Amakhono weScienceist Skills for Resumes, Letters Cover, and Interviews
Ososayensi bezinkampani basebenza ezimbonini ezihlukahlukene, kusukela ku-tech kuya emithi kuya ejensi kahulumeni.
Iziqu zomsebenzi ezenzweni zesayensi ziyahlukahluka, ngoba isihloko sibanzi. Kodwa-ke, kunamakhono athile abaqashi ababheke cishe kuwo wonke usosayensi wedatha. Ososayensi datha badinga amakhono okubala, okuhlaziya nokubika.
Nasi uhlu lwamakhono ososayensi wedatha wokuqalisa, ukumboza izincwadi, izicelo zokusebenza, kanye nezingxoxo. Kuhlanganiswe uhlu oluningiliziwe lwamakhono ayisayensi ososayensi obaluleke kakhulu, kanye nolwazi olude lwamakhono ahlobene nakakhulu.
Indlela yokusebenzisa uhlu lwamakhono
Ungasebenzisa lolu hlu lwamakhono kuyo yonke inqubo yokusesha umsebenzi. Okokuqala, ungasebenzisa lawa magama akhono ekubuyiseleni kwakho. Ekuchazeni umlando wakho womsebenzi, ungase ufune ukusebenzisa amanye alawa magama ayisihluthulelo.
Okwesibili, ungasebenzisa lokhu encwadini yakho yekhava . Emzimbeni wencwadi yakho, ungasho eyodwa noma amabili kulawa makghono, futhi unikeze isibonelo esithile sesikhathi lapho ubonisa khona lawo makghono emsebenzini.
Okokugcina, ungasebenzisa lawa magama amakhono ku-interview. Qinisekisa ukuthi okungenani isibonelo esisodwa sesikhathi ubonise amakhono aphezulu ayisihlanu abhalwe lapha.
Yiqiniso, umsebenzi ngamunye uzodinga amakhono ahlukene nokuhlangenwe nakho, ngakho qiniseka ukuthi ufunda incazelo yomsebenzi ngokucophelela, futhi ugxile kumakhono adwe ngumqashi.
Phinda ubuyekeze olunye uhlu lwethu lwamakhono adalwe umsebenzi kanye nohlobo lwekhono .
Amakhono Ayisosayensi Ezingu-Five Data Scientist
I-Analytical
Mhlawumbe ikhono elibaluleke kakhulu kumsayense wedatha ukukwazi ukuhlaziya ulwazi. Ososayensi datha kufanele babheke, futhi baqonde, ukuhamba okukhulu kwedatha. Kumele bakwazi ukubona amaphethini nezitayela kudatha, bese uchaza lawo maphethini. Konke lokhu kuthatha amakhono okuhlaziya aqinile.
Ukudala
Ukuba usosayensi omuhle wedatha kusho nokudala. Okokuqala, kufanele usebenzise ubuciko ukubona izintandokazi kudatha. Okwesibili, udinga ukwenza uxhumano phakathi kwedatha okungase ibonakale ingahlobene. Lokhu kuthatha ukucabanga okuningi kokudala. Okokugcina, udinga ukuchaza le datha ngezindlela ezicacile kubaphathi enkampanini yakho. Lokhu kuvame ukudinga ama-analog nokudala.
Ukuxhumana
Idatha ososayensi akudingeki ukuthi bahlaziye idatha kuphela, kodwa futhi kumele bachaze lelo datha kwabanye. Kumele bakwazi ukuxhumana idatha kubantu, bachaze ukubaluleka kwamaphetheni kudatha, futhi basikisele izixazululo. Lokhu kuhilela ukuchaza izindaba eziyinkimbinkimbi kwezobuchwepheshe ngendlela elula ukuyiqonda. Ngokuvamile, ukuxhumana kwedatha kudinga amakhono okubonwayo, omlomo, nokubhala okubhaliwe.
Izibalo
Nakuba amakhono athambile afana nokuhlaziywa, ubuciko, nokuxhumana kubalulekile, amakhono aqinile nawo abuyele emsebenzini. Usosayensi wedatha udinga amakhono ematheksthi, ikakhulukazi ekubaleni okungenakuqhathaniswa ne-algebra eqondile.
Ukuhlela
Amasayense edatha adinga amakhono ayisisekelo ekhompyutheni, kodwa amakhono okuhlela abaluleke kakhulu. Ukukwazi ikhodi kubalulekile cishe noma yikuphi isikhundla sesazi sesayensi. Ulwazi lwezilimi zokuhlela ezifana ne-Java, R, Python, noma i-SQL kubalulekile.
Amakhono e-Data Scientist
A-C
- Ukuguquguquka
- I-Algorithms
- I-Algorithmic
- I-Analytical
- Amathuluzi okuhlaziya
- Ama-Analytics
- AppEngine
- Ukuqinisekisa
- AWS
- Idatha enkulu
- C ++
- Ukusebenzisana
- Ukuxhumana
- Amakhono ekhompyutha
- Ukwakha Amamodeli Wokubikezela
- Ukubonisana
- Ukudlulisela Ulwazi Lwezobuchwepheshe kubantu Abangewona Ubuchwepheshe
- CouchDB
- Ukudala i-Algorithms
- Ukudala Ukulawula Ukuqinisekisa Ukunemba Kwemininingwane
- Ukudala
- Ukucabanga okubucayi
- Ukuhlakulela ubudlelwane nabathintekayo bangaphakathi nabangaphandle
- Insizakalo yekhasimende
D-J
- Idatha
- Ukuhlaziywa kwedatha
- Idatha Analytics
- Ukuguqulwa kwedatha
- Idatha yokuHlanganisa
- Ama-Science Science Tools
- Amathuluzi wedatha
- Idatha yokuThola
- D3.js
- Ukwenza isinqumbo
- Izinqumo zemithi
- Ukuthuthukiswa
- Ukubhala
- Ukudweba Ukuvumelana
- ECL
- Ukuhlola izindlela ezintsha ze-Analytical
- Ukusebenza endaweni ehamba ngokushesha
- Ukugqugquzela imihlangano
- Vula
- I-Google Visualization API
- Hadoop
- HBase
- Amandla Aphezulu
- IziTatimende Zokuthola Imininingwane Ulwazi
- Ukuhumusha Idatha
- I-Java
L-P
- Ubuholi
- I-Algebra engqimba
- Ukucabanga Okucatshangelwayo
- Amamodeli Wokufunda Amashini
- Amasu Wokufunda Komshini
- Izibalo
- I-Matlab
- Ukuphikisa
- Amamethrikhi
- I-Microsoft Excel
- Imayini yezokuThuthukiswa koMphakathi
- Idatha ye-Modeling
- Amathuluzi wokufanisa
- I-Calculus Multivariable
- I-Perl
- I-Power Point
- Isethulo
- Ukuxazululwa kwenkinga
- Ukukhiqiza Ukubukwa kwedatha
- Ukuphatha iphrojekthi
- Izindlela Zokulawulwa Kwephrojekthi
- Izikhathi zeProjekthi
- Ukuhlela
- Ukuhlinzeka Isiqondiso kubachwepheshe be-IT
- I-Python
R-W
- R
- I-Raphael.js
- Ukubika
- I-Software Reporting Tool
- Amathuluzi okubika
- Imibiko
- Ucwaningo
- Ukucwaninga
- I-Risk Modeling
- I-SAS
- Izilimi zeSkripthi
- Ukuzikhukhumeza
- SQL
- Izibalo
- Amamodeli wokuFunda weStatisti
- I-Modeling yesitatimende
- Ukuqapha
- Ithebula
- Ukuthatha Okokuqala
- Ukuhlolwa kwama-hypotheses
- Ukuqeqeshwa
- I-Verbal
- Ukusebenza ngokuzimela
- Ukubhala
Funda kabanzi: Isayensi Yedatha Izitifiketi Zomsebenzi
Izihloko Ezihlobene: I- Soft vs. Skills Hard | Indlela Yokufaka Amagama Angukhiye Ekuphumeni Kwakho | Uhlu lwamagama angukhiye e-Resumes and Cover Letters | Amakhono oQembu | Qalisa kabusha uhlu lwamakhono