Data Scientist vs Data Analyst - Which Is Right For You? (2024)

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Hey, I've got a question for you. What's the difference between a data analyst and a data scientist? Well, in this video, we're going to give you the answers. From health care to ride hailing apps, online shopping to streaming services - big data has transformed applications and the way in which we interact with them. Underpinning all of this is the emergence of two key fields: data analytics and data science. Before we get started, I want to know any questions that you have about data analytics or data science in the comments below. In this video, we'll cover what data science is, what data analytics is, the key differences between them and provide you with some final key takeaways. Let's dive straight in and look at data science first. Data science is a multidisciplinary field. That, as the name suggests, focuses primarily on data. As an area of scientific study, it can be applied in numerous areas, from finance to retail to e-commerce to healthcare and much more. As a multi-disciplinary field, data science brings together key skills ranging from data analytics and machine learning to computer science and artificial intelligence. The aim of data science in a nutshell is to research vast amounts of raw, unstructured data to devise strategic questions which will push an organization forward. If this sounds a bit ambiguous, it's okay. Data science is a highly varied and complex role, and a data scientist's exact responsibilities can vary between different organizations. As a rule, data science involves many complicated and interlinked tasks. It could involve data modeling, building algorithms from scratch, managing large teams and stakeholders, building and implementing new data structures, and generally being the go-to data expert in a given organization. The main takeaway, though, is a data scientist focus is less on the micro day-to-day concerns and more on asking long term macro strategic questions. For this reason, data science or data scientist is usually quite a senior role. Now, let's have a quick look at data analytics. Data analytics is a single discipline within the umbrella of data science, as well as being a standalone field in its own right. Whilst data science focused on answering broad strategic questions, data analysts usually have a more narrow and specialized role seeking out the answers to specific questions. For instance, a data analyst's job might involve identifying which particular product features users prefer. They might have to uncover how marketing spend improves conversion rates to help target it better. While data analysts require fewer skills than a data scientist, unlike data scientist, they probably have a better niche understanding of a particular area in a particular business. Rather than having a total oversight, they might work in a specific department like sales or marketing. One of the reasons that data science and data analytics is so confused is that they both work with big data. However, by the time that data analysts use this data, the data is usually organized into a more structured format suited to the specific question that the analyst needs to answer. For this reason, data analysts take a much more structured approach for analyzing data. Their process involves following a relatively strict series of steps using tools and techniques such as Python, SQL and data visualization software such as Tableau to collect, clean and analyze the data set. This process and these data tools helps data analysts provide actionable insights that a business can execute. These insights commonly support decision making. Now that we can define these two disciplines, we can ask the big question - data analytics versus data science. What's the difference? Many data experts start their career in data analytics before proceeding into data science. While the line between them is blurry at times, we can largely divide them as follows. Data science skills include data modeling, predictive analytics, advanced knowledge of math and statistics, and a high level of expertize in software engineering, and programing. Data analytics skills include business intelligence tools, solid statistics knowledge, intermediate programing skills, and the ability to explore data using SQL and Python. Data science focuses on the macro asking strategic level questions and driving innovation. Data analytics focuses on the micro finding answers to specific questions using data to identify actionable insights. Data science explores unstructured data using tools such as machine learning and artificial intelligence. Data analytics explore structured data using tools such as Microsoft Excel and data visualization software. This can all be a little bit hard to grasp. So let's use an analogy. Use your imagination for a moment and imagine that the business is a human body. In this case, a data scientist will be a general practitioner, whereas a data analyst would be a specialist consultant. Both have crucial roles in guaranteeing the health of the person. But in this case, the business. Firstly, the data scientist or GP's job is to take a holistic understanding of the entire patient. Now broadly they must know how different elements interact and work whilst understanding the impacts that external factors have on the patient's health. This knowledge allows data scientists uncover illuminating questions about patient or business's well-being that others might not ask. Meanwhile, the data analyst or specialist consultant in this analogy focuses on a particular body part or business area. The data analyst is capable of answering specific questions about their area of expertize - say the heart of the brain using specialist knowledge. As such, they can identify specific solutions to specific problems, such as heart palpitations, for example. However, the GP or data scientist will still take oversight of the patient's overall health. In short, data scientists and data analysts, both play vital roles in the healthy running of a business and both inform each other's work. However, despite overlapping skills their overall objectives differ. Let's give you some final key takeaways to round off this video. As we've seen, the distinction between the two professions is not always clear cut, which is why the terms are sometimes used interchangeably. The main takeaways are that data science is a scientific discipline that evaluates all aspects of unstructured data. It asks complex strategic questions and aims to drive innovation. Data analytics is a specific process for answering known questions. It uses existing structured data to provide actionable insights that drive decision making. Data science is generally considered more senior than data analytics, but data analysts might have more specific knowledge of a certain area than data scientists. If you're considering a new career in data analytics or data science, you're in luck. Whichever discipline feels right for you, both roles are in very high demand at the moment. A trend that doesn't look set to change anytime soon. If you want to get started, why not sign up to CareerFoundry's Free five day data analytics short course. The link for the course is in the description below. Thank you so much for watching. I hope this answers some of the key differences between data science and data analytics. If you enjoyed this video, I think you'll really enjoy this video because this is a deep dive into data analytics - what it is, what the industry is all about as a deeper dive into the profession. Filmed it a few months ago, but it's still a great intro for you. I recommend watching that and thank you so much for watching this video today.
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Channel: CareerFoundry
Views: 60,958
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Keywords: The Difference Between Data Analytics And Data Science, Data Science Vs Data Analytics, data analyst, data science, data analytics, data scientist, data analyst career, data analyst job, data scientist vs data analyst, data analyst skills, what is data science, data analytics career, data science career, data science tutorial for beginners, how to become a data analyst, data science learning, how to data science, What IS Data Science, What IS Data Analytics, CareerFoundry
Id: T08eJt9DlgU
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Length: 7min 1sec (421 seconds)
Published: Thu Mar 24 2022
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