ʻO ka lawelawe noiʻi hh.ru me ka MADE Big Data Academy mai Mail.ru i hōʻuluʻulu i kahi kiʻi o kahi loea Data Science ma Rusia. Ma hope o ke aʻo ʻana i 8 tausani mau mea hou o nā ʻepekema data Lūkini a me 5,5 tausani mau hakahaka hana, ua ʻike mākou i kahi e noho ai a hana ai nā loea Data Science, pehea ko lākou mau makahiki, he aha ke kulanui a lākou i puka ai, he aha nā ʻōlelo papahana a lākou e ʻōlelo ai a me ka nui o nā kekelē kula. loaʻa.
Koi
Mai ka makahiki 2015, ke ulu mau nei ka pono o nā loea. I ka makahiki 2018, ua hoʻonui ʻia ka nui o nā hakahaka ma lalo o ke poʻomanaʻo Data Scientist i 7 mau manawa i hoʻohālikelike ʻia me 2015, a ua hoʻonui ʻia nā hakahaka me nā huaʻōlelo Machine Learning Specialist i 5 mau manawa. Ma ka manawa like, i ka hapa mua o 2019, ʻo ka noi no nā loea ʻepekema ʻikepili i loaʻa i ka 65% o ka noi no ka 2018 holoʻokoʻa.
Hōʻikeʻike
ʻO ka hapa nui o nā kāne e hana i ka ʻoihana; ma waena o nā ʻepekema data ʻo kā lākou mahele he 81%. ʻOi aku ma mua o ka hapalua o ka poʻe e ʻimi nei i nā hana i ka ʻikepili ʻikepili he poʻe loea i ka makahiki 25-34 makahiki. He kakaikahi wale na wahine ma ka oihana - 19%. Akā, he mea hoihoi i ka hōʻike ʻana o nā kaikamahine ʻōpio i ka hoihoi hou aʻe i ka ʻIke ʻIke. Ma waena o nā wahine i hoʻopuka i kā lākou resume, aneane 40% he mau kaikamahine 18-24 makahiki.
Akā he liʻiliʻi ka hoʻomaka ʻana o nā mea noi kahiko - ʻo 3% wale nō o nā ʻepekema data ma luna o 45 mau makahiki. Wahi a nā manaʻo koho, ma muli paha o nā kumu he nui: ʻo ka mea mua, he kakaikahi nā ʻelele kahiko ma Data Science, a ʻo ka lua, ʻo nā mea noi me ka ʻike hana nui ʻaʻole hiki ke kau i kā lākou resume ma nā kumuwaiwai hulina nui a loaʻa pinepine ka hana ma o nā ʻōlelo paipai. .
Wehewehe
ʻOi aku ma mua o ka hapalua o nā hakahaka (60%) a me nā mea noi (64%) aia ma Moscow. Eia kekahi, koi ʻia nā poʻe loea i ke kahua o ka ʻikepili ʻikepili ma St. Petersburg, ma nā wahi o Novosibirsk a me Sverdlovsk a ma ka Repubalika o Tatarstan.
ho okumu
ʻO 9 o 10 mau ʻoihana e ʻimi nei i nā hana ma ka ʻikepili ʻikepili he kekelē koleke. Ma waena o ka poʻe i puka i ke kulanui, aia ka hapa nui o ka poʻe e hoʻomau i ka hoʻomohala ʻana i ka ʻepekema a ua hoʻokō ʻia e loaʻa kahi kekelē hoʻonaʻauao: 8% loaʻa kahi moho o ka ʻepekema ʻepekema, 1% loaʻa ke kauka o ka ʻepekema.
ʻO ka hapa nui o nā loea e ʻimi nei i ka hana ma ke kahua o ka ʻIkepili ʻIkepili i aʻo ʻia ma kekahi o kēia mau kula: MSTU i kapa ʻia ma hope o N.E. Bauman, Ke Kulanui Mokuʻāina ʻo Moscow. M.V. Lomonosov, MIPT, Higher School of Economics, St. Petersburg State University, St. Petersburg Polytechnic University, Financial University ma lalo o ke Aupuni o ka Russian Federation, NSU, KFU. Ua kūpaʻa pū ka poʻe hana i kēia mau kulanui.
Ua ʻike ʻo 43% o nā loea ʻepekema data ma kahi o ka hoʻonaʻauao kiʻekiʻe ua loaʻa iā lākou hoʻokahi hoʻonaʻauao hou. ʻO nā papa pūnaewele maʻamau i ʻōlelo ʻia ma ka hoʻomaka ʻana he aʻo mīkini a me ka ʻikepili ʻikepili ma Coursera.
Nā mākau kaulana
Ma waena o ka papa inoa o nā ʻepekema ʻikepili koʻikoʻi ma kā lākou hoʻomau ʻana, ʻo Python (74%), SQL (45%), Git (25%), Data Analysis (24%) a me Data Mining (22%). ʻO kēlā poʻe loea e kākau e pili ana i ko lākou ʻike i ka aʻo ʻana i ka mīkini i kā lākou hoʻomaka ʻana e haʻi pū i ka mākaukau ma Linux a me C++. ʻO nā ʻōlelo papahana kaulana loa i waena o nā loea ʻepekema Data: Python, C++, Java, C#, JavaScript.
Pehea lākou e hana ai
Manaʻo ka poʻe paʻahana e hana nā loea ʻepekema Data ma kahi keʻena piha. ʻO 86% o nā hakahaka i kau ʻia he manawa piha, 9% maʻalahi, a ʻo 5% wale nō o nā hakahaka e hāʻawi i ka hana mamao.
I ka hoʻomākaukau ʻana i ke aʻo ʻana, ua hoʻohana mākou i ka ʻikepili e pili ana i ka ulu ʻana o nā hakahaka, nā koi uku o nā mea hana a me ka ʻike o nā mea noi, i kau ʻia ma hh.ru i ka hapa mua o 1, a hāʻawi ʻia e ka lawelawe noiʻi o ka hui HeadHunter.
Source: www.habr.com