發覺好多人對Data Analyst有錯誤幻想

呢個唔係應該係algorithm engineer 咩
DS 擴啲 基本上end to end

你點分呢種data scientist 同ml engineer?

想問下工作上實際要計數多唔多
還是用excel 整model就ok
用返啲咩mse naive dse 去predict？

我唔太清楚
但依個係我對外國Data Scientist既了解

只可以話 data engineer既demand一定大過data scientist

Bba 淨係識吹水

DS role冇algorithm engineer呢d role
Data scientist本身真係做呢d

叫business analyst先吹脹

ML Engineer focuses on the codes surrounding the model
例如data metadata, pipeline, distribution/ concept drift, deployment code, API, etc
Data Scientist focuses on the model itself

DE唔叫轉行啦下話 升職就差唔多

香港地搵工第一件事學識係唔好睇title
要睇份工jd做乜
好多hr,head hunt都讀唔成書呃下飯食
根本都唔知果行做啲乜，啲skill邊個打邊個
Title同jd成日九唔搭八

唔會係升職啦

落手計唔會，但interview會問到最基本果堆數
但如果你對D數唔熟就用果D tools就好易有問題
我成日都會溫返notes去make sure自己無用錯到

　有無試過真係provide到非阿媽係女人嘅insight?

de個skillset同ds完全唔同wo 唔係咩
de似係搞data structure,transformation


我又想問下如果想純粹整recogniton model之類 係應該報ds定ml engine?

De/data architect demand大好多好多
Ds個demand lagging 因為好多公司連data infra都未整好
Pipelines 都未駁好 邊有data玩advance野
講緊Uber/grab 呢啲起哂kafka hadoop 已經玩埋streaming pipelines