10 Yakanakisa Yemahara Yemahara Data Analytics Makosi

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emahara online data analytics makosi
emahara online data analytics makosi

Kune emahara online data analytics makosi? Chokwadi, variko! Aya makosi haangobatsiri nekuti akasununguka, kana kungoti ari online uye anozvifambisa asi fungidzira chii? Ivo vepamusoro-soro!

Sezvo data analytics chiri chinhu chakakosha chebhizinesi, pane kuwedzera kuri kudiwa kwe data analytics nyanzvi. Iko shanduko yetekinoroji uye kuwedzera kwezuva nezuva kwezvisarudzo zvinoenderana nedata kwavewo nyaya yekudzidza.

Kamwe kamwe, data analytics inoita senge nyaya inopisa, ichikusiya uchinetseka kuti chii. Iwe haufanirwe kutarisa zvakare; World Scholars Hub ine mhinduro dzakanakisa!

Chii chinonzi data Analytics?

Data analytics izita rakajairika remhando dzakasiyana dzekuongorora data. Inobata nekushandiswa kwezviono zviripo uye maitiro ekugadzirisa rimwe dambudziko. Mukutaura kwakapfava, ongororo yedata inobata nekuongorora data kana manhamba zvakarongeka.

Kubva pakunzwisisa uye kushandiswa kwematanho e data, data analytics kusvika kumagumo kuti sarudzo inoshanda nesainzi inoitwa. Sainzi mupfungwa yekuti data inofanirwa kuita matanho akasiyana siyana uye maitiro sarudzo isati yaitwa.

Chero mhando yeruzivo inogona kuiswa kune data analytics sezvo chiri chakavanzika chekuita optimization, purofiti maximization, uye kwakaringana kuita sarudzo mubhizinesi rega rega.

Chimwe chinhu chakanaka nezve data analytics ibasa rayo kusiyana. Ne data analytics, unogona kuve muongorori we data, muongorori wehungwaru hwebhizinesi, sainzi yedata, injinjini yedata, muongorori anoshanda, maneja weprojekiti, kana IT system analytics, kutaura zvishoma.

Kune matanho akasiyana-siyana anosanganisirwa mu data analytics, uye ese aya ndeekukura kwebhizinesi. Imwe neimwe yematanho aya ine kiyi yekubudirira kwebhizinesi.

Ndeapi Matanho Akabatanidzwa muData Analytics?

Pazasi pane 4 matanho anobatanidzwa mune data analytics:

1. Descriptive analytics:

Descriptive analytics inobata nemafambiro ari kuita bhizinesi. Mubvunzo unowanzo bvunzwa panguva ino ndewekuti "Chii chiri kuitika mubhizinesi rako?"

Muchikamu chino che data analytics, ruzivo rwakadzika harudiwi. Inopindura mubvunzo wekuti kune kana kuti kwete kune kufamba zvakanaka kwebhizinesi. Nokudaro, mhinduro yacho kazhinji haina kunyatsotsanangurwa.

2. Diagnostic analytics:

Iri ndiro danho mushure mekuongorora kunotsanangura. Diagnostic analytics inobata nechikonzero chinokonzera dambudziko. Mubvunzo unobvunzwa panguva ino unogara uchitanga na sei. Semuenzaniso: "Nei izvi zviri kuitika kubhizinesi rako?"

Neruzivo rwakadzama nezve bhizinesi, dambudziko rekuti "nei" rinoiswa pachena. Iyi nhanho inobatsira kuona bhizinesi anomalies.

3. Predictive analytics:

Iri ndiro danho mushure mekuongorora analytics. Mukufungidzira analytics, nhamba yapfuura uye algorithms inoshandiswa kupa mazano. Mubvunzo unonyanya kubvunzwa panguva ino ndewekuti "chii chingangoitika mune ramangwana?"

Iyi nzira inobva pane maitiro uye maitiro pamusoro pemakore. Inoshandiswa kuona kana imwe tsika ichadzoka zvakare. Izvo zvinobatsirawo kupa zvakanakisa zviripo kurudziro yezvichaitika mune ramangwana.

4. Prescriptive analytics:

Iri ndiro danho mushure mekufungidzira analytics. Prescriptive analytics inosarudza sarudzo yakanaka yekuita. Iyi nzvimbo inobatsira kupindura mubvunzo we "Chii chinofanira kuitwa?"

Ndiro rinotara zvokuita nezvisingafaniri kuita. Izvi zvinogona kushandiswa kuraira vashandisi pane zvinogoneka mhedzisiro uye chii chinofanira kuitwa kune yakanyanya bhizinesi optimization. Panguva ino, kunyange mukutarisana nekusava nechokwadi, sarudzo inotungamirirwa nedata inoitwa.

Zvekutarisisa Paunenge Uchisarudza Yemahara Online Data Analytics Course

Kusarudza kosi yakakunakira iwe ibasa rakawanda kupfuura zvaunofunga kuti ndizvo. Zvimwe zvinhu zvinofanirwa kutariswa usati wasarudza kupinda mukosi yepamhepo.

Pazasi pane zvimwe zvinhu zvekufunga nezvazvo pakusarudza yemahara online data analytics kosi:

1. Kuvimbika uye kuyerwa:

Izvi zvinofanirwa kutariswa usati wasarudza chero kosi. Izvi zvinosanganisira wongororo dzakapihwa nevadzidzi vakambotora kosi. Inosarudza kuti nzira inogona kuvimbwa nayo yakawanda sei. Iwe hausi kuzoda kupinda mukosi uye uchitevedza mutsara ziva kuti ine chiyero che1.0. Iwe haungade izvozvo, handiti?

2. Nguva yekosi:

Makosi epamhepo anogona kunge ari enguva pfupi (maawa mashoma kusvika kumavhiki) kana kwenguva refu (mwedzi kusvika kumakore). Nguva yekosi inofanirwa kuenderana nekuwanikwa kwako uye nhanho yekunzwisisa. Kazhinji, makosi enguva refu anowanzo kutsanangurwa zvakanyanya zvichienzaniswa nekosi dzenguva pfupi.

3. Kusimba:

Online makosi akasiyana evatangi, epakati, uye epamberi, nepo mamwe ari akateedzana ane ese aya matanho. Mamwe makosi anoda kuti iwe uve neruzivo rwekutanga usati wapinda mukosi.

Paunenge uchisarudza kosi mune data analytics, sarudza kosi yakanyatsokodzera kune yako yazvino nhanho yedzidzo.

4. Kosi kuwanikwa:

Mamwe makosi emahara anowanikwa mukati menguva (inowanikwa kwenguva pfupi) nepo mamwe achiwanikwa kwehupenyu hwese. Kusarudza kosi inowanikwa zvachose ndiyo yakanyanya kunaka nekuti unogona kugara uchidzokera kwavari kana paine kusavimbika pamusoro pechidzidzo.

5. Mapurogiramu ekutsigira uye ekudzidzisa:

Kune kumwe kubviswa mumakosi mazhinji emahara uye izvi zvinobviswa zvinosanganisira zvitupa zvekosi, rutsigiro, uye zvirongwa zvekudzidzisa. Zvimwe zvezvirongwa zvekutsigira uye zvekupa mazano zvinosanganisira mapuratifomu ehurukuro, zvivakwa zvekudzidzisa mhenyu, uye maturusi ekuenzanisa.

Zvisinei nekubviswa uku, rubatsiro rwemari isarudzo mune mamwe makosi aya.

6. Chitupa chinogoverwa uye rubatsiro rwemari:

Chitupa chakapihwa mushure mekupedza kosi inoshanda sehumbowo hwehunyanzvi. Kunyangwe mapuratifomu mazhinji ekudzidza epamhepo achida chiratidzo kuti uwane chitupa chinogovaniswa nepo mamwe mapuratifomu achipa rubatsiro rwemari kuvadzidzi vanoda kudzidza makosi aya mahara uye vachiri kupihwa zvitupa. Ita shuwa kuti unotarisa kana rubatsiro rwemari rwuripo kana zvitupa zvisiri zvemahara.

Ndeapi Akanyanya Mahara Emahara Data Analytics Makosi?

Pazasi pane akanakisa emahara epamhepo data analytics makosi:

10 Yakanakisa Yemahara Yemahara Data Analytics Makosi

1. Data Analytics Short Course

  • Zvakanakisa zve:  Beginners
  • Rating: 4.84 kunze kwe5
  • Duration: Maminitsi makumi mana zuva nezuva
  • Platform: Career Foundry.

Data Analytics pfupi kosi sumo inoshanda kune data analytics. Muchidzidzo ichi, iwe unotanga nekuchenesa uye kupedzisa nekuona (kusanganisira machati nemagirafu), uye yakakosha nzwisiso. Kuti unzwisise zviri nani, data chaiyo seti ichashandiswa kugadzirisa nyaya dzebhizinesi.

Iyi kosi ine sumo yedata, uye zvakare ine mamwe maekisesaizi. Zuva rega rega, unogashira zvishanu zvezuva nezuva zvidzidzo zvega mukati menguva yemaminitsi gumi nemashanu.

Dzidzo yega yega ine musanganiswa wevhidhiyo sumo, zvidzidzo zvakanyorwa, maoko-pamabasa, uye mibvunzo inopindirana. Iyo ine sumo kune data analytics inova chidzidzo chakazara chinosanganisira kuchenesa data, kuona, uye ruzivo rwekupedzisira.

2. Kunzwisisa Data Sayenzi

  • Zvakanakisa zve: Beginners
  • Rating: Hazvambotaurwa
  • Duration: 2 maawa
  • Platform: Datacamp.

Kunzwisisa Data Sayenzi kuchawedzera ruzivo rwako rwesainzi yedata, mutauro wemashini, kuona data, data engineering, uye Cloud computing. Iyi kosi ine mavhidhiyo gumi nemashanu uye makumi mana nemasere ekudzidzira.

Muchidzidzo ichi, iwe uchadzidza izvo zvekutanga zvesainzi yedata, kuunganidza data, kuchengetedza, kugadzirira, kuongorora, kuona, kuedza, uye kufanotaura.

Iwe unozodzidzawo kududzira data uye kuisirwa muhupenyu hwezuva nezuva. Zvakare, iwe unozodzidziswa mabasa e data sainzi pasina kunetseka nezve coding.

3. Nhanganyaya kuData Analytics

  • Zvakanakisa zve: Beginners
  • Rating: 4.8 kunze kwe5
  • Duration: 6 mwedzi
  • Platform: Kuda nhangemutange.

Nhanganyaya kuData analytics ichakutora iwe kuburikidza nedata analytics kubva kutanga sezvo pasina ruzivo rwekutanga runodiwa. Iyi kosi ichakujekesera iwe pane-mukuda hunyanzvi hunodiwa kuti iwe ugadzirire basa mune data analytics.

Muchidzidzo ichi, iwe uchadzidza kugadzirira, kuronga, kuongorora uye kuona data rekuongorora. Zvakare, iwe unozodzidziswa nzira yekushandisa maspredishiti, SQL, uye R programming kupedzisa ongororo uye kuverenga.

4. Nhanganyaya yekuongorora data

  • Zvakanakisa zve: Beginners
  • Rating: Hazvambotaurwa
  • Duration: anenge mavhiki e6
  • Platform: Kuipa.

Nhanganyaya yekuongorora data ine ruzivo rwekuita mibvunzo, kuronga data rako mufomati inoshandisika, uye kugadzirisa chero nyaya. Iyo ine zvakare zvidzidzo zvekuongorora data, kutsvaga mapatani mairi, kugadzira yako data intuition, uye kuita mutongo, mhedziso, kana kufanotaura.

Iwe zvakare unozofungwa nzira dzakanakisa dzekutaura zvaunowana. Pamusoro pezvo, iwe unozodzidza kukodha mune yakapfupika uye nekukurumidza nzira uchishandisa iyo Python raibhurari NumPy, Pandas, uye Matplotlib.

Sechinhu chinodiwa kune iyi kosi, iwe unofanirwa kuve wakasununguka nehurongwa muPython uye neruzivo nezve pfungwa dzayo, usati wanyoresa mukosi iyi. Kana zvisiri, ivo vane kosi ye "sumo kune python programming kosi" iyo inokutora iwe kuburikidza neizvi.

5. Math yeData Science

  • Zvakanakisa zve: Beginners
  • Rating: Hazvambotaurwa
  • Duration: 5-6 maawa.
  • Platform: Alison.

Math yeData Sainzi inovhara izvo zvakakosha zvekubvira, zviverengero, uye mutsara algebra sezvo zvine chekuita nekushandisa masvomhu mune data sainzi. Sekunzwisisa kwekutanga kwemasvomhu kunodiwa kune yega yega nyanzvi yedata (data sainzi, muongorori wedata, muongorori webhizinesi, kana mainjiniya wedata), kosi iyi inovhara zvese zvinodiwa.

Kosi iyi haina kufanana nemasvomhu ese asina kunyorwa, asina kushandiswa. PaAlison, iwe uchadzidza masvomhu anozogonesa iwe kupesvedzera nyika. Kosi iyi ndeyechitatu munhevedzano. Kuti uwane zvakanakisa kubva mukosi iyi, zvinokurudzirwa kutora aya makosi maviri ekutanga pasainzi yedata usati watora masvomhu esainzi yedata.

6. Lean Analytics Workshop

  • Zvakanakisa zve: Beginners
  • Rating: 4.6 kunze kwe5
  • Duration: 2 maawa 23 maminitsi
  • Platform: udemy.

Dzidza analytics workshop inokubatsira kuti unzwisise zvakakosha zveanalytics, iyo data-inofambiswa nepfungwa, uye yakaonda yekutanga misimboti. Muchidzidzo ichi, iwe waizotarisa mienzaniso mitanhatu yekuti mabhizinesi mamodheru ane hukama sei nekutanga-ups eese saizi.

Iwe unozodzidzawo maitiro ekuziva nguva yekuenderera mberi nesarudzo uye kushandisa pfungwa dzeLean Analytics kumabhizinesi akasimbiswa uye zvigadzirwa.

7. Nhanganyaya KuData Kuongorora Uchishandisa Excel

  • Zvakanakisa zve:  Beginners
  • Rating: Hazvambotaurwa
  • Duration: 4 mavhiki (pa2-4hrs pavhiki)
  • Platform: edX

Microsoft Excel uye matafura ayo akabatanidzwa epivot ndeimwe yeakanakisa ekuongorora maficha ekuongorora data. Muchidzidzo ichi, iwe uchadzidza maitiro ekuongorora data uchishandisa Excel inonyanya kufarirwa maficha.

Muchisumo chekuongorora data uchishandisa excel, iwe uchaona maitiro ekugadzira matafura epivot muExcel uchishandisa huwandu hwemitsara nemakoramu. Iwe unozopupurirawo kushanda kweExcel pivots mukuita, kusanganisira kugona kwayo kupfupisa data munzira dzakasiyana siyana, kufambisa nekukurumidza kuongorora data, uye kugadzira ruzivo rwakajeka kubva kune data rakaunganidzwa.

8. Bayesian Statistics: Kubva Pfungwa Kuenda Kuongorora Data

  • Zvakanakisa zve: zvenguva
  • Rating: 4.6 kunze kwe5
  • Duration: 12 maawa
  • Platform: Kuda nhangemutange.

Iyo nzira yeBayesian yehuwandu ichaunzwa mune ino kosi kuburikidza nekudzidza kwezvingangoita uye kuongororwa kwedata. Zvakare, nheyo dzeBayesian maitiro pamwe nekushandiswa kwayo kune akajairwa data marudzi anozodzidziswa.

Nzira yeBayesi ichafananidzwa neFrequentist nzira pamwe chete nezvakanakira nzira yeBayesian. Kugadzira nzvimbo yekudzidza inonakidza, kosi iyi inosanganisa mavhidhiyo ehurukuro, kuratidzwa kwekombuta, kuverenga, maekisesaizi, uye mabhodhi ehurukuro.

9. Google Data Analytics

  • Zvakanakisa zve: beginners
  • Rating: 4.8 kunze kwe5
  • Duration: 6 mwedzi (pamaawa gumi pavhiki)
  • Platform: Kuda nhangemutange.

Google data analytics inokupa kunzwisisa kwakakwana kwemaitiro uye nzira dzinoshandiswa nejunior kana shamwari muongorori wedata zuva nezuva.

Muchidzidzo ichi, iwe zvakare uchadzidza akakosha nzira dzekuongorora dzinosanganisira kuchenesa data, kuongorora, uye kuona uchishandisa aya maturusi: maspredishiti, SQL, R programming, uye Tafura. Iwe unozodzidzawo maitiro ekuratidza zvakawanikwa data pane dashboards, mharidzo, uye akakurumbira ekuona mapuratifomu.

10. Dzidza KuCode For Data Analysis

  • Zvakanakisa zve: beginners
  • Rating: 3.5 kunze kwe5
  • Duration: 24 maawa
  • Platform: OpenLearn.

Dzidza kunyora kodhi yekuongorora data ichakudzidzisa maitiro ekugadzira ako mapurogiramu ekombuta nekodhi (imwe mutsara wekodhi panguva). Nekuda kwekufarirwa kwepython munzvimbo dzese dzedzidzo, ndiwo mutauro wekuronga unoshandiswa mukosi iyi.

Kushandisa data chaiyo kubva kuWorld Bank, World Health Organisation, nemamwe masangano, kudzidzira makodhi uye kunyora-up ongororo uchishandisa inozivikanwa Jupyter Notebooks chikuva ichaitwa. Izvi ndezvekugonesa iwe kuona ipapo ipapo mhedzisiro yekumhanyisa kodhi yako uye zvinoita kuti zvive nyore kwauri kuona nekugadzirisa zvikanganiso.

Muchidzidzo ichi, iwe uchadzidza nzira yekuwana yakavhurika data, kuigadzirira iyo kuongororwa, kugadzira zviono, uye kunyora uye kuparadzira ongororo pachena uye pachivande.

Inowanzo Bvunzwa Mibvunzo Pane Yemahara Online Data Analytics Makosi

Ndeipi yakanakisa yemahara online data analytics kosi?

Data Analytics kosi pfupi neCareerFoundry

Sainzi yedata yakafanana neye data analytics?

Aihwa.

Ndeipi mikana yebasa iripo kwandiri kana ndikadzidza data analytics?

Ne data analytics, unogona kuve muongorori wedata, muongorori wehungwaru hwebhizinesi, sainzi yedata, injinjini yedata, muongorori anoshanda, maneja weprojekiti, IT system analytics uye zvimwe zvakawanda.

Makosi ese e data analytics akakodzera kune vanotanga?

Kwete, mamwe makosi anoda imwe prerequisite ruzivo mune mamwe minda usati wasarudza-kupinda kosi.

Yakakosha sei data analytics kune bhizinesi?

Data analytics ndicho chakavanzika chekuita optimization, kuwedzera purofiti uye kwakaringana kuita sarudzo mubhizinesi rega rega.

Tinokurudzirawo

mhedziso:

Kamwe kamwe munhu wese anoita kunge ari kutaura nezve data analytics uye iwe unenge wakaita senge "Ko iyi data analytics yakaita sei?" Sezvakavimbiswa kare, tinovimba wakwanisa kunzwisisa kuti data analytics ndeyei.

Isu tinovimba zvakare wakwanisa kusarudza kubva kwakasiyana emahara data analytics makosi. Tinoda kunzwa kubva kwamuri!