Ever wondered how hospitals use algorithms to more accurately diagnose patients, how universities determine the number of staff they need to hire or how tech companies identify patterns in users’ behavior? The answer is data science, and it’s one of the most exciting new fields of the 21st century.
Discover what data science actually is, how it’s being used across a wide range of industries and the tools data scientists use on a daily basis.
What Is Data Science?
An interdisciplinary field is one that draws knowledge and information from multiple other fields. And since data science includes aspects of fields like mathematics, statistics, data analysis, programming and science, it certainly qualifies.
More specifically, data science involves using techniques from all those fields to extract useful insights from vast amounts of data.
Since it’s a relatively new field, the tasks performed and methods used by data scientists can vary. But in general, data scientists will spend their time:
- gathering large volumes of data;
- devising new and improved methods for identifying patterns;
- cleaning the data they’ve collected;
- analyzing and extracting key information from data; and
- using programming languages to create machine learning models which can efficiently process data.
If you think that sounds similar to the work of a data analyst, you’re not wrong—the difference when comparing data analytics vs. data science is that data scientists take things to the next level by using advanced techniques, specialized tools and the scientific method to find the answers they’re looking for.
Data Science Applications
Since data can be gathered from just about any human activity, it makes sense that data science has a nearly endless number of applications across a wide range of industries.
When working for corporations, financial companies or banks, data scientists can use their skills to:
- gather data based on customer behavior;
- predict market trends;
- increase security; and
- analyze internal finances.
In a healthcare setting, data scientists can help:
- collect information about patients and their health;
- create more effective treatment strategies;
- streamline staff workloads; and
- develop ways to better identify illnesses and injuries.
Data scientists working in the transportation industry can:
- reduce traffic congestion;
- identify hazards and improve safety measures;
- create more efficient routes; and
- reveal customers’ behavior and preferences while traveling.
From elementary schools to universities, data scientists in the field of education can help to:
- identify students’ behavioral patterns;
- evaluate the efficacy of curriculum changes;
- find the optimal student-faculty ratio; and
- create reports on student and instructor performance.
Science and Technology
Since data science is a type of science, it’s only natural that it should have a place in science and technology. In those fields, data scientists:
- derive insights from user data;
- gather and interpret the results of large-scale experiments;
- identify patterns in users’ behavior; and
- build customized machine learning models that can help make sense of new scientific breakthroughs.
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Data Science Tools
The scope of data scientists’ work can be broad, so they can use a wide array of tools to get the job done. While a great number of specialized options exist, the following are some of the most widely used.
With uses ranging from website development to blockchain creation, Python is a programming language that’s almost as multi-purpose as data science itself.
By using Python for data science, it’s possible to calculate probabilities, create models, make predictions and much more.
Another programming language that lends itself well to data science is R. As the R Foundation puts it, R is “a language and environment for statistical computing and graphics” that includes “software facilities for data manipulation, calculation and graphical display.”
With R, data scientists can store, analyze and visualize large amounts of data, all in a single environment.
Apache Spark and Hadoop
Two offerings from The Apache Software Foundation, Spark and Hadoop, are commonly used by data scientists to glean insights from huge datasets. Spark is a multi-language engine for data analytics, while Hadoop is a framework that enables distributed large-scale data processing across multiple clusters of computers.
By combining the powers of both Spark and Hadoop, data scientists can effectively store, analyze and extract insights from even the most colossal datasets.
The programming and numeric computing environment MATLAB has a variety of applications. It can be used to perform tests, train machine learning models and create scripts, as well as a slew of other functions.
With MATLAB, data scientists can efficiently organize, clean, explore and visualize data, and also utilize its machine learning, app building and programming capabilities as needed.
The Alteryx Analytics Automation Platform, or just Alteryx for short, was specifically designed for data science projects.
Its user-friendly design makes it easy for data professionals to import, prepare, cleanse, filter and interpret data. It also features low-code and no-code technology, so even people unfamiliar with programming can quickly learn to use it.
The New Frontier of STEM
With its incredible versatility, surprisingly accessible fundamentals and ability to make sense of seemingly endless seas of information, data science is on the cutting edge of every major industry.
Best of all, many of the tools that real data scientists use are completely free. So with a little guidance and a healthy dose of creativity, anyone can learn data science and be a part of STEM’s new frontier.
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