‫Welcome back.
‫I want to take a quick section to cover a topic that is often forgot about in data science and machine
‫learning courses.
‫However, in order to truly understand a topic, we need to understand the high level principles and
‫the landscape of that topic.
‫In this case, we want to work as a data scientist or a machine learning engineer.
‫So we need to at least understand the topic of data engineering.
‫But what does that mean?
‫What's with all these titles?
‫Data scientist.
‫Data analyst.
‫Data visualization.
‫This is madness.
‫This is so confusing.
‫Well, let's talk about data engineering and how it fits into this whole ecosystem that we've been talking
‫about up until now.
‫Now, we learned that data science is all about using data to make business decisions.
‫Right?
‫One of the ways that we can make business decisions is to use a technique like machine learning.
‫That allows a computer to learn and figure out the solution to a problem that may be a little too complicated
‫for a human to solve.
‫Or maybe too tedious.
‫And takes too long of a time.
‫So we want to automate it.
‫So machine learning is a technique, and data science is the idea of using data and converting it into
‫something useful for a product or business.
‫And data analysis is a subset of data science that allows us to analyze the data that we have.
‫But the missing piece is that of data engineering, and not all companies have data engineers.
‫But as companies get bigger and bigger, they need to hire for this role.
‫Let's take a look at why.
‫Who brings all this data to us in a nice little file?
‫Where is this data coming from?
‫How do we manage it?
‫Do we throw it out after we're done?
‫Who labeled this data?
‫You see, a data scientist or a machine learning expert doesn't need to concern themselves most of the
‫time with these topics.
‫You ideally have the data accessible to you because this part of data is handled by a data engineer.
‫So who is this mythical figure?
‫Well, a data engineer would take all of the data points that are incoming, let's say for a company,
‫let's say this company has all these products and all these datas are coming from their users, from
‫their security cameras, from their website, from Iot devices.
‫And a data engineer takes all this information.
‫And then produces it and maintains it in databases or in certain type of computers so that the business
‫has access to this data in an organized fashion.
‫So they're kind of like the librarians where they collect all this information and they organize it
‫for us so that people like machine learning or data science experts can use this data.
‫But this is a simplified version of what a data engineer does.
‫In order for us to really understand this, we need to go back to the beginning and ask the question
‫What is data?
‫I'll see you in the next one.
‫Bye bye.