What Is Data Science? Definition, Examples, Jobs, and More

Data science is an interdisciplinary field that uses algorithms, procedures, and processes to examine large amounts of data in order to uncover hidden patterns, generate insights, and direct decision-making. To create prediction models, data scientists use advanced machine learning algorithms to sort through, organize, and learn from structured and unstructured data.

As a fast-growing field with applications across numerous industries, data science offers a variety of job opportunities—from researching to computing. In this article, you will learn about how data science is used in the real world, the job outlook for the field, its required skills, and what credentials you need to land a job.

Data science definition

Data science is the study of data, much like marine biology is the study of sea-dwelling biological life forms. Data scientists construct questions around specific data sets and then use data analytics and advanced analytics to find patterns, create predictive models, and develop insights that guide decision-making within businesses.

Data science applications

Although data science is often concerned with making sense of abstract data points, its impact can be seen concretely throughout the world today. Here are some familiar examples of data science or data science-powered services that can be found all around us:

1. Health care

Data science applications are especially beneficial to health care, where its used for a wide range of purposes, including:

Even medical operations have benefited from data science innovations. Employing a range of methods and frameworks, such as MapReduce, data science has been used to identify malignancies, artery stenosis, and organ demarcation. Support vector machines (SVM), content-based health care image indexing, and wavelet analysis are among the machine-learning approaches used to classify solid textures.

2. E-commerce

Data science has impacted the e-commerce sector in a variety of ways, helping businesses identify their target markets, anticipate goods and services, and optimize price formations. Natural language processing (NLP) and recommendation engines, in particular, have proven extremely beneficial to the e-commerce companies that employ these methodologies to analyze customer purchases and gain insight into potential growth strategies. Furthermore, NLP is used to analyze texts and online surveys, which helps businesses provide quality services to their customers.

3. Transportation

Data science has been at the center of some of the most impactful innovations in transportation over the past two decades.

While self-driving vehicles are likely the most eye-catching data science development in the world of transportation, data scientists have also been foundational to producing fuel usage statistics, analyzing driver conduct, and monitoring vehicular performance. By combining reinforcement learning with automation, car manufacturers may create smarter, safer vehicles with better logistical routes.

Data science salary and job outlook

Data scientists earn a higher-than-average salary and have a positive job outlook. According to the US Bureau of Labor Statistics (BLS), the mean annual salary for data scientists is $108,660 [1]. Furthermore, the BLS projects that data scientists, as well as other computational and data research jobs, will see 21% job growth between 2021 and 2031, resulting in about 3,300 new job openings a year [2].

Data science jobs

There are many jobs in the field of data science. Some of the most common positions include:

Educational requirements

To qualify for an entry-level data scientist role, you’ll most likely need a bachelor’s degree in data science or a related field, such as computer science. But, some jobs may require a master’s degree.

Common certifications

Whether you want to get a certification through an approved university, gain more training as a recent graduate, improve vendor-specific abilities, or showcase your skills in data analytics, there’s likely a useful certification program for you. The following are commonly acquired certifications for a career in data science:

Cloudera Certified Professional (CCP) Data Engineer
Dell EMC Data Science Track (EMCDS)
Google Professional Data Engineer Certification
IBM Data Science Professional Certificate
Microsoft Certified: Azure Data Scientist Associate
Open Certified Data Scientist (Open CDS)
SAS Certified Data Scientist
Tensorflow Developer Certificate

Data scientists need to have a well-honed set of workplace and technical skills to ensure they do the best possible job. Some top skills you’ll need to be successful include:

Workplace skills

Technical skills

Linear algebra
Machine learning techniques
Multivariable calculus
Statistics
Identifying algorithms
Creating and maintaining algorithms
Information retrieval data sets

Tools

Data scientists are key decision-makers tasked with evaluating and manipulating massive amounts of unorganized and organized data. To do it, data scientists employ a variety of tools and computer languages, the most common of which include such programs as SAS, Excel, Tableau, and Apache Spark [3].

Next steps

The first step to entering the data science field is to gain the technical skills you need for the job. If you’re just getting started, consider obtaining IBM’s Data Science Professional Certificate to build in-demand data science skills like Python, SQL, data visualization, and more in less than six months. The beginner-friendly Professional Certificate requires no prior experience or education.

Source