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 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 various job opportunities, from researching to computing. In this article, you will learn about real-world data science use, the job outlook for the field, its required skills, and what credentials you need to land a job. 

Read more: Data Scientist Salary Guide: What to Expect

What is data science?

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 and advanced analytics to find patterns, create predictive models, and develop insights that guide business decision-making. 

Real-world 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 surrounding us: 

Read more: Data Analyst vs. Data Scientist: What’s the Difference?

1. Advertising and marketing

Data science models are essential in advertising, from display advertisements on numerous websites to digital posters at airports.

One of the most common ways that data science is used in marketing is when people Google a term and algorithms create relevant search results, including targeted ads related to your query. This application of data science is why you may see an online advertisement for data science training programs, while someone else in the same region may see an advertisement for clothes. 

Businesses can customize data science models based on the user's previous behaviour. This is why digital advertisements have a far higher click-through rate (CTR) than traditional advertising. Many marketing firms have taken advantage of data science to promote their offerings based on users' interests and data relevance to enhance customer engagement. Companies like Amazon, Twitter, Google Play, Netflix, and others also adopt this strategy.

2. Health care

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

• Medical image analysis

• Genomics and genetics

• Pharmaceutical research and development

• Bots for health and virtual assistants

Even medical operations have benefited from data science innovations. Employing a range of methods and frameworks, such as MapReduce, medical professionals have used data science 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.

3. E-commerce

Data science has impacted the e-commerce sector in various ways, helping businesses identify their target market, anticipate goods and services, and optimize price formations.

Natural language processing (NLP) and recommendation engines, in particular, have proven extremely beneficial to e-commerce companies, which employ these methodologies to analyze customer purchases and gain insight into potential growth strategies. Furthermore, NLP analyzes texts and online surveys, which helps businesses provide quality services to their customers. 

4. Transportation

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

While self-driving vehicles are an eye-catching data science development in 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.

Job outlook and salary expectations

According to the Government of Canada Job Bank, the median hourly salary for a data scientist is $39 CAD [1]. The highest median hourly wage by area is $56.41 CAD, and the lowest is $23.08 CAD. Your wage will vary by the province or territory where you work, your experience, the industry you work in, and more. Glassdoor lists an average salary for a data scientist at $91,379 CAD annually [2].

The job outlook is “Good” in most areas, but two outliers are Quebec and Prince Edward Island, which are “Very Good” and “Moderate,” respectively [3].

Job opportunities in data science

Many jobs are available in the field of data science. Some of the most common positions include: 

• Data analyst $64,984 CAD [4]

• Machine learning engineer $94,983 CAD [5]

• Data engineer $91,633 CAD [6] 

• Database administrator $84,046 CAD [7]

• Product analyst $66,569 CAD [8]

• Financial analyst $68,511 CAD [9]

• Data system developer $74,036 CAD [10]

Educational requirements

To qualify as an entry-level data scientist, you'll likely need a bachelor's degree in data science or a related field, such as computer science or mathematics. Some jobs may even require a master’s or doctoral degree. Jobs will typically require some computer programming experience too.

Common certifications  

Whether you want to get a certification or certificate through an approved organization, 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

• Microsoft Certified: Azure Data Scientist Associate

• Open Certified Data Scientist (Open CDS)

• SAS Certified Data Scientist

The following are certificates for data science: 

• DeepLearning.AI TensorFlow Developer Professional Certificate

• IBM Data Science Professional Certificate

Data science skills and tools

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

Workplace skills

• Receptivity/open-mindedness

• Communication

• Empathy

• Business acumen

• Team spirit

• Innovation

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, such as Julia, IBM SPSS, D3.js, and Apache Spark [11].  

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.