0:00 data analyst, data scientist, and data engineer. Most people don't know how different they are. And if you're picking a data career, choosing the wrong one can lead to a massive waste of time and money, too. And this will be the only one video you need to make sure you don't miss on the best option for you. I will cover now the differences between the roles. You will find out what each job actually does. The salary comparison, so you will see how much each role typically pays, so you know what to expect. I will cover the long-term growth, so you will learn which one offers steady opportunities for the next decade, the top tools and technologies. So, I'll show you the skills you can start learning right now to make yourself stand out and also my personal suggestion at the end. And so, you will discover which one I would personally choose if I had to start over. So, if any of these sounds
0:45 interesting, make sure to stay with me. So my plan is to start by looking at these three data rows as if we are building a marketing strategy from the ground up which is a proper super practical uh business scenario to give you a really good understanding and we can use the data pyramid of needs as our road map. So picture it like a three layer pyramid. So first we have the data engineer so the bottom layer which is infrastructure organization. Then we have data analyst. So the middle layer, so insights and reporting. And then we have data scientist, the top layer with prediction and optimization. So starting at the bottom of the pyramid, we have the data engineer. This is the person who makes sure you can actually use the data in the first place. Think of them like the city planner and construction crew for your marketing data. They are
1:30 the ones who connect all the roads, lay down pipelines, and keep the traffic flowing smoothly. And so in our marketing example, let's say that your company runs ads on Google, Facebook, and LinkedIn. The data engineer sets up pipelines to pull data from each of these platforms, clicks, impressions, cost into a central database or data warehouse like Snowflake or BigQuery. They will handle tasks like cleaning up messy data. Maybe your ad IDs and campaign names don't match across different platforms or setting up automatic jobs that run nightly. So when you start your morning, all the fresh data is already there. And again to give you a practical idea, a typical day might start with checking logs to see if last night pipelines run successfully. If something broke, maybe Facebook changed its API. Your data engineer fixes that fast so that the marketing
2:17 team is not left in the dark. In the afternoon, they might optimize data storage so everything runs faster or spin up a new cloud service to handle a sudden spike in ad traffic. And so why matters? Well, because without proper data plumbing, nobody else in the company can do their job effectively. No reliable data means no meaningful analysis or predictions. Climbing up to the middle of the pyramid, we find the data analyst, the person who turns all the freshly collected data into digestible insights for the marketing team. Think of them as your translator and storyteller, bridging raw numbers and actual business decisions. And so again in our marketing example, let's say the CMO, which is the chief marketing officer, wants to know how much each platform contributes to lead generation. The data analyst hops into a
3:03 tool like Tableau, PowerBI or Looker, connecting to that central database that data engineer set up. They will clean up some inconsistency or outliers like campaigns with zero spend or accidental double counting and build dashboards that compare cost per click, conversion rates, and overall ROI by channel. A typical day might include pulling fresh data to see if yesterday email campaign performed better than your usual ads. Then meeting with stakeholders to say, "Hey, we spent 20% more on Facebook last week, but we got 40% more leads, so let's double down on that next month." And so why matters? Because the data analyst translates raw data into actionable insights. They tell you which marketing channels are worth your money and which ones need a reboot. Finally, at the top of the pyramid, we have the
3:49 data scientist, the forward thinker who uses advanced techniques to predict future outcomes and discover hidden patterns. If the analyst is describing the past and present, the scientist is all about shaping and predicting the future. Again, in our marketing example, suppose the marketing team wants to predict which leads are most likely to turn into paying customers. The data scientists gather historical data, maybe from our CRM, about leads and their behaviors. Did they open certain emails? What pages did they visit? Did they attend any webinars? And then they build a predictive model in Python using libraries like uh Psycharn or maybe a cloud-based service to score each lead. That way sales can prioritize the hottest leads. A typical day might start with checking how last night model
4:35 deployment performed. If the accuracy decreased, they will investigate if new data is messing up the model or if the models is just getting stale. Then they might meet with the data engineer to make sure all the relevant features like click timest times uh page visits are flowing in properly and in the afternoon they can uh test new algorithms. So refine hyperparameters to have a few more percentage points in the model accuracy. So why matters? Because the data scientist helps you stay ahead of the competition by using data not just to explain what happened in the past but to forecast what's coming next and suggest how to act on it. And so hopefully this shows you how this is all connected is one continuous flow. The data engineer ensures that raw data from all those ad platforms and CRM tools
5:20 lands neatly and reliably in your system every day. Data analyst grabs that organized data, build reports, and shows the marketing team where you're winning or losing. And the data scientist takes it a step further using those insights and that clean data to build models that predict future performance. So pinpoint your highest values leads. And when this pyramid works well, you get a steady pipeline of fresh reliable data which is from the engineers. Then you have clear dashboards and insights that shape everyday decisions. So that's from the analyst and powerful predictions that keep you ahead of the game which are the scientist. Together they transform raw numbers into real world business impact especially in marketing where every click and conversion counts. Now let's look at the juicy details on the average salaries and we're going to take US as
6:06 an example. So when it comes to paychecks, the numbers can vary quite a bit based on location, level of experience, and the company size. However, according to Glastor, data analysts typically earn an average base salary of around $87,000 per year. Data engineers are around $134,000 per year and data scientists often see averages near $126,000 per year. These figures are just ballpark estimates and in the tech hubs like San Francisco, New York salaries can be uh way higher while smaller towns or companies might come in a bit lower. Nevertheless, this gives you a solid idea of how these roles stack up in terms of earning potential. It's also worth noting one more thing. So data engineers and data scientists often require a deeper technical background. Uh you can think of advanced
6:53 programming, machine learning or large scale data architecture. And this generally means you will need a few extra years of study and some projects or both uh which naturally contributes to the highest salary range. Data analyst on the other hand can often get started with a more foundational understanding of data like SQL or Excel or visualization tools and then grow their skill set as they progress on their careers. Let's move on to key tools and technologies. So for data engineers popular choices include SQL for querying, Python or Scala for building data pipelines and frameworks like Apache, Spark and Adoop for large scale data processing and also cloud platforms such as AWS, Azure or Google Cloud are also common for hosting data infrastructure. For data analysts, well, proficiency in SQL, spreadsheet tools like Excel and visualization platforms
7:38 like Tableau, PowerBI or Luker are typically considered musthaves. And for data scientists along with Python, often using libraries like pandas, numpy, psycharn, pytorch or tensorflow. Expertise in R for statistical analysis is a plus. And familiarity with cloud computing like AWS as your or Google cloud for model deployment and data retrieval is also increasingly common. I also want to touch base on one important point which is industry confusion. So the reality is that titles and responsibilities vary widely from one organization to another and that often creates confusion about what a given data role really does. Sometimes the HR departments or recruiter who might not have a deeper technical knowledge writes a job description that lists every data buzzword they can find even though the
8:24 actual role might not require half of those skills. In other cases, companies hire a data scientist but don't have the infrastructure ready for machine learning projects. So that person ends up doing mostly basic reporting tasks. So uh another company might hire a data scientist. We actually building cuttingedge AI models full-time. It all comes down to how each company structures and name its roles. Which is why it's crucial to look past the job title and dig into the actual tasks and expectations. All three roles, data engineer, data analyst and data scientist are incredibly valuable and can lead to fulfilling careers. Each involves a unique blend of skills and interest. However, if I have to bet on one for the future, and this might be controversial and I would love to know
9:09 uh what you think in the comments. I would lean towards the data analyst role. The reason is simple. Analysts often act as the bridge between raw data and decision makers, which puts them in a prime position to communicate insights, influence strategy, and directly shape business outcomes. Data scientists and data engineers might be at the cutting edge of technical innovation, which is awesome, but they can sometimes feel a step removed from the urgent day-to-day needs of executives and stakeholders. Plus, as AI continues to evolve, many of the purely technical or repetitive data task may become more automated. While the data analyst role, uh, communicating with real people, spotting context specific trends, and translating data into meaningful action might be harder for AI
9:54 to fully replicate. If you love both the numbers and the human side of business, the analyst role could offer the best balance of impact and job security in this rapidly changing landscape. Again, I would love to know what you think about this in the comments down below. And there you have it, a simple guide on all you need to know about the three most in demands data roles out there. As always, if you found at least one useful information in this video, make sure to subscribe to my channel so that I can help you even further in the next videos. And if you're interested about starting in data analytics, check out my data master course in the video description. We have hundred of students who love it and it's the only course you need to get a job in data. And in case you want a video to learn more about my predictions for the future of data analytics, make sure to check it out here at the link that you see in the
10:40 screen. And well, enjoy the rest of your day. Chia for now and see you in my next
