I am a data scientist with an education company in Irvine, CA; Ask Me Anything!

Yikes, everything that is considerably relevant to my business question. I would not store the information on Ikea chairs & their form factor if I was trying to answer about a business's expense sheet. Maybe if I was trying to predict the hype cycle of Ikea's products compared to my own products. 

I have a Twitter : @ktbernoulli. 😉

A data engineer combines their knowledge of data storage with data maintenance. A warehouse of data storage is what you desire then a data engineer is who you need to buy it / maintain it. Once you're happy with the data you've been able to collect, now you want to analyze it for information? Come talk to the data scientist.

Before, no, sometimes grocers / big retail have sort of company tracking inventory that will automatically tell them when the next time to buy is approximately, or they have some kind of subcontract that allows them to buy only at a certain time. After, the game changes because regardless of which of the two scenarios they are in, I'm sure after data science, they will not only know how much to buy, but how much to sell, how long to sell, how often to stock on shelves, etc. etc.

The only problem with collecting data on your target market is there will be an instance where your target market is not the supposed target market for your product. You can definitely start with who has been historically buying your product over the time that you have been offering it as well as simultaneously seeing if the current customers are the same as the previous ones. What's really interesting is someone who was completely outside of your supposed target market buying your product. That is the person you should really analyse to see why they did not fit your predictions & how would you able to capture more customers like them.

I've been inaccurately told that data science is just some machine learning repackaged over the years, but it's pretty wrong in my opinion. Those who do not know the full scope of what a data scientist can do should respectfully sit down & consider when they have ever been considered for a recommendation systems project or incorporated image data in their studies. So quite frankly, five years.

You should consider your customers as the first line of data you should be consider collecting on. It sounds like privacy issues, but as long as you are getting extremely specific like 'how many times did Bob walk into my store this past week?' kind of situation then most customers would be delighted for personalization of their experience with your product.

As a consultant, either bits & pieces are given to me to finish in a project that is my specialty or the whole project end-to-end. Both reuqire the same amount of dedication because I personally have a bare minimum in quality before I even let it be considered viewable.

As an educator, I am always flexing the material to better suit the students to really help them both understand data science & realize that their industry needs it. 

Data science research might as well be either research using data science or research on the future techniques for data science. Either of which needs a PhD & patience, because research itself is pure hard work along with techniques for data science being discovered are very industry specific.

Being able to educate those who do not have a background in data science about the uses of data science. Their eyes really open up about what they can now answer / do once they acquire the skills to do so. 

Useful is a very debatable word. One moment, you do not care precisely the ratio of peas in your products' soup & the next day, there is a pea shortage announced so now you have to care. It might be as slightly meaningless as that or much more serious. So in all of my experience so far, it is better to have too much data than to have too little data.

In all seriousness, if a person in a division that's not form your own were to come offer & crystal clearly understand what your data / how you are collecting & storing & labeling your data, then it is the clarity we all want to achieve. Unfortunately, there will be instances many instances of companies having little to no notes on why they label their data a certain way & expect outsiders to know exactly what they are saying.

Machine learning is a subdivision of data science; while the true work of data science lies in the data preparation, we only prepare data for the machine learning algorithms we will use further. But what sets machine learning apart from data science is the fact that data science then goes beyond what machine learning is & answers a business purpose.

If you are talking about database data, then databases. If you are talking about images, videos, Twitter feeds, Facebook notifications, etc. etc. then by all means necessary.

Reading data? Hm, there's the machine learning aspect that might lead you first in your analysis about the data, but physically just yourself 'reading' the data ? None, besides pure research on the data you are dealing with.

Hm, in my perspective, data science has the role of linking the ideals of academia with the pureness of business today. A data scientist very well knows the capabilities of their skills to the world of academia, but chooses to display it / incorporate it with their chosen industry / company's issues.

To a data scientist, a data engineer is who we come to when we feel like the quality of the data that was presented to us is neither at a minimal quality nor can be fixed by us. Data engineers are in charge of both maintaining a company's data as well as keeping the quality in check.

I have been in the data science field for about 2 years.

Data science is a multidisciplinary field that includes programming, statistics, a sense of communication, & domain expertise. There have been some data scientist that have scrapped by without say... domain expertise because their team/industry has domain experts for them to thrive on.