Ke ʻimi nei ʻoe i ka palapala hōʻoia hōʻoia ʻikepili manuahi maikaʻi loa? inā ʻoe e hana, a laila ʻo ka hōʻoia hōʻoia ʻikepili data 10 a mākou i helu ai i kēia ʻatikala ka mea āu e pono ai.
ʻO ka hōʻoia hōʻoia ʻikepili kahi ala maikaʻi loa e hoʻomaikaʻi ai i kāu holomua, hoʻonui i kāu ʻoihana, a loaʻa i kekahi mau kālā keu. ʻO ka hapa maikaʻi loa? ʻAʻole pono ʻoe e uku no ka palapala hōʻoia.
Nui nā kumuwaiwai manuahi weliweli i loaʻa ma ka pūnaewele e kōkua iā ʻoe e loaʻa nā mākau a me ka ʻike ma ke kahua o ka ʻikepili ʻikepili; hāʻawi kekahi o lākou i ka palapala hōʻoia.
ʻO ka ʻikepili ʻikepili ke kaʻina o ka nānā ʻana i nā pūʻulu ʻikepili i mea e huki ai i nā manaʻo e pili ana i ka ʻike i loko o lākou, me ke kōkua ʻana o nā ʻōnaehana kūikawā a me nā polokalamu.
Hoʻohana nui ʻia nā ʻenehana a me nā ʻenehana ʻikepili i nā ʻoihana kalepa e hiki ai i nā hui ke hana i nā hoʻoholo ʻoihana ʻoi aku ka ʻike a e nā ʻepekema a me nā mea noiʻi e hōʻoia a hōʻole i nā kumu hoʻohālike ʻepekema, nā manaʻo, a me nā kuhiakau.
Hāʻawi kēia ʻatikala i kahi papa inoa o nā palapala hōʻoia manuahi 10 kiʻekiʻe e hiki ai iā ʻoe ke hoʻohana e hoʻomaikaʻi i kou akamai a me kāu ʻoihana. Ua hoʻokomo mākou i nā papa pūnaewele ʻelua a me kēlā me kēia nā papahana hōʻoia pūnaewele. Akā, ma mua o kou lele ʻana i laila, e aʻo kākou i kekahi mau mea.
Table of Contents
He aha ka ʻokoʻa ma waena o kahi papa ʻikepili ʻikepili manuahi a uku ʻia?
No laila, ua hoʻokumu mākou i ka ʻikepili ʻikepili. Pehea ʻoe e ʻike hou ai?
He ala maikaʻi loa ka lawe ʻana i kahi papa ʻikepili ʻikepili manuahi e hoʻāʻo ai i nā wai a hoʻoholo inā makemake ʻoe e hele hohonu. Eia nō naʻe, aia kekahi mau ʻokoʻa koʻikoʻi ma waena o nā papa manuahi a uku ʻia āu e ʻike pono ai.
Aia ma lalo iho nā ʻokoʻa ma waena o kahi papa ʻikepili manuahi a uku ʻia:
1. Ka pae o ka kiko'ī
ʻO ka pahuhopu o ka papa manuahi ka mea maʻamau ka hāʻawi ʻana i kahi hiʻohiʻona kiʻekiʻe e loiloi inā pono ke uku ʻia kahi papahana piha. He kūpono nā papa pōkole no ka loaʻa ʻana o kahi ʻike ākea o kahi kumuhana.
I kēia manawa, kahi papahana piha (ma ka liʻiliʻi loa, kahi maikaʻi!) E hāʻawi iā ʻoe i nā mea pono āpau.
2. Ka lōʻihi o ka papa
ʻOi aku ka pōkole o nā papa hōʻoia hōʻoia ʻikepili manuahi (maʻa mau, akā ʻaʻole i nā manawa a pau) no ka mea ua hana ʻia lākou ma ke ʻano he "teaser trailer."
Hiki ke lōʻihi mai kekahi mau hola a i kekahi mau lā o ke aʻo ʻana. ʻO nā mea ʻē aʻe ma mua o kēlā, a ua komo ʻoe i ke aupuni o nā polokalamu uku. Ma muli o ka paʻakikī o ke kumuhana, hiki ke lawe ʻia nā papa ma nā wahi a pau mai hoʻokahi pule a i nā mahina he nui e pau ai.
3. Ka pae o ke kākoʻo
ʻO ke aʻo alakaʻi ponoʻī kahi mea nui o nā papa manuahi. Eia nō naʻe, e hāʻawi maʻamau nā papahana ʻikepili piha i ke kākoʻo alakaʻi ma ke ʻano o ke kumu aʻoaʻo a i ʻole ke kumu aʻoaʻo, a me ke kōkua me ka ʻimi ʻoihana-no ka laʻana, ka hoʻomākaukau ʻana i kahi CV analyst a me ka hoʻomohala ʻana i kahi kōpili ʻikepili. ʻO kekahi mau papa pipiʻi a me nā kahua hoʻomoana e hōʻoiaʻiʻo i ka hana.
5. Ka pae ʻike
Kuhi ʻia nā papa hōʻoia hōʻoia ʻikepili manuahi i ka poʻe me ka ʻike ʻole. He mea maikaʻi kēia no ke aʻo ʻana i nā kumu.
Eia naʻe, ke mākaukau ʻoe e holomua, pono ʻoe e hana hou i nā haʻawina home! ʻOi aku ka paʻakikī o nā polokalamu i uku ʻia, akā ma hope o ka hoʻopau ʻana i hoʻokahi, e loaʻa iā ʻoe nā mana āpau (a me nā hōʻoia) pono ʻoe e kapa iā ʻoe iho he mea loiloi ʻikepili mākaukau-a ʻaʻole ia he mea hiki ke hāʻawi i kahi papa manuahi.
Ka papa inoa o ka hōʻoia hōʻoia ʻikepili manuahi maikaʻi loa
Aia ma lalo kahi papa inoa o ka hōʻoia hōʻoia ʻikepili manuahi maikaʻi loa:
- Google Analytics Academy - Google Analytics no nā mea hoʻomaka
- IBM Data Science Professional Certificate
- ʻO ka ʻikepili ʻikepili pōkole (CareerFoundry)
- ʻEpekema ʻIkepili no kēlā me kēia kanaka (Datacamp)
- E aʻo i ke code no ka ʻikepili ʻikepili (OpenLearn)
- Nā Haʻawina ʻEpekema Pūnaewele (Ke Kulanui ʻo Harvard)
- Nā Haʻawina ʻEpekema ʻIkepili (Dataquest)
- ʻO ka haʻi moʻolelo no ka hopena (edX)
- Nā Papa ʻEpekema ʻIkepili (Alison)
- Ka nānā ʻana a me ka nānā ʻana i ka ʻikepili me Excel (edX).
10 ʻOi aku ka maikaʻi o ka hōʻoia ʻana i ka ʻikepili ʻikepili no ka poʻe hoʻomaka, waena, a me nā ʻoihana
1. Google Analytics Academy - Google Analytics no nā mea hoʻomaka
ʻO Google Analytics kahi lawelawe Google manuahi e nānā i ka ʻikepili ma kāu pūnaewele.
He mea maikaʻi loa ka ʻike i hāʻawi ʻia e Google Analytics i ka hoʻoholo ʻana i ke ʻano o ka hana ʻana o ka poʻe me kāu pūnaewele.
Hāʻawi ia iā ʻoe i ka ʻike e pili ana i ka hana a nā mea hoʻohana ma ka pūnaewele, e like me nā ʻaoʻao a lākou i kipa aku ai a pehea ka lōʻihi, kahi i hele mai ai lākou (kahi ʻāina), a pēlā aku.
Hiki iā ʻoe ke koho wikiwiki i kāu pūnaewele me ka hoʻohana ʻana i kēia ʻike e hāʻawi i kahi ʻike mea hoʻohana ʻoi aku ka maikaʻi.
ʻO kekahi o nā hōʻailona kaulana loa ma waena o nā ʻoihana kūʻai digital ʻo ia ka palapala hōʻoia ʻo Digital Analytics Fundamentals. Ke aʻo nei kēia haʻawina i nā kumu kumu o ka ʻikepili kikohoʻe e pili ana i nā ala kūʻai like ʻole.
Pono ʻoe e hoʻopau i kahi papa no ka loaʻa ʻana o kahi palapala hōʻoia ʻikepili manuahi. Inā ʻoe he hoʻomaka, waena, a i ʻole ka mea pāʻani kiʻekiʻe, e ʻike ʻoe i kahi papa no kāu pae.
2. IBM Data Science Professional Certificate
ʻO ka IBM Data Science Professional Certificate he papahana papa pūnaewele i hāʻawi ʻia e IBM ma o Coursera e komo pū ana me ʻeiwa mau papa pūnaewele a me nā papahana lima lima e kōkua iā ʻoe e hoʻomohala i kāu mau mākau ʻepekema data. Aia kēia papahana hoʻomaʻamaʻa pūnaewele i nā papa kumu a me nā papa kiʻekiʻe e kōkua iā ʻoe i ka lilo ʻana i loea ʻepekema data.
No nā poʻe hoʻomaka e makemake e aʻo i nā lā Analytics, hāʻawi ʻo IBM i kahi papa hōʻoia hōʻoia ʻikepili manuahi. Loaʻa i nā mea komo kahi palapala hōʻoia ma ka pau ʻana o ka papa manuahi.
3. ʻO ka ʻikepili ʻikepili pōkole (CareerFoundry)
Inā makemake ʻoe i ka hoʻolauna wikiwiki ʻana i ka ʻikepili ʻikepili, ka palapala hōʻoia ʻikepili manuahi manuahi a CareerFoundy pōkole maikaʻi loa.
Ke kau inoa ʻoe, hiki iā ʻoe ke komo i nā papa lima lima 15 mau minuke, kēlā me kēia e kālele ana i kahi ʻano ʻokoʻa o ke kaʻina hana ʻikepili. Hāʻawi ka papa iā ʻoe i kahi ʻike ākea o ka ʻikepili ʻikepili a hoʻomākaukau iā ʻoe e hele hohonu i ke kumuhana inā makemake ʻoe.
ʻAʻohe hoʻolimalima huna, ʻaʻole like me ka nui o nā papa ma kā mākou papa inoa, e hana ana kēia i kahi koho haʻahaʻa haʻahaʻa no ka nui o nā novices.
Hoʻopili ka papa i nā mea āpau mai nā ʻano like ʻole o ka ʻikepili ʻikepili i ka loiloi o nā mea hana a me nā mākaukau e pono ai ʻoe e kūkulu inā makemake ʻoe e ʻimi i kahi ʻoihana ma ke kula, a hiki iā ʻoe ke manaʻo e loaʻa ka ʻike lima me nā kumu. o ka ʻikepili ʻikepili.
Inā ʻoluʻolu ʻoe i ka papa pōkole, hāʻawi pū ʻo CareerFoundry i kahi papahana uku piha e lawe iā ʻoe mai ka hoʻomaka ʻana a hiki i ka ʻikepili ʻikepili mākaukau hana, i kākoʻo ʻia e ka CareerFoundry Job Guarantee.
4. ʻEpekema ʻIkepili no kēlā me kēia kanaka (Datacamp)
ʻO DataCamp kahi mea hoʻolako papa no ka loaʻa kālā i loea i ka ʻikepili ʻikepili.
Eia nō naʻe, manuahi kā lākou ʻIke ʻIke ʻIke no kēlā me kēia kanaka ka papa mua (a mokuna '). Hōʻalo ʻo ia i ka jargon ʻenehana a kūpono no ka poʻe hou i ke kumuhana.
Hoʻopili ka papa i kahi kahe hana ʻepekema data maʻamau a me ka wehewehe ʻana i ke ʻano o ka ʻepekema data. Loaʻa kēia i kekahi mau hoʻomaʻamaʻa hoʻomaʻamaʻa maikaʻi loa e kōkua i ka hoʻohālikelike ʻana i ke ʻano o ka hoʻohana ʻana i ka ʻikepili ʻikepili e hoʻoponopono i nā pilikia o ka honua maoli. Eia naʻe, ke pau ʻoe i ka mokuna mua, pono ʻoe e kau inoa i mea e hiki ai ke komo i nā ʻike hou aʻe.
5. E aʻo i ke code no ka ʻikepili ʻikepili (OpenLearn)
ʻO ke kahua OpenLearn, ka mea i hoʻolako ʻia e ka UK's Open University, ua paʻa i nā kumuhana mai ka astrophysics a i ka cybersecurity a, ʻoiaʻiʻo, ka ʻikepili ʻikepili.
Ua kaulana nā papa ma OpenLearn no ko lākou kūlana kiʻekiʻe, a ʻo ka hapa nui o lākou he manuahi. No ke aha ʻaʻole ʻoe e aʻo i ke code ke loaʻa ʻoe i nā kumu kumu?
E aʻo i ka Code for Data Analysis, kahi papa coding manuahi ʻewalu pule i hāʻawi ʻia e OpenLearn, e hāʻawi iā ʻoe i ka ʻike piha o ka papahana kumu a me nā manaʻo ʻikepili ʻikepili, a me ka hiki ke hoʻomohala i nā algorithm analytical maʻalahi i loko o kahi papahana papahana. Hoʻopiliʻia kēia mau mea a pau me nā hana pāʻani a me kahi palapala hoʻopau manuahi i ka hopena. Pōmaikaʻi!
6. Nā Haʻawina ʻEpekema Pūnaewele (Ke Kulanui ʻo Harvard)
Ua makemake paha ʻoe e haʻaheo e pili ana i kāu hoʻonaʻauao Harvard? ʻO kēia kou manawa e ʻālohilohi ai! Loaʻa ka nui o nā papa ʻikepili ʻikepili o Harvard University no ka manuahi ma EdX. E ʻimi i nā kumuhana mai ka hoʻopololei ʻana i ka ʻikepili a hiki i ka regression linear a me ke aʻo ʻana i ka mīkini.
ʻOiai ʻoi aku ka maikaʻi o kēia mau papa i ka poʻe me ka ʻike mua, uhi lākou i kahi ākea o nā kumuhana loea a hele i ka hohonu hohonu ma mua o ka hapa nui. papa manuahi.
ʻO ka hemahema wale nō, ʻo ka hapa nui o lākou e koi ana i ka manawa nui, e like me kekahi mau hola i kēlā me kēia pule no nā pule he nui e kū'ē ana i kahi papa ulia i loko o kekahi mau hola a i ʻole mau lā. Inā makemake ʻoe i kahi palapala hoʻopau, pono ʻoe e uku pū kekahi.
Eia naʻe, inā makemake ʻoe e hoʻomaikaʻi i kāu mau talena, he koho kūpono kēia.
7. Nā Haʻawina ʻEpekema ʻIkepili (Dataquest)
Hāʻawi lākou i kahi ākea o nā lima lima nāʻikeʻepekemaʻikepili a he mea hoʻolako hoʻonaʻauao ʻikepili ʻē aʻe. ʻOiai ua loaʻa i ka Dataquest kahi hiʻohiʻona o ke kau inoa o kēlā me kēia mahina, ʻo kekahi o kāna mau ʻike, e like me nā pilikia hoʻomaʻamaʻa, loaʻa ka manuahi.
Hoʻonohonoho maikaʻi ʻia nā papa e ka ʻoihana a me ke ala akamai (a me ka ʻōlelo hoʻonohonoho), e ʻae iā ʻoe e kālele i kāu aʻo ʻana. Eia nō naʻe, inā makemake ʻoe i ke komo ʻole hoʻolaha a i ʻole kahi palapala hoʻopau, pono ʻoe e uku no ke kau inoa.
8. ʻO ka haʻi moʻolelo no ka hopena (edX)
Inā ʻoluʻolu ʻoe e hana pū me Power BI a me Excel, e aʻo kēia papa iā ʻoe pehea e haku ai i ke akamai o ke kamaʻilio ʻana i nā hopena i huki ʻia mai nā hiʻohiʻona a me ka nānā ʻana me ke ʻano. E hana i nā moʻolelo e hoʻohui i ka waiwai i kāu poʻe hālāwai a loiloi i nā hopena.
Hāʻawi pū nā kumu aʻoaʻo i nā manaʻo no ka hoʻohana ʻana i nā hana maikaʻi loa no ka hoʻolalelale ʻana i kāu mau hōʻike a me ka hoʻomalu ʻana i ka lumi i ka wā e hāʻawi ana iā lākou.
9. Nā Papa ʻEpekema ʻIkepili (Alison)
E ʻike ʻoe i nā ʻano papa diploma a me nā palapala hōʻoia ma kēia pūnaewele e-learning, e kālele ana i nā mea like ʻole o ka ʻepekema data a me nā kumuhana pili.
Inā makemake ʻoe e hoʻomaʻamaʻa iā ʻoe iho me nā huaʻōlelo a me nā manaʻo koʻikoʻi, he koho kūpono nā papahana pae hoʻomaka. No nā poʻe akamai, ʻo nā wahi e like me nā hoʻohālike hoʻomaʻamaʻa, nā hiʻohiʻona, a me ka mining kekahi o nā koho e hele ai.
10. Ka nānā ʻana a me ka nānā ʻana i ka ʻikepili me Excel (edX)
Pono kēia palapala hōʻoia ʻikepili manuahi i ka ʻike mua o ka hiki ʻana o Excel a me ka hana ʻana me nā waihona a i ʻole nā faila kikokikona ma ke ʻano he koi.
Mai laila mai, e alakaʻi nā kumu aʻo iā ʻoe i kahi huakaʻi e loaʻa ai iā ʻoe ka mākaukau i ka lawe ʻana i ka ʻikepili mai nā kumu like ʻole, ka hui ʻana, a me ka hana ʻana i nā hiʻohiʻona.
ʻO nā haʻiʻōlelo ma lalo nei e hoʻonui i nā mea ma o ka hana ʻana i ka nānā ʻana a me nā hiʻohiʻona i nā faila āu i hoʻomākaukau ai.
Nā nīnau i nīnau pinepine ʻia e pili ana i ka hōʻoia hōʻoia ʻikepili
He aha nā ʻano o ka Data Analytics?
ʻEhā ʻano o ka ʻikepili ʻikepili: descriptive, diagnostic, predictive, and prescriptive. Pane ka wehewehe wehewehe ʻana i ka nīnau o ka mea i hana ʻia. Ke hoʻāʻo nei ka ʻikepili diagnostic e pane i ke kumu i hiki mai ai. Hoʻohana ka ʻikepili wanana i nā ʻenehana he nui mai ka ʻimi ʻana i ka ʻikepili, ka helu, ka hoʻohālike ʻana, ke aʻo ʻana i ka mīkini, a me ka naʻauao akamai e kālailai i ka ʻikepili o kēia manawa e hana i nā wānana e pili ana i ka wā e hiki mai ana. Hoʻokahi ʻanuʻu ʻoi aku ka ʻikepili prescriptive a hōʻike i kekahi ʻano hana a paipai paha i kahi hoʻoholo.
He aha ka ʻikepili ikepili?
ʻO ka ʻikepili ʻikepili ke kaʻina o ka nānā ʻana i nā pūʻulu ʻikepili i mea e huki ai i nā manaʻo e pili ana i ka ʻike i loko o lākou, me ke kōkua ʻana o nā ʻōnaehana kūikawā a me nā polokalamu. Hoʻohana nui ʻia nā ʻenehana a me nā ʻenehana ʻikepili i nā ʻoihana kalepa e hiki ai i nā hui ke hana i nā hoʻoholo ʻoihana ʻoi aku ka ʻike a e nā ʻepekema a me nā mea noiʻi e hōʻoia a hōʻole i nā kumu hoʻohālike ʻepekema, nā manaʻo, a me nā kuhiakau.
He aha kāu e nānā ai i loko o kahi papa ʻikepili manuahi?
ʻOi aku ka maikaʻi o nā hana lima ma ka noʻonoʻo ma mua o ka heluhelu ʻana i ke kumumanaʻo. E ʻimi i kahi papa me nā mea waiwai a hoihoi. ʻAʻole ʻoe makemake i kahi papa paʻakikī loa no ka poʻe hoʻomaka, ʻaʻole pono ia maʻamau i mea ʻole ia iā ʻoe. ʻO ka hope loa, pono e kūkulu i kahi papa ʻikepili pōkole a manuahi paha i kou hilinaʻi e hoʻonui i kāu aʻo ʻana.
No ke aha he palapala hōʻoia Data analytics?
Ke hoʻopau ʻoe i kahi palapala hōʻoia ʻikepili manuahi, hōʻike ia i nā mea hana ua loaʻa iā ʻoe nā mākau koʻikoʻi ma kēia wahi. Hāʻawi ia iā ʻoe i ka manaʻo maopopo o nā wahi o ka ʻike a me ka loea e hana ai ma hope.
He aha ka mea nui o ka ʻikepili Analytics?
Hiki ke kōkua ʻo Analytics i ka hoʻoholo ʻana i ke kumu i hiki mai ai kekahi mea, wānana i ka mea e hiki mai ana a kuhikuhi i kahi papa hana maikaʻi loa. Ma mua o ka hiki ʻana mai o ka ʻikepili nui, ua mālama ʻia ka hapa nui o nā ʻikepili ma nā kamepiula pākahi i nā pāpalapala, nā faila kikokikona, a me nā waihona. ʻO ka pilikia me kēia ʻano o ka mālama ʻana ʻo ia ka paʻakikī o ka loaʻa ʻana o kahi hiʻohiʻona kiʻi nui i nā ʻikepili āpau. Ua hoʻololi ka ʻikepili nui i nā mea āpau ma ka hana ʻana i kahi waihona kikowaena no kāu ʻike āpau, e maʻalahi ai ka hoʻopili ʻana i nā mea hana analytics i kāu ʻikepili.
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lalo laina
Ma ka hōʻuluʻulu manaʻo, nui nā papahana hōʻoia hōʻoia ʻikepili uku e hāʻawi i nā mea hoʻoikaika like a me nā pōmaikaʻi, a me ka uhi ʻana i ka hapa nui o nā mea kumu like.
ʻO ia no ka mea ke hoʻokūkū nei lākou me nā papahana like ʻole.
ʻO nā papa hōʻoia hōʻoia ʻikepili manuahi, ma ka ʻaoʻao ʻē aʻe, hiki ke ʻokoʻa ʻē aʻe. No ka mea ʻaʻole lākou e hoʻokūkū no kāu kālā, hiki iā lākou ke hoʻokō i nā koi like ʻole o nā haumāna. Eia nō naʻe, pono ʻoe e hōʻoia e pili ana kēia mau papa i ke kumuhana āu e makemake ai e aʻo. Hoʻopili ʻia nā papa pōkole i nā kumuhana kikoʻī loa.
E ho'āʻo e ʻimi i kahi hoihoi iā ʻoe.