What is data analysis? what is data sciences? Who is a Data Scientist? how both are they related and which one is right for me is what we’ll be covering in today article. 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 I’m so really 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.
Who is a Data Scientist
Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data, and apply knowledge and actionable insights from data across a broad range of application domains
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 scientist, 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.
Also 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 five to 10% of their job. And most of the time, they have these models set up that they use 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 gonna be good and usable for their models.
So when they plug it into these models, it’s gonna give them the best results in the best output. And then it’s formatted correctly for their machine learning algorithm to actually work and actually read the data and gives 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 in two different results. And seeing which one actually gives you better results in that show. That really is all AV 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 animals.
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 they’ll use either sequel 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 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.
Most times, 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 them 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 the 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 Glassdoor 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 scientists have, which are computer science, mathematics, economics, we 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.
And now let’s look at the skills section.
So for data scientists, some of the skills that you might need are sequel 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, pie charm, and some type of ID E. 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 sequel R and Python, and then some libraries for that are going to be pandas, NumPy, and matplotlib. 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 some type of cloud platform like AWS, or Azure.
Now, before we get to 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. Let’s look at data scientists 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, then for a senior level position, you’re looking at somewhere around $120 to $150,000.
For a data analyst for an entry level position, you’re looking at around 45 to 60,000 for mid level 65 to 85,000, for a senior level position, around 85 to 110,000. Now looking at the 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 skill sets 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 masters 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 may 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 to 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 reading. If you liked this article, be sure to like and share.