 What's going on everybody? My name is Alex Friedberg and today we're going to be talking about the difference between a data scientist and a data analyst. Now, as many of you know, if you watch my channel, I am a data analyst. But what you may not know is that I actually work on a data science team. And so I work alongside a lot of data scientists. And so I know a lot of the work that they do. And so I feel like I have a pretty good understanding of each position and the differences between these jobs. And so we're going through four main areas today, which are responsibilities, qualifications, skills, and then at the very end, salary. And after all of that, I'm going to talk about what position might be right for you. So let's start off with responsibilities. What kind of things are you going to be working on in your actual job? Let's start off with data scientists. As a data scientist, you're going to be using your data to discover opportunities. And what that means is you're going to be using your current data to find trends and patterns that are going to affect the future business that you are working in. You'll also be developing analytical methods and machine learning models. And most people when they think data scientists think that is the core work that they're going to be doing. And that's actually not true. That's actually probably be five to 10% of their job. And most of the time, they have these models set up that they used over and over and over again. So they already know what kind of models they're going to be using. They are just working with the data to put it into those models. And then at the end, they're tweaking their hyper parameters to really narrow down their accuracy and get better results. But genuinely, they aren't doing a ton of work in these machine learning models. They're not developing new models. They're just trying to fit their data into these models to get the best results they can out of them. The next thing is data cleaning. And when I say data cleaning, I mean a lot of data cleaning. Because genuinely they are doing so much work, just cleaning their data, making sure it's going to be good and usable for their models. So when they plug it into these models, it's going to give them the best results and the best output and that it's formatted correctly for their machine learning algorithm to actually work and actually read the data and give them the output that they want. And you'll also be conducting A-B testing. Now this looks very different in different industries, but basically you're going to be doing two independent tests, getting two different results and seeing which one actually gives you better results. In a nutshell, that really is all A-B testing is, but it can get quite complicated. And so I'm not going to go too much into that. But let's look at the data analysts now. As a data analyst, you're going to use your data to solve problems that your company has right now. So instead of trying to find trends or opportunities for the future, you're trying to answer questions that your company has now and have an immediate impact. Other responsibilities are also creating reports or creating dashboards. And for creating reports, a lot of times you'll use either SQL or some cloud platform or any number of other tools that are out there for creating reports. And then for dashboards, you might be using something like Power BI or Tableau or maybe even Python. It just depends on what your company is using. I've seen a very wide variety, but creating reports and dashboards can be a large part of what a data analyst actually does. And often they'll also help with gathering incremental data from different sources. So you need the data, you have to get it from somewhere. So you may work with a client or an internal team to help gather that data or get that data into your systems, whether that's your warehouses or just your SQL servers or whatever that is for your company. But you have to be getting that data somewhere and using that data for these reports and for these dashboards. So that may also be a part that you're doing. Now let's look at the qualifications for each of these positions and let's start out with the data scientist. As a data scientist, you're often going to need a master's degree or above. That can be in anything from computer science, econ, mathematics, physics. It really depends on what industry you're going into and what they value. But oftentimes those more STEM backgrounds are really good for a data scientist. Now it's not to say that you have to have a master's degree, but oftentimes that is a prerequisite for most positions. But there are some positions where they're really just looking at experience and your skills to see if you're a good fit and they might take you if you only have a bachelor's degree. But again, this is the prerequisite for a lot of positions that you'll find on LinkedIn or Gloucester or other job posting websites. You need a master's degree just to meet the base requirements. And for a data analyst, you're going to need a bachelor's or above for most positions and that's going to be in a lot of the same degree fields that a data scientist had, which are computer science, mathematics, economics. But you don't have to have a bachelor's degree or have a bachelor's degree in those fields. You can have no degree and have really good skills and work your way up. And you can also have a degree that's completely unrelated, but you've made the switch and it is absolutely possible to do that. I will say that for qualifications, the bar is much lower for a data analyst than it is a data scientist. And you may have a lot better chance of getting your foot in the door as a data analyst than you would if you're trying to actually get a data scientist position right off the bat. If you were liking this video, be sure to like and subscribe below. And now let's look at these skills section. So for a data scientist, some of the skills that you might need are SQL, R and Python. And in Python, there's a few libraries that really stand out, which are pandas, NumPy, scikit-learn, and TensorFlow. Then you have things like Tableau, Power BI, data visualization tools. You may also be working with NLP, which is natural language processing, which could be structured or unstructured data. You may be using Apache Spark, Jupyter Notebooks, PyCharm, some type of IDE. And then you may also be using some statistical tool, which is SAS or SPSS or any number of other tools that are out there. So for a data analyst, you may need SQL, R and Python. And then some libraries for that are going to be pandas, NumPy, and Matpotlib. You'll also need a data visualization tool like Tableau or Power BI. You'll need to be doing data modeling. Also need a statistical tool like SAS or SPSS or many others. You'll be working in Excel a lot. And then you'll also probably work in something like a cloud platform like AWS or Azure. Now, before we get into salary, we are looking at salary ranges. These aren't going to be specific answers for either your location or your industry. So if you do want specific answers for either of those things, I recommend doing your own research on those. But let's get into it and let's look at data scientist salaries. So for an entry level position, you're looking at around 85 to 95,000. For a mid-level, you're looking at 100 to 120,000. And then for a senior-level position, you're looking at somewhere around 120 to 150,000. And for a data analyst, for an entry-level position, you're looking at around 45 to 60,000. For a mid-level, 65 to 85,000. For a senior-level position, around 85 to 110,000. Now, looking at these salaries, you might think, well, Alex, I'll just go be a data scientist. Obviously, they make a lot more money. I'll just go do that. But I want to urge you to really look at these positions and see which one fits your skillsets, your education, and the kind of work that you want to be doing. Because I will say that although there are a lot of similarities, there's a lot of differences as well. I think for a data scientist, it takes someone who's very driven to get either a master's or a PhD in a specific degree and really pursue machine learning and know how to use those models correctly. Although a lot of your time is going to be data cleaning, you have to know how to use the models and which models fit best for your data. But if you have those qualifications and you have those skills or you are actively looking to pursue those skills, that might be the perfect career for you. And it might be a very lucrative career for you, especially if you get into the right industry. Now, for a data analyst, I think that a lot of people are going to fit into this category instead of a data scientist category. And I think this is for a few reasons. One, I think it's a little bit easier to learn the skills that you need in order to become a data analyst. And two, since the qualifications are a bachelor's or above, a lot more people are going to be included or have the opportunity to become a data analyst. Overall, it really is up to you on which one you prefer. I think you should really look at yourself and see what kind of work you enjoy doing. I think both of these careers are fantastic options long term. And I don't think these jobs are going to be going away at all in the near future. I think the popularity of these jobs are only going to increase over the next 10 years. So getting started now and knowing what you want to do and just going for it is really the best advice that I have. So that's all I got. Thank you guys so much for watching. If you like this video, be sure to like and subscribe below. And I'll see you in the next video.