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Data Sayenzi Modeling uye Kukosha muKusanganisa

Kunyangwe iwe uchida kuregeredza zvakadii asi hazvigoneke kufuratira kukosha kwedata. Iyo data inozoongororwa, kurongedzwa, uye kugadziriswa kuti uwane mhedzisiro. Data yesayenzi inoumbwa nekubatanidza zvinhu zvakawanda pamwechete. Izvi zvinhu zvinosanganisira algorithm kuvandudza, data interface, uye zvigadzirwa. Inobatsira mukugadzirisa matambudziko akaomarara uye mudzi wekuumbwa iyi data.

Sezvo isu tese tichiziva kuti iyo data mbishi ruzivo inoyerera mukati uye inochengetwa mumatura yedata rekambani. Ipapo iyo date kuchera inoenderera pane iyo data yakaunganidzwa ichiita kuti tikwanise kuisa masimba epamberi. Basa guru re data science ndeye kushandisa data mune yakasarudzika nzira kuti uwane mari yebhizinesi.

Ida zvaunoverenga? Wobva wabata chidimbu chitsva chemukati iko pano.

Zvekugadzirisa data science, iyo inonyanya kukosha chikamu ndechekuenzanisira data. Maitiro ekuenzanisira ndiyo nzira yekubhadhara zvakanyanya iyo yave nzvimbo yekutarisisa yevadzidzi ve data. Asi, kunyangwe iwe uchifunga zvakadii, zvinhu hazvisi nyore uye zvakapusa. Izvo hazvisi zvekushandisa chete mabasa kubva kune imwe pasuru kirasi uye kuishandisa kune iripo data. Kune zvimwe kune izvo kupfuura izvo.

Data yesayenzi modelling zvinoenderana nekuongororwa kweiyo chaiyo modhi. Iwe unofanirwa kuona kuti iyo data yakasimba uye yakavimbika mune zvisikwa usati waenderera kumberi. Kwete izvi chete, asi iyo data science modhi inosanganisirwa neruzivo rwekugadzira chimiro. Kunze kwezvinhu zvakawandisa, iyo data modelling inodawo mamwe maitiro anovimbisa kuti iyo data inoshandiswa yakanyatso kuenderera ichipa zvirinani uye zvinowirirana mhedzisiro.

Uyewo Verenga: Data Sayenzi seSevhisi (DSaaS): Kuongorora Dhata Zvirinani

Big Data is all about looking out for the latest and interesting trends through various processes like Machine pakudzidza, nhamba, and another numeric method. However, it really wants to have a better insight that you must use the predictive modeling technique. This technique is linked with the Data Featurization.

Chii chinonzi Dhizainzi? 

Mune izvi zvese, unogona kunge uchishamisika kuti chii chaizvo Featurization ndizvo. Kuita kuti zvive nyore, inova nzira inoshandura iyo nested JSON chinhu kuita pointer. Iyo inova vector ye scalar kukosha icho chiri chakakosha chinodiwa pakuongorora maitiro.

Zvakanaka, iyi dudziro imwechete inogona kududzirwa nenzira dzakasiyana. Heano seti yezvinoreva kuseri kwechirevo ichi.

  • JavaScript Object Notation (JSON) is a lightweight format for the data set through which machines can easily write and read. The main reason behind using JSON is that it can easily and strongly interact with different languages (platform) such as JavaScript, R, Python, etc. The software that is used to interact with the stored data is mainly for the data that is influenced by JSON.
  • Imi mose mungangodaro makadzidza izwi rokuti scalar mufizikiki. Iyo i1-yakatarwa kuwanda kwemuviri. Mune iyo data featurization, inoshandiswa kune komputa sisitimu yekuchengetedza uye kuyeuka yuniti yechiyero. Iyo poindi yekurangarira ndeyekuti inongochengetera kukosha mune imwechete fomu senge 'bhuruu', '1', nezvimwe.
  • Mune Linear Algebra tsika, izwi rekuti vector raishandiswa zvakanyanya. Iko kuunganidzwa kwezvinhu zvinoverengeka kunozivikanwa se vector nzvimbo. Semuenzaniso, [2, 5, 7, 9].

Nerubatsiro rwevheji yezviyero, zvinokwanisika kuita yehuwandu uye yekuverenga basa risingakwanise kuitwa neyakajeka data.

Uyewo Verenga: 6 Zvikonzero zvekusarudza R chirongwa cheData Sayenzi

Robust Dhata Muenzaniso

Ndichiri kutaura nezve akasimba mamodheru, ivo vanokosheswa nekuumbwa kwechigadzirwa. Kune zvakawanda zvivakwa zvakafukidzwa nemhando yakadaro. Iwe unowana kuita kwakanaka kunozove kunoenderana zvachose pane metric kukosha. Nekudaro, pane yakawanda nguva iyo imwechete metric kukosha inogona kuve isina kukwana zvachose kana kutsausa. Zvakare, mashandiro eiyo modhi pazvinhu zvakasiyana zvinonetsa kuenderera mune yakadaro kesi. Izvi zvinowanzoitika kana sisitimu iine dambudziko rekusarudzika.

Kunze kweizvi, iwe unogona kuwana yakanaka generalization kuburikidza neiyi yakasimba modeli. Izvi zvinongoreva kuti iyo modhi inoshanda nemazvo uye zvine mutsindo nemadhata uyezve pane iyo yavasina kudzidziswa.

On talking about the aspect of data science modeling, you simply can’t miss out on the kuongorora kwekunzwisisa. Iyo inoshandiswa kuyedza kusimba kwemuenzaniso kuburikidza nenzira dzakakosha dzinovimbisa mhedzisiro iri nani. Mune ino mamiriro, kana isu tikachinja kukosha kwekuisa kwemuenzaniso ipapo mhedzisiro inozoshandurwa zvakanyanya. Nekudaro, izvi zvinhu hazvigone kushanda kwazviri zvinoshandura kukosha seichi nekuti kusimba kune zvese nezve kugadzikana.

Chimwe chinhu chakakosha sekushushikana kududzira (izvo zvisingaite nguva dzese). Seizvo zita rinoratidzira, ndezvekuti mhedzisiro yemuenzaniso inodudzirwa sei. Nekudaro, kune akawanda mamodheru ayo anoonekwa seakasviba-bhokisi nekuda kwezita ravo rakaoma rekududzirwa. Nekuda kweizvozvo, zvinokurudzirwa kushandisa modhi inogona kududzirwa zviri nyore. Izvi zvinotevera zvakanyanya kana kuburitswa kweiyo modhi kuchida kuve munzvimbo yakachengeteka.

Uyewo Verenga: Chii chinonzi Hadoop uye zvachinja sei Data Science

The Vision yeKuratidziro iri Nani Dhata

There are times when the vision or understanding of a model can be used to highlight the improvement that must be incorporated in the future model to elevate its efficiency. Also, if the Database transaction has extra data like transaction type then it can be extremely helpful. This will make the association between frequency and typical amount easy to determine. The integration of a new data set requires a lot of additional work for linking and cleaning. After which the data is refined in the featuring.

Uyewo Verenga: 5 Makuru Akakosha data musayendisiti anofanirwa kuve neBig Data Projects

Kukosha muKushandisa

Pane akasiyana maitiro ekuwedzera kukosha kune iyo featurization maitiro. Izvo zvinotevera

Sezvambotaurwa, featurization inoshandiswa kubvarura musiyano uripo pakati pehukama uye yakacheneswa fomati ine fomati iyo inopa fungidziro yekufananidza maalgorithms uye bvunzo dzinoverengeka dzinogona kushandiswa pane iyo data.

Nekubatsira kwekuratidzira, zviri nyore kupfuura nemumatambudziko mune yevatengi chirongwa nenzira dzakasiyana.

  • Inobatsira mukugadzira maturusi anogona kushandiswa pamapurojekiti uye chengetedza mamwe mabasa uchiita kudaro.
  • Iwe unogona kushandisa iyo yekugadzirisa maitiro pane yekushandisa-kesi iyo ichadaidzwa kakawanda, Nekudaro, pakuisa mari mune yakasarudzika uye iyo Hardware mhinduro, zviri nyore kunzwisisa kuti zvinobatsira mukubhadhara mamwe masheya pamapurojekiti.
  • Unogona kunyange kushandira pamwe nenzira inoshanda. Nekuda kweyakagara ino sisitimu inopa, inotibvumidza isu kutora nyore basa kubva kwarakasiiwa. Zvakare, zviri nyore kuona zvinobuda kuburikidza nemazano uye ongororo zvinoita kuti data riwedzere kusanzwisisika.

Kana iwe uri kushandisa yekufanofungidzira ongororo nzira saka iyo featurization inova inonyanya kukosha uye zvine simba basa kuenderera naro. Iwe unogona kuwana nyore chiratidzo nezve iyo data uye modelling.

Uyewo Verenga: 4 Nzira Nzira Dhijitari Dhata iri kuchinja Data Sayenzi

Maitiro Ekuita Kuratidzira? 

Kana iwe uchishuva kuvaka modhi ine yakawanda yekugona saka zvakafanira kuti ubvise ruzivo rwese rwezvinhu zve data. Kune nzira dzakawanda dzekuwana izvozvo. Unogona kushandisa izvi chero kupi, zvisinei, iwe uchazoda sarudzo yekuchenesa data. Iyi sarudzo inobatsira kubvisa dambudziko uye kugadzirisa iyo data poindi, kubvisa mheremhere misiyano, uye kuzadza zvisipo tsika.

Zvisinei, Kujairika inodikanwa usati wamhan'arira misiyano mukuenzanisira kwedata. Ichave nechokwadi chekuti hunhu hwakasiyana hwakaringana hwakaringana hunogona kuitwa kuburikidza neshanduko ine mitsara. Nekudaro, iyo nzira yekumisikidza inoshandiswa zvakare kushandura iyo kusiana mufomu yechimiro mushure mekuichenesa.

Kuti ubatsire featurization, imwe nzira yakamhan'arwa iri Kubhinya. Izvi zvinobatsira mukugadzirwa kwemazita emhando dzakasiyana dzinogona kupaza muzvinhu zvebhinari zvinoshandiswa mukuenzanisira kwedata.

Ikozve kune yekuderedza nzira yehuremu iyo inobatsira mukuumba iyo ficha yakatarwa. Izvi zvinogadzira Meta maficha kana maficha akagadzirwa nematanho musanganiswa. Iyo inoratidzira iyo ruzivo sezvakaratidzirwa nediki diki.

Uyewo Verenga: Artificial Intelligence Vs. Machine Kudzidza Vs. Data Sayenzi

Kunze kweizvi, the data science attributes do more than just creating the value of the raw data. As mentioned above points are just the bit and pieces of featuring data modeling. However, it requires ample learning and proper metrics to study the data. Data modeling is an easy branch but it still needs to be mastered properly that can be beneficial for the company. It may take some time but with an easy robustness method, it will be a cakewalk.

Une chirongwa chinoda Dhata Modeling? Wobva watambanudzira kwatiri kubvunza.

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