 Data science. This might be one of the most requested videos of all time on this channel. So in this video, I'm going to be going over what exactly is data science and also some of the most important factors when it comes to becoming a data scientist. We're going to be talking about pay, job satisfaction, job growth, and other x factors that I think are important. And I'm going to rank them on a scale from one to 10. And then at the very end, I'm going to give you a total score. Now, if you appreciate my hard work on this, go ahead, gently tap that like button and let's get into it. So usually when you look up the definition of something on Google, it's way too complicated and it doesn't really make sense. But actually, the definition I saw this time was pretty decent. Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data. Now that's actually a better definition than what you'd usually hear on Google. But let's break that down a little bit further. Well, when it comes to data, there's basically five main stages capture, maintain, process, analyze, and communicate. Now, generally speaking, first you would capture the data, then you would maintain it, then you would process it, then you would analyze it, and then you would communicate your findings to business people in order to help them make strategic business decisions. And each of these main steps have processes, as you can see in this photo, and you can actually get hired as a data science and just do one of these processes. For instance, I've mentioned this person before on my channel, somebody I know that is a database architect. All they do is database architecture, and they do a very specific type of database architecture at that. So there are many different careers and sub specialties within data science. Now, what is a data scientist? Google has this definition as a professional responsible for collecting, analyzing and interpreting extremely large amounts of data. And by the way, I know it's pronounced data scientist. I already know there's people that are probably just seriously commenting right now. Yes, I know it's pronounced data scientist. I say data, get over it. So basically what they do in layman's terms is they'll receive data. And when they first get it, it's just a bunch of gibberish. So basically you want to clean up that data, try to organize it and make sense of it so that you can help the company make strategic decisions. So another thing I wanted to cover is data scientist versus data analyst. Now, technology in general is famous for having a ton of overlap between different types of careers. So for instance, software developer and software engineer at some companies are the exact same thing. And then at other companies, there are differences. Same thing goes with data scientist and data analyst. Although there is definitely a bigger difference there. And this is also a very controversial subject. You see a lot of people arguing about the different titles, but generally speaking, data scientist is definitely going to be the more prestigious title. And it also pays a lot better. But with that being said, in this video, I am going to be referring to data scientists. So the next question is how do you become a data scientist? So this is another very controversial subject. And if you ask 10 different data scientists, you might get five people on one side and five people on the other side. But generally speaking, there is two different pathways to become a data scientist. One of them is more of a formal education pathway. And then the other one is kind of a shortcut. The pathway of getting a formal education is a well paved path. Many people have gone down that path, you kind of know what you're going to get if you're able to make it to the end. This would be something like getting an undergraduate degree in computer science and then getting a master's in statistics. But again, this is such a new field that it's really only existed for the last 15 years or so. Some companies really care about you having a formal education. Others only care about your skills and being able to demonstrate them. Now with that being said, BLS groups data science with other careers. So they call it computer and information research scientists. And they do recommend getting a master's degree and career one stop.org which is another government website has data scientists with 14% of them having a doctorate and 35% of them having a master's degree for a total of 49% of data scientists having a graduate level degree, 37% have a bachelor's degree of some sort, 4% have an associate and 7% did some college but no degree and then 3% have just a high school diploma. So it is possible to become a data scientist without getting a graduate level degree, but a good amount of people who become data scientists do have a master's or a doctorate. So it's pretty much split right down the middle there just like you saw, and it's truly going to depend on the company that you're trying to get a job with. Now, keep in mind, many of these people might have gotten completely unrelated degrees in undergraduate like chemistry, for instance, and then they decided to switch careers into data science. In that case, they were probably either self taught, or they took a boot camp or something along those lines. So most common ways to become a data scientist is to get a formal education, but you do have the option of apprenticeships, boot camps, certifications and being self taught. Now that second path is much more difficult, much more high risk. Most people will fail that way. But if you're somebody who's really good at teaching themselves, you might save yourself a lot of money and time by trying that path instead. Now becoming a data analyst is actually much easier than becoming a data scientist. In many cases, you can get an entry level data analyst job without getting a master's or a doctorate. So what many people do is they become data analysts first, and then they learn the skill set of a data scientist while they're on the job. And then they're able to get into a data scientist position. All right. So now we're going to go over the job growth of the data scientist career. First of all, all jobs in the technology industry are expected to grow at 13% over the next 10 years, which is much higher than average. The average is about 4%. Then if you look at computer and information research scientists, which is what this one is grouped under for BLS, they say that the job outlook is 22% over the next 10 years, which is fantastic. Now because of the popularity of data science, many people are trying to get into it. Lots of boot camps, certifications, online programs, et cetera have popped up claiming to be able to get you a job. So some say that this industry is becoming saturated and it's harder to get a job than it was just a few years ago. Now I think that the term saturation gets thrown around way too much and it's completely relative to the person's job position. For some people, saturation means they're no longer being constantly pestered by recruiters and offered $30,000 sign-on bonuses. And for others, that could mean sending out hundreds of job applications without getting a single interview. But with that being said, that's one thing to look into. And that's why I always recommend one, having the skills that you need in order to do the job and two, being able to demonstrate that you have those skills. And I also think that in the future, data scientists are going to specialize in having more industry specific skills. Right? So I think there's probably going to be like nursing informatics data scientists and pharmaceutical sales data scientists and video game data scientists, et cetera, et cetera. The reason for this is because it's really important for you to be familiar with whatever field you're collecting data in, because you're going to be helping that company make key strategic business decisions. So it's good for you to be familiar with the industry. So it may be that as the career evolves, data science might become a valuable add-on to other careers, which in my opinion would be a very good thing. So overall, this one is going to get nine out of 10 when it comes to job growth. Next, we're going to be talking about job satisfaction. So career explorer.com does surveys and data scientists came out in the top 43%. So slightly higher than average when it comes to job satisfaction glass door in 2021 had data scientists as the second highest rated job with a job satisfaction score of 4.1, which was really good. Now in pay scale did their survey, the two closest careers that I could find were database administrator and computer software engineer. And you could see that the meaning score was relatively medium to low, whereas the satisfaction score tended to be on the higher side. So what that basically means is they do enjoy their job. However, they don't necessarily think that it positively impacts the world. Now with that being said, never go into something just because it's popular or because it pays well, make sure that you have an interest in it, right? Make sure that you have a passion for it. Also, I would say that it's usually a good idea to go into something that you have a natural talent for, because being good at your job usually leads to a sense of satisfaction, which is going to increase your overall happiness over the long run. Also, your happiness is going to be significantly affected by the industry that you're in, as well as the company that you work for. And data scientists jobs do tend to be in the technology industry, which is known to treat their employees really well and give them great benefits. So overall, when it comes to satisfaction, I'm going to give data scientists a score of 8.5 out of 10. Next on the list is going to be salary. So computer and information research scientists make $126,000 a year, which is fantastic. And this is according to BLS. According to Glassdoor, they make about $117,000 a year, which is still great. Now, interestingly enough, depending on the company they work for, they could make a lot more than this. So for instance, with Amazon, they make about $130,000 a year. And with Cisco, they make $157,000. You can get even more specific with the salary if you check websites like levels.fyi, which is basically an anonymous open source place where you can report how much you make. So they report the company, the location, the years of experience, and the total compensation. So for instance, here's two different data scientists, they both live in Seattle, Washington, and they work for Amazon. One of them is an L six, the other ones in L five. And that's basically their rank within the company of Amazon, which weirdly sounds like a military rank. And then it also shows their years of experience as well as their years at the company. So for instance, this one has zero years of the company, but 10 years of experience as a data scientist. The bottom one has two years at the company, but four years of experience as a data scientist. And the one with 10 years of experience is making $475,000 a year. The one with four years of experience is making $195,000. Now just generally speaking, the technology industry pays super, super well. So for instance, the median annual age for computer and information technology occupations is $91,000. For all other occupations, it's $41,000. So it's literally over double. So technology related careers make fantastic money in general. And that's just a sign that there is a lot of opportunity in tech. So overall, when it comes to pay, I'm going to give data scientists a 9.5 out of 10. All right, next we're going to be talking about X factors. And this is anything that popped into my head that was important, but didn't fit into any of the other categories. So first of all, let's talk about automation. Will robots take my jobs dot com says that a computer and information research scientist has a 4% chance of automation and most technology related jobs have very low chances. And to be honest with you, if this sort of job could be automated, we would probably be at a point in society where robots have either taken us over and enslaved us or we don't really even have to work anymore because we can just have robots do our work for us. So it wouldn't matter at that point either way. Now, if you check the zip recruiter skills index, which is basically an index of different skills and how valuable it is on the open market, and you type in data to it, you are going to see dozens and dozens of suggestions that are data related, right? Data modeling, data management, data center operations, database management, big data skills, relational database, data warehousing, et cetera, et cetera, et cetera. And these are some of the highest ranked skills on the list. So the skills that you learn as a data scientist are incredibly valuable to companies. Now one thing I will mention here, this is a very new career and just like doctor or lawyer, there's probably going to be lots of different sub specialties that are kind of fuzzy right now. They're not exactly separated, but there is going to be very clear lines of separation in the future. And this is a relatively flexible job. It is one of those jobs where if you want to, you could probably find a remote job. Or if you're really ambitious, you want to rise up the ranks as an employee in the company, there's opportunities for that. Or if you're super ambitious and you want to start your own company, you will have very good opportunities in the technology industry. Data science will position you well with each of these goals. So it's very flexible. After all, data is now more valuable than oil or gold. So the X factor score here is going to be at 9.5 out of 10. All right. So final score is going to be 9.125 out of 10. That is fantastic. Probably one of the highest scores you're going to see. Just as a very quick recap, some of the pros here are going to be high pay, lots of opportunity and very flexible. Some of the cons are going to be that it's a very new career. So it's not well established how to break into it. It's also changing very fast and it requires many dynamic skill sets and ageism can be a big problem as it is with many technology related careers. If you enjoyed this video, you might want to check out my degrees that create the most millionaires video right here. And if you haven't done already, go ahead, gently tap that like button, hit the subscribe button, ring the notification bell and comment down below any thoughts, comments, et cetera, especially if you are a data scientist. I definitely want to hear your opinions down in the comments below and I will see you guys next time.