QUOTE(buffalowings @ Nov 13 2020, 10:42 PM)
data analyst, business analyst, business intelligence analyst, finacial analyst bank analyst, database programer, statistician, market researcher , sales analyst and many more thinking they are data scientist and assuming data science is just about understanding data or extracting data or doing SQL or putting data in nice graph for management to understand using Power Point ......thats why the market is flooded....a data analyst is imho is way far from being a data scientist...its like someone who just started learning how to speak claiming can publish a song album ....hahaha.....not to butt hurt anyone .....but if you dont know how to put together a solution that makes use of technology to process data via statistical modelling or ML ...you are just...a normal analyst..and not a data scientist.....unfortunately even organizations have diff thoughts wat a data scientist role is so good luck....know a telco GM who never done tech and from a sales background....was head of data science because he claims he understands data.....hence anything else can outsource to engineers...the most important thing is that he can interpret data better than less experience person.....(wat a joke)
a data scientist should know how to implement a real world data product and not just to story tell about data (this is like basic)... for a simple example......how to build a recommendation engine, how to create a credit scoring system or how to build a system to detect data phising ........and not just to report sales trend or answer what was my top selling product, which customers bought it and give a recommendation how to improve those reporting insights? (this to me is just analyst role using descriptive analytics basic skill to have)........as a data scientist you would know how to build a working prescriptive analytics solution end to end with constant data input and output.and it you are real good a solution that can slef learn......and nearlly 99 percent of the time...its a solution where a human cant interpret the amount of data that need to be analyzed....hence why is called big data.....for example netflix engine goes thorugh a billion customers viewing behaviors via clicks and interaction with thier product......and then use a statitcal model to find similliar patters for up to >50 movies recommendation for 1 person...similiar with facebook updates you see...they are personalized to a single individual based on algorithms that read billions of interaction the user of the world have with facebook.....a human analyst would not be able to interpret it........hence why you need data science........and data scientist that build data science products.......not a just hjire some one who can find data and interpret via excel.....or BI......that IMHO is an analyst......and you dont need a Masters for it....just go have on th job experience but please dont think u are really doing data science.......you just lying to yourself by having an inflated title....
another good example is alpha go which uses neural nets to study millions of players moves and then come out with a solution to beat a human player as well as IBM Deep Blue for Chess...the solution has to real time and via each move the human makes it predicts what the best move to make and do it real time.....based on its experiences from millions of possible combination.......a human wont be able to process such vast amount of data....if its possible then we only need an Analyst to give insights to the CHess / Go player in Powerpoint for wat should be the next move......lol
Hope the above shares why data science can be such an exciting and well paid job if ppl understand what it should and can do.......
and why if use just for analysis, insights and post event action recommendation by humans there is 1000 capable ppl can do this unlike the above where is a combination of technology and statistics and subject matter data (millions).....to provide realtime actions.....but like i mentioned earlier the other devil is more and more techs developers (google / Miscrosoft, AWS) are findings ways to automate the data science work using various Auto ML tools......so hence might be too late to the game .....where in the future you can buy data science off the shelf.......so probably a dat science wont get a high premium but definitely could be better than pay than an analyst....but likely be lower that a legal professional such as Accountant, Lawyer or IR Engineer where expertise in an subject is not easily duplicable by machines or humans.....
if everfyone can do it.....u wont be as valued.....if only limited ppl can do it....then u r an expert in that area and can ask for a premium in salary......a Masters. / PHD is window dressing that can open nice doors but cant help you to create the work........or maintain the role if you dont really know the job....
Good job on the detailed write-up! I agree for the most part. The funny thing is, people will be posting vacancies for DA , but inside the JD is actually for DS lol. Basically the same story as as other tech jobs, where companies need to hire an entire IT department but only can afford to pay for 1 person, therefore we give him an inflated title like "CTO" or "Head of IT" and maybe give him some stock options. Now, imagine this "CTO" can't actually code but only can talk cock about fluffy concepts. That is the state of things in our country now. Much cringe.a data scientist should know how to implement a real world data product and not just to story tell about data (this is like basic)... for a simple example......how to build a recommendation engine, how to create a credit scoring system or how to build a system to detect data phising ........and not just to report sales trend or answer what was my top selling product, which customers bought it and give a recommendation how to improve those reporting insights? (this to me is just analyst role using descriptive analytics basic skill to have)........as a data scientist you would know how to build a working prescriptive analytics solution end to end with constant data input and output.and it you are real good a solution that can slef learn......and nearlly 99 percent of the time...its a solution where a human cant interpret the amount of data that need to be analyzed....hence why is called big data.....for example netflix engine goes thorugh a billion customers viewing behaviors via clicks and interaction with thier product......and then use a statitcal model to find similliar patters for up to >50 movies recommendation for 1 person...similiar with facebook updates you see...they are personalized to a single individual based on algorithms that read billions of interaction the user of the world have with facebook.....a human analyst would not be able to interpret it........hence why you need data science........and data scientist that build data science products.......not a just hjire some one who can find data and interpret via excel.....or BI......that IMHO is an analyst......and you dont need a Masters for it....just go have on th job experience but please dont think u are really doing data science.......you just lying to yourself by having an inflated title....
another good example is alpha go which uses neural nets to study millions of players moves and then come out with a solution to beat a human player as well as IBM Deep Blue for Chess...the solution has to real time and via each move the human makes it predicts what the best move to make and do it real time.....based on its experiences from millions of possible combination.......a human wont be able to process such vast amount of data....if its possible then we only need an Analyst to give insights to the CHess / Go player in Powerpoint for wat should be the next move......lol
Hope the above shares why data science can be such an exciting and well paid job if ppl understand what it should and can do.......
and why if use just for analysis, insights and post event action recommendation by humans there is 1000 capable ppl can do this unlike the above where is a combination of technology and statistics and subject matter data (millions).....to provide realtime actions.....but like i mentioned earlier the other devil is more and more techs developers (google / Miscrosoft, AWS) are findings ways to automate the data science work using various Auto ML tools......so hence might be too late to the game .....where in the future you can buy data science off the shelf.......so probably a dat science wont get a high premium but definitely could be better than pay than an analyst....but likely be lower that a legal professional such as Accountant, Lawyer or IR Engineer where expertise in an subject is not easily duplicable by machines or humans.....
if everfyone can do it.....u wont be as valued.....if only limited ppl can do it....then u r an expert in that area and can ask for a premium in salary......a Masters. / PHD is window dressing that can open nice doors but cant help you to create the work........or maintain the role if you dont really know the job....
In Malaysia, most companies already "koyak" at the data collection stage. They think they need a DS as a magic bullet to fix the whole thing but they don't. NOT YET. What they really need is a team of data engineers to solve the root problem first. DE is very un-sexy job but it is the first step that you cannot afford to fuck up if you are serious about deploying machine learning models in the organisation. So in terms of practicality if you need a job, I'd say DE is the most secure one. Without DE, literally the whole data team can die.
Nov 14 2020, 10:53 AM

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