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Data science is a form of applied statistics that incorporates elements of computer science and mathematics to extract insight from both quantitative and qualitative data.
What Is Data Science?
Data science is a form of applied statistics that incorporates elements of computer science and
mathematics to extract insight from both quantitative and qualitative data.
Tools and technologies used in data science include machine learning algorithms and frameworks, as well
as programming languages and visualization libraries.
A data scientist combines programming, mathematics, and domain knowledge to answer questions using data.
Why Is Data Science Important?
Data science practices keep businesses competitive and more productive.
Organizations that prioritize data science uncover trends and opportunities that might have gone unrealized had
they chose not to tap into the data available to them. The insights gained from data science can have a
tremendous impact on business outcomes.
Data science extracts useful information from both big datasets and small datasets. Although large amounts of
data are needed to train artificial intelligence (AI) systems, data science can still help with small datasets.
For example, retailers used to forecast inventory for their stores based on same-store sales. When the COVID-19
pandemic caused stores to close, retailers had to change their forecasting methods as the amount and type of
data available changed.
When there is only a small amount of data to look at, data science uses practices like data augmentation,
synthetic data generation, transfer learning, and ensemble learning to supply insights.
Data science also enables an organization to build additional resiliency. In this rapidly changing, technological
world where things can change at a moment’s notice, businesses need to be able to adapt and respond quickly in
order to survive — and data science can help facilitate that.
Data science is leveraged by many organizations and has so many industry-specific applications. Organizations
that don’t leverage it risk falling behind — or shutting down all together.
Data Science Lifecycle
Data science is a cyclical process. The lifecycle can be broken down into the following steps:
Data Science Applications Across Different Industries
Companies use data science every day to improve their products and internal operations. Almost any type of
business in any industry can benefit from practicing data science.
Some example use cases include:
- An energy software company using recommendation models to match eligible customers with new or existing energy products
- A financial services company using machine learning models to reach prospective customers that may have been overlooked by traditional banking institutions
- A car sharing company using dynamic pricing models to suggest prices to the people who list and rent out cars
- A higher education institution combining data from transcripts, standardized test scores, demographics and more to identify students at risk of not graduating
- A fintech company using a combination of complex data lookups and decision algorithms to assess whether a loan applicant is fraudulent
Dive into each of these use cases in this whitepaper Data Science in Practice: Five Common Applications.
Business Intelligence vs Data Science
While data science has significant business applications, its focus is broader and tactics more diverse than business intelligence.
Business intelligence leverages statistics and visualization tools against traditional structured data to
describe and present current and historical trends in a way that’s easy for people to consume and understand.
Data science leverages these approaches as well as machine learning against both structured and
unstructured data to investigate relationships and discover likely outcomes or optimal actions.
While business intelligence’s most typical output is some form of report or dashboard (thus informing a
human, who will make a best-estimate decision), data science produces decisions and actions that can be
Who Can Use Data Science?
Despite what many think, data scientists aren’t the only ones who use data science. In reality, anyone can do
data science. Thanks to technology advancements, data science no longer requires specialized coding knowledge or
advanced statistical know-how. “Drag-and-drop” data science is now a widely-accepted and viable form of data
science, giving analysts and other data workers the power to build and deploy models at scale. These “citizen
data scientists,” or data workers who can wield advanced analytics without knowing the intricacies of the
back-end processes, are a highly sought-after demographic of worker.
Because data science is so in demand, because traditional data scientists often command high salaries, and
because their limited number can create bottlenecks, citizen data scientists are seen as a data science
multiplier. With appropriate checks in place, citizen data scientists can largely ramp up model production in
any corporation, driving insights and revenue that would otherwise be impossible.
How to Get Started With Data Science
Alteryx Analytics Automation Platform allows you to build automated and
repeatable workflows that can make the larger data science process easier and more efficient. Data access,
preparation, modeling, and sharing of analytic results all happens in the same place, on one easy-to-use
You can also learn how to integrate Alteryx with
Snowflake, a cloud-based data storage and analytics tool,
using our starter
kit. Using the two
together makes it easy to drive analytic and data science outcomes in the cloud.