Me Elena Gerasimova, ke poʻo o ke kumu "» ma Netology ke hoʻomau nei mākou i ka hoʻomaopopo ʻana i ko lākou launa ʻana me kekahi a me ka ʻokoʻa o nā ʻepekema Data a me nā ʻEnekinia ʻIkepili.
Ma ka hapa mua ua hai mai lakou .
Ma kēia ʻatikala e kamaʻilio mākou e pili ana i ka ʻike a me nā loea e pono ai ka poʻe loea, he aha ka hoʻonaʻauao e waiwai ʻia e nā mea hana, pehea e hana ʻia ai nā nīnauele, a me ka nui o ka loaʻa ʻana o nā ʻenekinia data a me nā ʻepekema data.
He aha nā mea ʻepekema a me nā ʻenekinia e ʻike ai
ʻO ka hoʻonaʻauao kūikawā no nā poʻe loea ʻelua ʻo Computer Science.
ʻO kēlā me kēia ʻepekema data-ka ʻepekema ʻikepili a i ʻole ka mea loiloi-pono e hiki ke hōʻoia i ka pololei o kā lākou hopena. No kēia mea hiki ʻole iā ʻoe ke hana me ka ʻole o ka ʻike helu helu a me ka helu helu pili i ka makemakika kumu.
He mea nui ke aʻo ʻana i nā mīkini a me nā mea hana ʻikepili i ka honua hou. Ināʻaʻole i loaʻa nā mea hana maʻamau, ponoʻoe e loaʻa nā mākau e aʻo koke i nā mea hana hou, e hana ana i nā palapala maʻalahi e hoʻokaʻawale i nā hana.
He mea nui e hoʻomaopopo pono i ka ʻepekema ʻikepili e kamaʻilio maikaʻi i nā hopena o ka loiloi. E kōkua iā ia i kēia ʻike ʻikepili a i ʻole nā hopena o ka noiʻi a me ka hoʻāʻo ʻana i nā kuhiakau. Pono nā loea e hana i nā pakuhi a me nā kiʻi, hoʻohana i nā mea hana ʻike, a hoʻomaopopo a wehewehe i ka ʻikepili mai nā dashboards.
No ka mea ʻenekinia ʻikepili, ʻekolu mau wahi i hele mai i mua.
Algorithms a me nā hoʻonohonoho ʻikepili. He mea nui ka maikaʻi ma ke kākau ʻana i nā code a me ka hoʻohana ʻana i nā hana kumu a me nā algorithm:
- ka nānā ʻana i ka paʻakikī algorithm,
- hiki ke kākau i nā code maʻemaʻe, mālama ʻia,
- hana pūʻulu,
- hana manawa maoli.
Nā waihona ʻikepili a me nā waihona ʻikepili, Business Intelligence:
- mālama ʻikepili a me ka hana ʻana,
- hoʻolālā o nā ʻōnaehana piha,
- ʻIke ʻikepili,
- puana waihona waihona.
Hadoop a me ka ʻikepili nui. Nui aʻe ka ʻikepili, a ma ka pae o 3-5 mau makahiki, e lilo kēia mau ʻenehana i mea pono no kēlā me kēia ʻenekinia. ʻOi aku:
- ʻIkepili Lakes
- hana pū me nā mea hoʻolako kapua.
Aʻo mīkini e hoʻohana ʻia ma nā wahi āpau, a he mea nui e hoʻomaopopo i nā pilikia ʻoihana e kōkua ai e hoʻoponopono. ʻAʻole pono ka hiki ke hana i nā hiʻohiʻona (hiki i nā ʻepekema data ke mālama i kēia), akā pono ʻoe e hoʻomaopopo i kā lākou noi a me nā koi e pili ana.
ʻEhia ka nui o ka loaʻa ʻana o nā ʻenekinia a me nā ʻepekema?
ʻIkepili ʻEnekinia Loaʻa
Ma ka hana honua ʻO nā uku hoʻomaka he $ 100 i kēlā me kēia makahiki a hoʻonui nui me ka ʻike, e like me Glassdoor. Eia kekahi, hāʻawi pinepine nā hui i nā koho waiwai a me nā bonus makahiki 000-5%.
I Rusia i ka hoʻomaka ʻana o kahi ʻoihana, ʻaʻole i emi iho ka uku ma mua o 50 tausani rubles ma nā wahi a me 80 tausani ma Moscow. ʻAʻole koi ʻia ka ʻike ma mua o ka hoʻomaʻamaʻa hoʻopau ʻana i kēia pae.
Ma hope o 1-2 mau makahiki o ka hana - he 90-100 tausani rubles.
Hoʻonui ka mākia i 120-160 tausani i 2-5 mau makahiki. Hoʻohuiʻia nā mea e like me kaʻoihana kūikawā o nā hui mua, ka nui o nā papahana, hana me nāʻikepili nui, a me nā mea'ē aʻe.
Ma hope o 5 mau makahiki o ka hana, ʻoi aku ka maʻalahi o ka ʻimi ʻana i nā hakahaka ma nā keʻena pili a i ʻole ke noi ʻana i nā kūlana kūikawā e like me:
- Hoʻolālā a alakaʻi paha i ka mea hoʻomohala ma kahi panakō a kelepona paha - ma kahi o 250 tausani.
- Pre-Sales mai ka mea kūʻai aku nona nā ʻenehana āu i hana pū ai me ka pili loa - 200 tausani me kahi bonus hiki (1-1,5 miliona rubles).
- Nā loea i ka hoʻokō ʻana i nā noi ʻoihana ʻoihana, e like me SAP - a hiki i 350 tausani.
Loaʻa kālā o nā ʻepekema data
Makeke o nā mea kālailai o ka hui "Normal Research" a me ka recruiting agency New.HR e hōʻike ana e loaʻa ana i nā loea ʻIke ʻIkepili ma ka awelika ka uku kiʻekiʻe ma mua o nā mea loiloi o nā ʻoihana ʻē aʻe.
Ma Rusia, ʻo ka uku hoʻomaka o kahi ʻepekema data a hiki i hoʻokahi makahiki o ka ʻike mai 113 tausani rubles.
Hoʻopau ʻia nā papahana hoʻomaʻamaʻa i kēia manawa i manaʻo ʻia he ʻike hana.
Ma hope o 1-2 mau makahiki, hiki i kahi loea ke loaʻa i ka 160 tausani.
No kahi limahana me 4-5 mau makahiki o ka ʻike, piʻi ka lāʻau i 310 tausani.
Pehea e hana ʻia ai nā nīnauele?
Ma ke Komohana, loaʻa i nā haumāna puka o nā papahana hoʻomaʻamaʻa ʻoihana kā lākou nīnauele mua ma ka awelika 5 mau pule ma hope o ka puka ʻana. Ma kahi o 85% loaʻa kahi hana ma hope o 3 mau mahina.
ʻO ke kaʻina hana nīnauele no ka ʻenekinia ʻikepili a me nā kūlana ʻepekema data ʻaneʻane like. ʻO ka maʻamau he ʻelima mau pae.
Hōʻuluʻulu. Pono nā moho me ka ʻike mua ʻole (e laʻa, ke kūʻai aku ʻana) e hoʻomākaukau i kahi leka uhi kikoʻī no kēlā me kēia hui a i ʻole he kuhikuhi mai kahi ʻelele o kēlā hui.
ʻIke ʻenehana. Lawe pinepine ia ma ke kelepona. Loaʻa i hoʻokahi a ʻelua mau nīnau paʻakikī a e like me ka nui o nā nīnau maʻalahi e pili ana i ka waihona o ka mea hana i kēia manawa.
Nīnauele HR. Hiki ke hana ma ke kelepona. Ma kēia pae, hoʻāʻo ʻia ka moho no ka lawa ʻana a me ka hiki ke kamaʻilio.
nīnauele loea. ʻO ka hapa nui o ke kanaka. Ma nā ʻoihana like ʻole, ʻokoʻa ke kiʻekiʻe o nā kūlana ma ka papaʻaina limahana, a ʻokoʻa paha ka inoa o nā kūlana. No laila, ma kēia pae ʻo ia ka ʻike loea i hoʻāʻo ʻia.
Nīnauele me CTO/Chief Architect. He kūlana koʻikoʻi ka ʻenehana a me ka ʻepekema, a no nā ʻoihana he mea hou lākou. He mea nui e makemake ka luna i ka hoa hana a ʻae pū me ia i kona manaʻo.
He aha ka mea e kōkua ai i nā ʻepekema a me nā ʻenekinia i ka ulu ʻana o kā lākou ʻoihana?
Ua ʻike ʻia ka nui o nā mea hana hou no ka hana ʻana me ka ʻikepili. A he kakaikahi ka poe i like ka maikai i kela mea keia mea.
ʻAʻole mākaukau nā hui he nui e hoʻolimalima i nā limahana me ka ʻole o ka ʻike hana. Eia naʻe, hiki i nā moho me ka liʻiliʻi liʻiliʻi a me ka ʻike o nā kumu o nā mea hana kaulana ke loaʻa ka ʻike kūpono inā lākou e aʻo a hoʻomohala iā lākou iho.
Nā hiʻohiʻona maikaʻi no ka ʻenekini data a me ka ʻepekema data
Ka makemake a me ka hiki ke aʻo. ʻAʻole pono ʻoe e alualu koke i ka ʻike a hoʻololi paha i nā hana no kahi mea hana hou, akā pono ʻoe e mākaukau e hoʻololi i kahi wahi hou.
ʻO ka makemake e hoʻokaʻawale i nā kaʻina hana maʻamau. He mea nui kēia ʻaʻole wale no ka huahana, akā no ka mālama ʻana i ka maikaʻi o ka ʻikepili kiʻekiʻe a me ka wikiwiki o ka lawe ʻana i ka mea kūʻai.
ʻO ka nānā a me ka hoʻomaopopo ʻana i ka "mea ma lalo o ka pā" o nā kaʻina hana. ʻO kahi loea nona ka nānā a me ka ʻike piha o nā kaʻina hana e hoʻoponopono wikiwiki i ka pilikia.
Ma waho aʻe o ka ʻike maikaʻi loa o nā algorithms, nā ʻōnaehana data a me nā pipeline, pono ʻoe e aʻo e noʻonoʻo i nā huahana — ʻike i ka hoʻolālā a me ka hoʻonā ʻoihana ma ke kiʻi hoʻokahi.
No ka laʻana, he mea pono e lawe i kekahi lawelawe kaulana a hele mai me kahi waihona no ia. A laila e noʻonoʻo e pili ana i ka hoʻomohala ʻana i ka ETL a me DW e hoʻopiha iā ia me ka ʻikepili, he aha ke ʻano o nā mea kūʻai aku a he aha ka mea nui e ʻike ai lākou e pili ana i ka ʻikepili, a pehea hoʻi e launa pū ai nā mea kūʻai aku me nā noi: no ka ʻimi hana a me ka launa pū ʻana, ka hoʻolimalima kaʻa. , palapala noi podcast, kahua hoʻonaʻauao.
Ua kokoke loa nā kūlana o ka mea loiloi, ʻepekema data a me ka ʻenekinia, no laila hiki iā ʻoe ke neʻe wikiwiki mai kahi ʻaoʻao a i kekahi ʻoi aku ka wikiwiki ma mua o nā wahi ʻē aʻe.
I kēlā me kēia hihia, e maʻalahi ia no ka poʻe me ka ʻike IT ma mua o ka poʻe i loaʻa ʻole. Ma ka awelika, hoʻomaʻamaʻa hou a hoʻololi ka poʻe mākua i hoʻoikaika i nā hana i kēlā me kēia 1,5-2 mau makahiki. He mea maʻalahi kēia no ka poʻe e aʻo i kahi hui a me kahi kumu aʻoaʻo, ke hoʻohālikelike ʻia me ka poʻe hilinaʻi wale i nā kumu wehe.
Mai nā mea hoʻoponopono o Netology
Inā ʻoe e nānā nei i ka ʻoihana o Data Engineer a i ʻole Data Scientist, kono mākou iā ʻoe e aʻo i kā mākou papahana papa:
- ʻoihana "".
- ʻoihana "".
- pono"".
Source: www.habr.com