Data Science vs Machine Learning

Data Science vs Machine Learning; Which Is Better?

Data science and machine learning are buzzwords in the technology world. Both
enhance AI operations across the business and industry spectrum. But which is
best?

Technology is the backbone of the world. It’s evolving at an unprecedented
pace and serves as an enabler across all industries. In the last decade or so,
data science and machine learning have become popular terms, from tiny
start-ups working on the next big app, to giants like Google, Facebook, and
Netflix.

Data science and machine learning are terms that are sometimes (wrongly) used
interchangeably, but they have several fundamental differences and
applications.

Both terms and their functions exist as a part of Artificial Intelligence
(AI). Machines use AI to make decisions, just like a human would, based on
experiences and heuristics. These experiences are based on data, which is
where Machine Learning (ML) comes in. Humans learn from their everyday
experiences, while machines learn from data.

The data needed for machine-based learning comes from big data. One
organization alone can produce petabytes of data in a short timeframe. While
the accessibility of cloud-based storage makes it easier to store data, the
problem now is squarely on the processing of this data to make better business
decisions. Data science and machine learning play a critical role in this
process.

Modern AI can take massive amounts of data, and analyze and process it to
unearth patterns of human consumption and behavior, or to answer other
questions a business may want to explore. Data science powers the data
analysis performed by machines, providing all the inputs needed to create
relevant algorithms and models. Simply put, data science utilizes various
algorithms, protocols, and methods to extract insights from raw data.

With this understanding of data science and machine learning, it’s now easier
to understand their differences.

The Differences Between Data Science vs. Machine Learning

Data science and machine learning each have practical applications that are
quite different. However, both are used to carry out everyday activities —
some which happen millions of times a day, such as online shopping.

Consider a business called ABC that’s selling a new product, such as a pair of
sunglasses. The sunglasses are readily available from Company ABC — but also a
range of competitors. When a potential customer visits ABC’s website for the
first time and browses through all the versions of sunglasses that are
available, they often use filters provided by ABC to narrow down options based
on their preferences. Common filter options include size, color, price, and
style.

After filtering the sunglasses by features, the customer is left with three
options that fit their criteria. Once the customer makes a choice, they might
add it to their cart.

ABC’s website will then offer various options and recommendations to the
potential customer based on their preferences and insights gained from
processing vast amounts of big data. Customers might see additional products
listed under headlines such as, “We Also Recommend”, or “Customers Who Bought
This Also Bought”. These recommendations are based on information gathered
from millions of previous purchases.

Purchasing a tablet? You might want to buy a new case or an extra-long
charging cable.

The suggestions not only provide the customer with helpful products, they also
provide the business with a successful upselling model. This is data science:
The entire process of collecting, sifting, processing, extracting actionable
trend patterns, and creating a model to arrive at an answer to a question. In
this case, the model provides the customer with better alternatives or may
influence them to buy a related product.

The model, on the other hand, is the machine learning function. Data
scientists build the model with algorithms that convert data into a learning
experience — in this case, providing customers with recommendations based on
their search criteria. These models enable a machine to learn what product
options to show a new customer based on knowledge gleaned from earlier
purchasers. It makes a suggestion based on its “experience” from the provided
data.

The example above is just one example of an ML application, but there are
millions more for every industry — from medical and research fields to retail
and insurance.

For example, in Fintech, ML is used to predict a range of behaviors. It
analyses transactions in real time and identifies complex patterns that
predict fraudulent behavior. ML also assesses past financial transactions from
individuals during the loan application process. It combines knowledge gained
from previous loan defaulters and uses them to make accurate predictions about
the likelihood of someone paying their loan as agreed.

And this segues into data modeling — the next stage of machine learning within
the data science cycle.

The quality of the model determines how much the machine learns about customer
buying habits. The better the model, the better the machine can predict future
decisions. The ideal machine model ensures progress for both the business
model and the learning process of the machine, which leads to businesses
seeing an improvement in targeted outcomes.

Data science deals with the visualization of processed data based on certain
parameters, enhancing business decisions. Machine learning places the
spotlight on enhancing its experience, from learning algorithms and from
learning derived from its experience with data in real-time. Data will always
remain central to data science and machine learning.

Comparing Data Science and Machine Learning

With this understanding of their application in real life, this is how these
two concepts differ from each other.

Choosing Between Data Science and Machine Learning

How does a company choose between data science and machine learning? The
answer is that an organization can’t have one without the other. Both these
processes are a part of each other. Machines can’t gain experience without
data, and data is always better analyzed when processed within the standards
of data science. In the future, specialists such as data scientists and
machine learning engineers will need to have at least a working understanding
of each other’s fields to improve the quality of the work that they do.

As AI increasingly becomes essential for organizations to succeed in the real
world, data science and machine learning both have the spotlight on them.
Advancement in the field is moving into deep learning, a part of AI and a
subset of machine learning. Modeled on the way the neurons of the human brain
fire and function, deep learning makes use of digital neural networks to
operate. It offers multiple layers of solutions to solve complex business
challenges. Self-driving cars are a great example of deep learning. Sources of
data are constantly expanding and the need to collect and analyze it will
continue to grow.

How To Capitalize On Data Science and Machine Learning In Your Organization

Your organization needs data science and machine learning to remain
competitive, relevant, and productive. The insights gained from application of
data science principles can guide the organization forward into the future;
accurate predictions allow data informed decisions that guarantee results. If
your organization has amassed data that it doesn’t know what to do with, or if
you’re falling behind the competition,
Alteryx
will give you the data science jumpstart you need.

Start today to realize the benefits of data science and machine learning in
your organization.