ukufanelekela ukuthola iziqu. isikhundla sezinga lokungena kulo mkhakha.
Ivan Yamshchikov
Umqondisi Wezemfundo wohlelo lwe-master master online "Isayensi Yedatha"
Inkinga ngezifundo ukuthi zinikeza ukusheshisa okusheshayo kodwa okuncane. Umuntu undiza ngokoqobo emsebenzini futhi ngokushesha afinyelele ophahleni. Ukungena emsebenzini isikhathi eside, udinga ukubeka isisekelo esihle ngokushesha ngendlela yohlelo lwesikhathi eside, isibonelo, i-master degree.
Imfundo ephakeme ifanelekile uma uqonda ukuthi lo mkhakha unentshisekelo kukho isikhathi eside. Awuzimisele ngokufika emsebenzini ngokushesha. Futhi awufuni ukuba nophahla lomsebenzi; futhi awufuni ukubhekana nenkinga yokuntula ulwazi, amakhono, ukuntula ukuqonda kwe-ecosystem ejwayelekile ngosizo okwakhiwa ngalo imikhiqizo emisha. Ngalokhu, udinga imfundo ephakeme, engagcini nje ngokudala isethi edingekayo yamakhono ezobuchwepheshe, kodwa futhi ihlele ukucabanga kwakho ngendlela ehlukile futhi ikusize wenze umbono othile womsebenzi wakho isikhathi eside.
Ivan Yamshchikov
Umqondisi Wezemfundo wohlelo lwe-master master online "Isayensi Yedatha"
Ukungabikho kophahla lomsebenzi kuyinzuzo enkulu yohlelo lwe-master's. Eminyakeni emibili, uchwepheshe uthola isisekelo esinamandla setiyori. Yile ndlela isemester yokuqala ohlelweni lwe-Data Science e-NUST MISIS ebukeka ngayo:
- Isingeniso Sesayensi Yedatha. 2 amaviki.
- Okuyisisekelo kokuhlaziywa kwedatha. Ukucubungula idatha. 2 amaviki
- Ukufunda ngomshini. Ukucubungula idatha. 2 amaviki
- EDA. Intelligence idatha analysis. 3 amaviki
- Ama-algorithms wokufunda komshini ayisisekelo. I-Ch1 + Ch2 (amaviki angu-6)
Ngesikhathi esifanayo, ungakwazi ngesikhathi esifanayo ukuthola isipiliyoni esiwusizo emsebenzini. Akukho okuvimba ukuthi uthole isikhundla esincane ngokushesha nje lapho umfundi esefunde kahle amathuluzi adingekayo. Kodwa, ngokungafani nomuntu othweswe iziqu, iziqu ze-masters azimisi izifundo zakhe lapho, kodwa ziyaqhubeka nokujula kulo msebenzi. Ngokuzayo, lokhu kukuvumela ukuthi uthuthuke ku-Data Science ngaphandle kwemikhawulo.
Kuwebhusayithi yeNyuvesi Yesayensi Nobuchwepheshe "MISiS" kulabo abafuna ukusebenza kwi-Data Science. Abamele i-NUST MISIS, i-SkillFactory, i-HeadHunter, i-Facebook, i-Mail.ru Group ne-Yandex, ngizokutshela ngezinto ezibaluleke kakhulu:
- "Uyithola kanjani indawo yakho ku-Data Science?",
- "Kungenzeka yini ukuba usosayensi wedatha kusukela ekuqaleni?",
- "Ingabe isidingo sososayensi bedatha sisekhona eminyakeni emi-2-5?"
- "Iziphi izinkinga ososayensi bedatha abasebenza kuzo?"
- "Ungawakha kanjani umsebenzi ku-Data Science?"
Ukuqeqeshwa okuku-inthanethi, idiploma yemfundo yomphakathi. kwamukelwe kuze I-10 Aug.
Source: www.habr.com