ʻO kēia ʻatikala e pili ana i nā kula kiʻekiʻe 10 no ka ʻepekema data ma USA, akā e kōkua pū kekahi iā ʻoe e aʻo i ke ʻano o ka ʻepekema data. ʻO ka ʻepekema ʻikepili kahi kahua multidisciplinary e hoʻohana ana i nā ʻano ʻepekema, nā kaʻina hana, nā algorithms, a me nā ʻōnaehana e unuhi i ka ʻike a me nā ʻike mai ka ʻikepili i kūkulu ʻia a i hoʻonohonoho ʻole ʻia.
Loaʻa iā ia ka manaʻo like me ka mining data a me ka ʻikepili nui.
Hoʻohana ka poʻe ʻepekema ʻikepili i ka ʻenehana ikaika loa, nā ʻōnaehana hoʻonohonoho ikaika loa, a me nā algorithm e hoʻoponopono ai i nā pilikia.
He māla wela kēia e ulu nei no nā makahiki, a ke hoʻonui mau nei nā manawa. Me ka nui o nā kulanui e hāʻawi ana i nā papa e pili ana i ka ʻepekema data a me ke aʻo ʻana i ka mīkini pū kekahi hoʻokahi makahiki ke kēkelē laepua ma Kanada, hiki ke paʻakikī ke ʻike i kahi e hoʻomaka ai. Eia nō naʻe, ua koho mākou i nā kula kiʻekiʻe he 10 no ka ʻepekema ʻikepili ma USA.
E hoʻomaka kākou i kēia ʻatikala ma nā kula kiʻekiʻe he 10 no ka ʻIke ʻIke ʻIke ma ʻAmelika Hui Pū ʻIa me kahi wehewehe pōkole o ka ʻIke ʻIke.
Table of Contents
He aha ka ʻepekema ʻikepili?
ʻEpekema ʻIkepili he kahua multidisciplinary e hoʻohana ana i nā ʻano ʻepekema, nā kaʻina hana, nā algorithms, a me nā ʻōnaehana e unuhi i ka ʻike a me nā ʻike mai nā ʻikepili i kūkulu ʻole ʻia.
He kanaka ʻepekema ʻikepili ke kuleana no ka hōʻiliʻili, ka nānā ʻana, a me ka unuhi ʻana i ka nui o ka ʻikepili.
Nā kumu e aʻo ai i ka ʻEpekema ʻIkepili
Inā kānalua ʻoe inā e aʻo a ʻaʻole ʻoe e aʻo i ka ʻepekema data, e hōʻoiaʻiʻo kēia mau kumu iā ʻoe i ke koho ʻana i ka ʻepekema data ma ke ʻano he kahua aʻo.
- Ka hopena maikaʻi ma ka honua
Ma ke ʻano he ʻepekema data, e loaʻa iā ʻoe ka manawa e hana pū me nā ʻāpana e hāʻawi i ka honua, no ka laʻana, mālama olakino.
Ma 2013, ua hoʻokumu ʻia ka 'Data Science for Social Good' no ka hoʻoulu ʻana i ka hoʻohana ʻana i ka ʻepekema data no ka hopena pilikanaka maikaʻi.
- Uku Uku Kiekie
ʻO nā ʻepekema data a me nā ʻoihana ʻepekema ʻikepili ʻē aʻe e loaʻa kālā loa. ʻO ka ʻoiaʻiʻo, ʻike pinepine ʻia kahi ʻepekema data ma waena o nā hana ʻenehana maikaʻi loa.
Wahi a Glassdoor.com, ʻo ka uku kiʻekiʻe loa no ka Data Scientist ma US he $166,855 i kēlā me kēia makahiki.
- Hana ma nā ʻāpana like ʻole
Hiki i nā ʻepekema ʻikepili ke loaʻa ka hana ma nā ʻāpana āpau mai ka mālama olakino a hiki i ka lāʻau lapaʻau, logistic, a me nā ʻoihana kaʻa.
- E hoʻomohala i kekahi mau mākau
Pono nā ʻepekema ʻikepili i kekahi mau mākau e like me nā mākau analytic, ʻike maikaʻi o ka makemakika a me nā helu, nā polokalamu a me nā mea ʻē aʻe, e hana maikaʻi i ka ʻoihana IT. Hiki i ke aʻo ʻana i ka ʻepekema data ke kōkua iā ʻoe e hoʻomohala i kēia mau mākau.
Inā ʻoe e noʻonoʻo ana e komo i ka ʻepekema data a i ʻole e ʻimi e hoʻonui i kāu hoʻonaʻauao, eia ka papa inoa o nā kula kiʻekiʻe 10 no ka ʻepekema data ma USA.
ʻO 10 mau kula kiʻekiʻe no ka ʻepekema Data ma USA
Aia ma lalo kahi papa inoa o nā kulanui maikaʻi loa no ka ʻepekema Data ma United States:
1. Ke Kulanuiʻo Stanford
2. Harvard University
3. Ke Kulanui o Kaleponi, Berkeley
4. Johns Hopkins University
5. Ke Kulanuiʻo Carnegie Mellon
6. Ke Kulaʻo Technologyʻo Massachusetts
7. Columbia University
8. Ke Kulanui o New York (NYU)
9. Ke Kulanui o Illinois Urbana-Champaign (UIUC)
10. Ke Kulanui o Michigan Ann Arbor (UMich).
10 ʻO nā Kulanui maikaʻi loa no ka ʻepekema ʻikepili ma ʻAmelika Hui Pū ʻIa āu e aloha ai
1. Ke Kulanuiʻo Stanford
Hāʻawi ke Kulanui i nā kekelē ʻepekema data ma ke kula haʻahaʻa a me ke kula puka.
Pono nā haumāna e noʻonoʻo ana i kēia mau koho he mea maʻamau ke kumukūʻai o kēia mau papahana a pono paha e noho ma ka pā kula no ka lōʻihi o ka pau ʻana o ka papahana.
ʻIke ʻepekema ʻikepili ma ke Kulanui ʻo Stanford i ka hoʻohana ʻana i nā ʻano ʻepekema, nā kaʻina hana, nā algorithms, a me nā ʻōnaehana e unuhi i ka ʻike a me nā ʻike mai ka ʻikepili i kūkulu ʻia a i hoʻonohonoho ʻole ʻia. Aʻo ʻia nā haumāna i nā papa e like me:
- Hoʻopili data
- Machine palapala he
- Nuiʻike.
- Ka nānā ʻana a me ka hoʻohālike wānana
- Hōʻike manaʻo
- pūnaewele
- Hoolaha.
2. Harvard University
He kahua hou ka ʻIkepili ʻIkepili me kāna mau noi ma nā kahua he nui.
He ʻāpana ia o ka hoʻoholo ʻana i ka ʻoihana, kōkua ia i ka hoʻoponopono ʻana i nā hewa a hiki ke hoʻohana ʻia e hoʻonui i ka pono o nā ʻōnaehana mālama olakino. He kahua multi-disciplinary e hoʻohana ana i nā algorithms, nā ʻano, a me nā ʻōnaehana e unuhi i ka ʻike mai ka ʻikepili.
Ua ʻike ʻia ka ʻepekema ʻikepili ma ke ʻano he ʻikepili a i ʻole ʻenekinia ʻikepili. ʻO ia kekahi o nā hana koʻikoʻi i kēia ao, hiki iā ia ke kōkua iā ʻoe e loaʻa kālā nui.
Wahi a Indeed.com, ʻo ka uku maʻamau no kahi ʻepekema data ma US he $121,000 me nā pōmaikaʻi. ʻAʻole kēia he mea kupanaha ke kālele nei nā kulanui ma ka ʻāina i ka hoʻololi ʻana i kā lākou mau haʻawina papa, ka hoʻolimalima ʻana i nā kumu hou, a me ka hoʻokaʻawale ʻana i nā kumuwaiwai hou aʻe i nā papahana ʻepekema data. A ʻaʻole nele ʻo Harvard University i kēia.
Hāʻawi ke kulanui i ka ʻIke ʻIkepili ma ke ʻano he wahi aʻo i loko o ke kula ʻo Harvard John A. Paulson School of Engineering and Applied Science.
Ma ʻaneʻi, noi nā haumāna i koho ʻia ma o GSAS.
ʻAʻohe kumu kūpono no ka poʻe noi i nā papahana o kā lākou haku ma ka ʻepekema data. Eia nō naʻe, pono e loaʻa i nā mea noi kūleʻa ka ʻike kūpono i ka Computer Science, Mathematics, and Statistics, me ka mākaukau ma ka liʻiliʻi o hoʻokahi ʻōlelo papahana a me ka ʻike o ka helu helu, linear algebra, a me ka helu helu helu.
3. Ke Kulanui o Kaleponi, Berkeley
ʻO kēia Kulanui kekahi o nā kula ʻepekema ʻikepili kiʻekiʻe ma USA no ka mea ʻaʻole wale lākou i loaʻa kekahi o nā lālā kumu maikaʻi loa a me nā keʻena lab, hana pū lākou me ka ʻoihana e hoʻomohala i nā ʻenehana hou e hoʻoponopono i nā pilikia o ka honua.
ʻO ka hopena, ʻo kā lākou papahana haʻahaʻa haʻahaʻa e pili ana i nā internships a i ʻole nā koho hoʻonaʻauao koʻikoʻi e hāʻawi i ka ʻike lima lima nui e hana pū ana me nā ʻoihana alakaʻi ma nā ʻano pilikia e kū nei i ke kaiāulu ʻoihana.
4. Johns Hopkins University
Loaʻa nā degere ʻepekema ʻikepili i ka lōʻihi, ka laulā a me ka nānā ʻana ma ke Kulanui ʻo Johns Hopkins.
Hāʻawi lākou i nā pae kiʻekiʻe kiʻekiʻe i kūpono no ka poʻe ʻoihana e manaʻo nei e hoʻololi i kahi ala ʻoihana ʻepekema data. Hāʻawi pū ʻo Johns Hopkins i nā papahana lae pua i hoʻolālā ʻia e kōkua i nā haumāna e hoʻomaka i kahi ʻoihana ma ke ʻano he ʻepekema data a hoʻomākaukau paha iā lākou no nā haʻawina puka.
Aia kekahi mau papahana ʻē aʻe i nā papa hana pūnaewele i hoʻolālā ʻia e aʻo iā ʻoe i nā mākau loea e pono ai ʻoe e wāwahi i ke kula. ʻO ka hapa maikaʻi loa, ua hoʻomohala ʻia kā lākou papa me ʻoe i ka noʻonoʻo, noʻonoʻo lākou i kāu:
- Kaila aʻo
- Pahuhopu oihana
- Kūlana kālā.
5. Ke Kulanuiʻo Carnegie Mellon
ʻO kekahi o nā kumu i ʻike ʻia ai ʻo Carnegie Mellon no kāna mau papahana hoʻonaʻauao ma nā kahua o ka ʻepekema kamepiula a me ka ʻenekinia. He 12,963 mau haumāna i kākau inoa ʻia mai loko mai o ke kulanui he 2,600 ka haku a me ka Ph.D. nā haumāna.
Hāʻawi ʻo Carnegie Mellon University i nā papahana ʻepekema data no ke kula haʻahaʻa a me ke kula kiʻekiʻe i hāʻawi ʻia ma ka manawa piha a hapa manawa paha.
Loaʻa maʻamau ke Kulanui ʻo Carnegie Mellon i ke kālā kālā a me ke kākoʻo mai nā keʻena aupuni a me nā hui pilikino e ʻike nei i ka ulu nui ʻana o ka ʻepekema data i ka hoʻokele waiwai o kēia mau lā.
6. Ke Kulaʻo Technologyʻo Massachusetts
ʻIke ʻia ʻo Massachusetts Institute of Technology (MIT) no kāna mau ʻimi noiʻi ʻepekema a ʻo ia kekahi o nā kula kiʻekiʻe no ka ʻepekema data ma ka honua.
He hale noiʻi nui ʻo MIT me ka nui o nā haumāna puka a me nā haumāna ʻoihana. Mai ka makahiki 1929, ua hāʻawi ka New England Association of Schools and Colleges i kēia ʻae ʻana o ke kulanui.
Mālama ʻia ka papahana haʻahaʻa makahiki ʻehā makahiki piha i kahi kaulike ma waena o ka ʻoihana a me ka ʻoihana a me ka ʻepekema nui a ua kapa ʻia ʻo "koho loa" e US News a me World Report, e ʻae wale ana i ka 4.1 pakeneka o nā mea noi i ka pōʻai komo 2020-2021. Hāʻawi nā kula ʻelima o MIT i 44 mau kula haʻahaʻa haʻahaʻa, e lilo ia i mea nui loa ma ka honua.
7. Columbia University
ʻO ka papahana Master of Science in Data Science ma Columbia University he papahana interdisciplinary e hoʻohui i nā helu helu, ka ʻikepili ʻikepili, a me ke aʻo ʻana i ka mīkini me nā noi i nā kikowaena like ʻole.
ʻO ia kekahi o ka Easiest Online Masters Degree Programs ma US.
ʻO kēia kula kahi kulanui noiʻi ʻo Ivy League pilikino ma New York City.
ʻO Columbia University, i hoʻokumu ʻia i ka makahiki 1754 ma ke ʻano he King's College ma ke kahua o Trinity Church ma Manhattan, ʻo ia ke kula kahiko loa o ke kula kiʻekiʻe ma New York a ʻo ka ʻelima mau makahiki ma United States.
8. Ke Kulanui o New York (NYU)
Hāʻawi ka NYU Center for Data Science i kahi palapala puka puka ma ka papahana Data Science. ʻAʻole ia he kekelē kūʻokoʻa akā hiki ke hoʻohui ʻia me nā degere ʻē aʻe.
Hāʻawi kēia papahana palapala i nā haumāna i kahi kumu ikaika i nā kumuhana ʻenehana koʻikoʻi e pili ana i ka ʻepekema data.
Ma waho aʻe o kahi kahua paʻa i ka ʻepekema kamepiula a me ka ʻenehana, pono ʻoe e manaʻo i nā papahana e hoʻokomo i ka papa hana ma ka helu helu, makemakika, a me ka ʻenekini uila a me ka ʻike ʻana i nā kumu ʻoihana.
Ma NYU, aia ka papahana ʻepekema ʻikepili i nā mākau koi kiʻekiʻe e pono ai e hana me ka ʻikepili. ʻOiai ua hoʻomaka kekahi mau kula e hāʻawi i nā kēkelē laepua i ka ʻepekema data, pili ʻo NYU i kā lākou papahana maʻamau akā hāʻawi i nā papa a me nā palapala hōʻoia e aʻo i nā haumāna pehea e hoʻoponopono ai i nā pūʻulu ʻikepili nui.
Manaʻo lākou he mea nui ka ʻepekema Data o ka hoʻonaʻauao 21st-century.
Hiki i nā haumāna āpau ke pōmaikaʻi mai ke kaʻina hana o ke aʻo ʻana e loiloi a hoʻomaopopo i ka ʻikepili, ʻoiai inā ʻaʻole lākou e ʻimi i nā ʻoihana ma ke ʻano he ʻepekema data.
ʻO ia ke kumu e hakakā nei lākou e hoʻokomo i ka ʻepekema data i kā lākou curricula.
9. Ke Kulanui o Illinois Urbana-Champaign (UIUC)
ʻO ke Kulanui o Illinois Urbana-Champaign (UIUC) ke poʻo o ka noiʻi ʻana i ke aʻo ʻana i ka mīkini, ka ʻimi ʻikepili, ka ʻike ʻike ʻikepili, a me nā ʻōnaehana ʻikepili nui mai ka makahiki 1960.
I kēia lā hāʻawi lākou i kekahi o nā papahana lae pua maikaʻi loa ma Data Science ma ka ʻāina. He pilina paʻa ko ke Keʻena ʻEpekema ʻEpekema o UIUC me nā keʻena ʻē aʻe e like me Statistics and Engineering a hāʻawi i nā papahana puka puka no nā haumāna e ʻimi nei i nā haʻawina holomua ma ka ʻIke ʻIke.
10. Ke Kulanui o Michigan Ann Arbor (UMich)
ʻO ka ʻepekema ʻikepili kekahi o nā kahua kaulana loa ma ʻAmelika Hui Pū ʻIa.
Ke koi nui nei nā haumāna a me nā ʻoihana loea i ka ʻepekema data, a mahalo nui ʻia ko lākou mākaukau e nā hui a puni ka honua.
Hoʻohana ka ʻepekema data maikaʻi i ka coding ikaika a me nā mākau makemakika e hoʻoponopono i nā pilikia o ka honua maoli. No ka hoʻomohala ʻana i nā mākau e pono ai, huli nā mea he nui i nā kulanui maikaʻi loa ma ʻAmelika no ka hoʻonaʻauao ʻepekema data a ʻo UMich kekahi o lākou.
I kēia mau lā, ua wehe ʻo UMich i kahi kikowaena interdisciplinary hou i kapa ʻia ʻo MCubed e kālele ana i ka noiʻi ʻana i ka ʻEpekema Data mai nā kihi he nui e pili ana i ka mālama olakino, cybersecurity, hoʻonaʻauao, halihali, a me ka ʻepekema pilikanaka.
Hāʻawi ʻo UMich i nā papahana lae a me nā papahana puka a me kahi papahana kekelē Master online a me nā papahana hoʻonaʻauao hoʻokō i aʻo ʻia e nā loea ʻoihana.
Nā nīnau i nīnau pinepineʻia
Ma ʻAmelika Hui Pū ʻIa, ʻo wai ka mokuʻāina ʻoi aku ka maikaʻi no ka ʻepekema data?
Wahi a kā mākou ʻike, ʻo Wakinekona ka mokuʻāina kiʻekiʻe loa no ka Data Scientists, me Kaleponi a me Wakinekona ka uku uku waena. He $119,916 ka uku ma waena o Data Scientists ma Wakinekona, me Kaleponi ka uku median kiʻekiʻe loa o nā mokuʻāina he 50.
Ke koi nui nei ka ʻepekema data ma ʻAmelika Hui Pū ʻIa?
Wahi a ka US Bureau of Labor Statistics, e hoʻonui ʻia ka noi no nā ʻepekema data ʻike a me ka ʻike e 27.9% e 2026, e hoʻonui ana i ka hana o 27.9%.
No ke aha ʻo ʻAmelika ka ʻāina kiʻekiʻe loa no ka ʻepekema data?
ʻO ka pōmaikaʻi nui o ka loaʻa ʻana o kahi MS ma ʻAmelika Hui Pū ʻIa e loaʻa iā ʻoe ka nui o nā koho hana ma ka ʻāina. I ka ʻepekema ʻikepili a me nā ʻenehana pili e like me ke aʻo ʻana i ka mīkini, ka naʻauao hana, ke aʻo hohonu, a me IoT, ʻo ʻAmelika kekahi o nā mākeke ʻoi loa a me nā mea hou.
He aha nā hana e pono ai iaʻu e lilo i ʻepekema data?
ʻO ka loaʻa ʻana o ke kēkelē laepua ma IT, ʻepekema lolouila, makemakika, ʻoihana, a i ʻole kekahi aʻo kūpono ʻē aʻe kekahi o nā ʻanuʻu maʻamau ʻekolu e lilo i ʻepekema data. E loaʻa i ka ʻike ma ke kahua āu e makemake ai e hana, e like me ka mālama ola kino, physics, a i ʻole ʻoihana, ma ka loaʻa ʻana o ke kēkelē laepua ma ka ʻepekema data a i ʻole ke aʻo like.
He aha nā kumuhana ʻepekema data ma ʻAmelika Hui Pū ʻIa?
No ka hoʻoponopono ʻana i nā pilikia paʻakikī, loaʻa nā papahana ʻepekema data i nā papa ma nā wahi haʻawina e like me ka helu, makemakika, a me ka ʻepekema kamepiula.
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Panina
He mea hoʻohauʻoli, loaʻa kālā, a hoʻopōmaikaʻi ke kahua ʻepekema data, no laila, ʻaʻole ia he mea kupanaha ke koi nui ʻia nā degere ʻepekema data.
Eia nō naʻe, inā ʻoe e noʻonoʻo ana i kahi kekelē i ka ʻepekema data, ʻo kēia papa inoa o nā kula maikaʻi loa no ka ʻIke ʻIkepili ma United States e kōkua iā ʻoe e ʻimi i kahi kula maikaʻi loa a hiki ke hāʻawi iā ʻoe i nā internships waiwai a me nā manawa ʻike hana.
Mai poina e hui pū me kā mākou kaiāulu a makemake wau iā ʻoe i ka maikaʻi a pau ke nānā ʻoe i kekahi o nā ʻO nā kulanui pūnaewele maikaʻi loa ma USA e kiʻi i kāu kēkelē.