- 46 minutes ago
Data analyst vs data scientist: the differences come down to the kind of work, the tools, and the career path. Here, the comparison breaks down responsibilities, technical skills, education, job titles, and pay so you can see which role fits your goals better. A data analyst usually cleans and analyzes static data, builds reports and dashboards, works with stakeholders, and handles a lot of ad hoc analysis. A data scientist leans more into machine learning, modeling, prediction, and A/B testing, using current data to uncover future opportunities.
The video also covers the overlap between the two careers, including shared tools like SQL, Python, R, Excel, and cloud platforms such as AWS or Azure. It explains how data analysts often work with Tableau, Power BI, pandas, numpy, and matplotlib, while data scientists may use scikit-learn, TensorFlow, Spark, Docker, Kubernetes, and Git. Education requirements are covered too, from bachelor’s degrees to master’s degrees and PhDs, along with common fields like statistics, computer science, engineering, information systems, and physics.
If you are deciding between becoming a data analyst or a data scientist, this breakdown gives a practical look at the day-to-day responsibilities, the skills you need, and the salary range you can expect. It also helps job seekers understand common titles like quantitative analyst, machine learning engineer, and domain-specific analyst roles.
SEO: Ideal for anyone searching for data analyst vs data scientist, data analytics career path, machine learning jobs, SQL Python Excel skills, business intelligence, Tableau vs Power BI, and data science salary comparison. Useful for career switchers, students, and beginners comparing data analyst jobs, data scientist jobs, and the best path into the data field.
The video also covers the overlap between the two careers, including shared tools like SQL, Python, R, Excel, and cloud platforms such as AWS or Azure. It explains how data analysts often work with Tableau, Power BI, pandas, numpy, and matplotlib, while data scientists may use scikit-learn, TensorFlow, Spark, Docker, Kubernetes, and Git. Education requirements are covered too, from bachelor’s degrees to master’s degrees and PhDs, along with common fields like statistics, computer science, engineering, information systems, and physics.
If you are deciding between becoming a data analyst or a data scientist, this breakdown gives a practical look at the day-to-day responsibilities, the skills you need, and the salary range you can expect. It also helps job seekers understand common titles like quantitative analyst, machine learning engineer, and domain-specific analyst roles.
SEO: Ideal for anyone searching for data analyst vs data scientist, data analytics career path, machine learning jobs, SQL Python Excel skills, business intelligence, Tableau vs Power BI, and data science salary comparison. Useful for career switchers, students, and beginners comparing data analyst jobs, data scientist jobs, and the best path into the data field.
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00:00What's going on, everybody? Welcome back to another video.
00:02Today, we're looking at the difference between a data analyst and a data scientist.
00:11Now, I myself have been a data analyst for many years, but I've worked with a lot of data
00:15scientists. And what we're going to do is break down category by category, the difference between
00:19a data analyst and a data scientist and exactly what they do. So let's not waste any time. Let's
00:23jump right onto my screen and take a look. All right. So the first thing that we're going to
00:26take a look at is the responsibilities of a data analyst versus a data scientist.
00:31Starting with a data analyst, some of the responsibilities are going to include
00:33cleaning and analyzing the static data, performing statistical and exploratory analysis,
00:38creating reports and visualizations, working directly with stakeholders to make data-driven
00:42decisions and conduct ad hoc analysis. So let's break this down just a little bit further.
00:47So when I say clean and analyze static data, that static data refers to data that is sitting
00:51in a database. You're not working with live data or a live stream of data.
00:55So a client might be sending you an Excel file every night, which you ingest and put into your
00:59database. And that's, you know, yesterday's data, or you could be accessing their database and you
01:03could pull something every month using something like a stored procedure. But most likely that data
01:07is just going to be sitting in a database somewhere, and you're going to go and clean that data
01:10to make it more usable and then analyze that data as well to find trends and patterns or whatever
01:15the client needs. Now let's get down to reports and visualizations. Now, in my mind,
01:19these are two separate things, although at some companies they are the exact same thing.
01:22Now, in my mind, a report is something like an Excel file or some type of analysis that I've done
01:27that I'm sending to a client and I can kind of automate that. So maybe I'm sending that every
01:31week or every month and I send that to a client. A visualization to me is something like a Power
01:36BI
01:36or a Tableau dashboard or a hundred other tools that you could do, even Python or R,
01:41but it's some type of visualization of the data. And sometimes you're going to send that to a client,
01:46but sometimes you'll just have it online in some type of web application that a client can go
01:50and just check that visualization. The last one I want to touch on is this ad hoc analysis. This
01:55refers to just one-off reports or one-off questions that a client might have. And this is something,
01:59unfortunately, that data analysts do a lot. You're going to be sitting at your computer doing your
02:03work. All of a sudden you get an email from a client saying, Hey, I need this analysis really
02:07quickly. Can you just grab that for me? And of course it's not going to be something super simple.
02:11It'll take, you know, a couple hours to do. And so you go and you do that one-off reports
02:16or that one-off
02:16analysis. And then you send that to the client. That unfortunately is just a big part of being a data
02:20analyst where you just get lots of requests for these kind of one-off reports or one-off types of
02:24analysis. Now let's take a look at data scientists. So a data scientist is going to use current data
02:29to discover future opportunities. They're going to use machine learning models and statistical
02:33methods to analyze the data. They're going to fine tune these models for improved accuracy.
02:38They're going to do a lot of data cleaning and modeling and conduct A-B testing. So this
02:42first one says they're going to use current data to discover future opportunities. What that means
02:46is they're going to use the current data to predict what's going to happen in the future,
02:49at least to the best of their abilities. So thinking about something like Netflix,
02:53you watch all these shows, they collect that data. They have machine learning models running on the
02:57backend that basically say, okay, we have this data. What shows would they want to watch next?
03:03And so they're using that current data to predict what you'll want in the future.
03:06These next ones relate mostly to machine learning models. So using machine learning models and then
03:10fine tuning those models for accuracy. There are a lot of different methods in machine learning that a
03:14data scientist can use. Now, typically most data scientists aren't going to actually write any of these
03:19models. They're using preexisting models that already exist and they're plugging in the data
03:23into these models. A lot of times a data scientist will have some idea of what models is going to
03:27work best, but they might test the data with one, two, three, or even up to like five or 10
03:31models,
03:32depending on what they're doing. And then when they find the one that is the most accurate for
03:36their use case, they're going to try to fine tune that. Now, the next ones are data cleaning and
03:39modeling and then A-B testing. Data cleaning and data modeling is something that both data analysts and data
03:44scientists do, but data analysts tend to be more on the data cleaning side and not as much of the
03:49modeling, unless you're like a senior level or a lead, or just depending on the role that you're in.
03:54Almost all data scientists are going to need to know how to model the data. And really what that
03:58refers to is shaping and cleaning that data for whatever model you're putting the data into.
04:02And now the last thing is A-B testing. Now, A-B testing is basically where you're testing to see
04:07whether one option or another is better for what you're trying to do. The simplest way that I can
04:11explain it is you have a lemonade stand and in front of it, you say 50 cents per cup,
04:15and you test that out for a while. And then you have another lemonade stand where you say
04:19half a dollar per cup. They're saying the same thing or doing something very similar. You're
04:23just testing to see which one has the best results based off some metric that you've defined.
04:27Now, I know that's super, super simplified. It can be very complex, but you're basically comparing
04:32two different things and seeing which one reacts better. So in general, a data analyst is going
04:36to work with a client. They're going to get data in and we're going to help clean it.
04:39And then we're going to analyze that data. With that data, we're going to create reports
04:43and visualizations and give it back to the client or the stakeholder or whoever it's for.
04:47Data scientists can also work with stakeholders and clients, but not as much as a data analyst.
04:51And they're going to use a lot more machine learning and a lot more technical skills in
04:55order to kind of predict what's going to happen next. Of course, they need to clean and model that
04:59data and make sure it's useful for their machine learning model. And then they're going to get
05:03their results and they might even visualize that data. But sometimes data scientists will work with
05:07data analysts to actually help them visualize the data. Now, let's move on to technical skills.
05:11And let's start off with the data analyst. So some of the technical skills that a data
05:14analyst is going to work with are things like SQL, R and Python. And then within Python,
05:18we have a few specific things that are just really popular things like pandas, polars,
05:22numpy and matplotlib, as well as a lot of others. We'll also use data visualization tools like
05:27Tableau and Power BI. Of course, we'll be using Excel. We may also use a cloud platform like AWS or
05:32Azure.
05:33And then lastly, some type of statistical tool like SPSS. Now, these are just some of the more
05:37popular skills and tools that you're going to use as a data analyst. But of course,
05:40there are thousands out there. I've used a ton of other things that aren't on this list. They're
05:44just not as popular for most data analysts. Now, let's take a look at data scientists.
05:49They're going to use SQL, R and Python, but the packages and the libraries may be a little bit
05:54different. They may use pandas, polars, TensorFlow, scikit-learn. They may also use a cloud platform like
06:00AWS or Azure, Excel. Then they may use Spark, Docker and Kubernetes. And then of course, Git.
06:06You'll notice that with the technical skills, there's quite a bit of crossover. They're both
06:09going to be using SQL. They're both going to be using Excel, subtype of cloud platform, R and Python.
06:14There is a lot of crossover between these two, but in general, the data scientist is going to be a
06:19little bit more technical. Now, let's take a look at education and let's start off with the data analyst.
06:24Now, most jobs are going to require a bachelor's degree. That's just pretty standard across all
06:30jobs. There are some jobs as well that do require a master's degree, but typically it's just a
06:34bachelor's degree. Now you don't have to have a degree at all. It just makes it more difficult.
06:38Although I have worked with people who don't have a degree. If you are looking to become a data
06:42analyst, here are some of the degrees that are really useful. Ones like statistics, mathematics,
06:46economics, computer science, business analytics, and information systems. Now let's take a look at data
06:51scientist. Now, typically you're going to need a bachelor's, a master's, or sometimes even a PhD.
06:56Those PhD ones are far and few between, but you will see more jobs requiring a master's for this
07:01type of position, but almost all of them require at least a bachelor's degree. The education for a
07:05data scientist, I think is a little bit more important. If you don't have a degree, I think
07:09it's a little bit tougher to break into the data science space. Some of the degrees that you might
07:13want to have to become a data scientist are things like computer science, engineering, software
07:17engineering, information systems, information technology, statistics, or physics. Now just
07:22to be transparent, I have a degree in recreational therapy, which is not on this list at all, but I
07:27use that healthcare to kind of transition into data analytics. So you don't have to have one of these
07:31degrees in order to become a data analyst or a data scientist. These are just some of the more
07:35recommended degrees. Now let's take a look at some of the job titles that you might see for a data
07:39analyst and a data scientist. Let's start off with the data analyst. The first of course is just going to
07:44be
07:44a typical data analyst title. That one's really common and you'll see that, you know, on almost
07:48any job board that you check. You'll also see ones like quantitative analyst and technical analyst,
07:53as well as ones that are domain specific. So things like healthcare, finance, marketing analyst.
07:58So just kind of insert your domain and then put analyst behind it. Typically there are jobs that
08:03are associated with those as well. Next for a data scientist, we have of course a data scientist.
08:07This could be data scientist one, two, three, same thing for data analysts. You'll see data analyst one,
08:12two, three. We also have machine learning engineer and machine learning developer. So if you're
08:17looking to become a data analyst or data scientist, these are some of the job titles that I would be
08:21looking for. Lastly, let's take a look at the pay for both a data analyst and a data scientist.
08:26Let's start off with data analyst. For an entry level data analyst role, you're looking at anywhere
08:30from 50 to 75. Now that's where you're going to get on average. Sometimes it'll be a little bit lower.
08:35Sometimes it might be a little bit higher. As a mid-level analyst, you're looking at anywhere from 70 to
08:3990,000.
08:39And as a senior level data analyst, anywhere from 80 to 120,000. Now this is for the United States.
08:45So if you're in another country, you might want to take a look at what your averages are there.
08:48Now, all of these averages are very dependent on several factors for both the data analyst and the
08:52data scientist. It can definitely depend on your years of experience, your education, where you live.
08:57All of these things play a factor into how much money you're going to make. But now let's take
09:00a look at the data scientist. For an entry level role, you'll be looking at 65 to $100,000.
09:05For a mid-level role, anywhere from 85 to 120,000. And then for a senior data scientist role,
09:10anywhere from 100 to 150,000. You'll notice some overlap on both of these where it's 65 to 100
09:16versus 85 to 120. Those are just the averages I'm giving you the data as it is based off of,
09:22you know, these aggregations from Google, Glassdoor and LinkedIn data, but they are pretty accurate.
09:27You're also going to notice that data scientists get paid more money. I think this is because of the
09:30technical nature of the work. Data scientists tend to need a little bit more education.
09:34It's a little bit more technical and difficult to understand some of the things that they're doing.
09:38So if you do have the expertise to be able to do that work, they're going to pay you more
09:41money to
09:42do it. It is kind of a double-edged sword though, because even though they make more money,
09:45it's harder to land those jobs, in my opinion, whereas a data analyst is a little bit easier
09:49to land a job. So that is the breakdown between a data analyst and a data scientist.
09:53Now, if you're trying to become one of those, there are many things that you should take into
09:56consideration. The first thing to consider is which one do you really like and have a passion for? If you
10:00really like analyzing data and finding those trends and patterns, then data analysis is a great place
10:05for you. But if you want to be a little bit more technical, work with machine learning and maybe
10:08even AI, kind of predict what's going to happen next, then trying to become a data scientist might
10:13be great for you. Now, just some of my unsolicited advice. If you want to become a data scientist,
10:17that's your end goal for you. You can still start out as a data analyst, especially if you don't have
10:21the education, you don't have the technical skills just yet, and you kind of want to work your way up
10:25to there.
10:26A data analyst might be a great place to kind of leapfrog into a data scientist position.
10:31I've seen a ton of people do that. Some people just stay data analysts forever, like me, because
10:35that's really what I have a passion for. But some people will use data analysis to kind of get some
10:39experience working with data, and then they'll build up their technical skills and then become
10:43a data scientist. Again, just some unsolicited advice. They have a lot of kind of the core skills
10:47that they use, and then data scientists tend to just be a little bit more technical. So I hope that
10:52this was helpful and that you understand the difference between a data analyst and a data scientist now.
10:56If you did, be sure to like and subscribe below, and I will see you in the next video.