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's 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 expertise in software engineering, and programming. Data analytics skills
include business intelligence tools, solid statistics knowledge,
intermediate programming 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 while understanding the impacts that external factors have on the patient's health. This knowledge allows data scientists to uncover illuminating questions about a 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 expertise - say the heart or 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.

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

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