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, organise, 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.

Real-world 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. Advertising and marketing

From display advertisements on websites to digital posters at airports, data science models are essential in modern advertising.

One of the most common ways that data science is employed in marketing is when you 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 programmes, while someone else in the same region may see an advertisement for clothes. 

Businesses can customise them based on the user's previous behaviour. This is why digital advertisements have a far higher CTR (call or click-through rate) 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:

• Genomics and genetics: Data science implementations provide a better level of therapy customisation via genetics and genomics analysis. The goal is to identify particular molecular linkages between genetics, disorders, and pharmaceutical responses to gain a better understanding of how DNA affects human health. 

• Pharmaceutical research and development: Data science techniques and artificial educational algorithms ease and streamline pharmaceutical R&D, giving a new perspective at every stage, from the initial screening of medicinal substances to forecasting the rate of success based on biological characteristics. Besides "lab tests," these algorithms use sophisticated mathematical modelling and simulations to forecast how a drug will react inside the body. Algorithmic drug development aims to create computational prediction models in the form of a biologically suitable network, making it easier to predict future outcomes with high precision.

• Bots for health and virtual assistants: AI-powered mobile applications, such as chatbots, can give basic health care assistance. Simply describe your health concerns or ask any health-related questions, and you'll receive critical information on your health status based on a large network of clinical symptoms and signs. Apps can remind you to take your prescription on time and, if necessary, make a doctor's appointment.

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.

3. E-commerce

Data science has impacted the e-commerce sector in a variety of ways, helping businesses identify their target market, anticipate goods and services, and optimise 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 analyse customer purchases and gain insight into potential growth strategies. Furthermore, NLP is used to analyse texts and online surveys, which helps businesses provide quality services to their customers.

4. Transportation

Data science has been at the centre 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, analysing driver conduct, and monitoring vehicular performance. By combining reinforcement learning with automation, car manufacturers may create smarter, safer vehicles with better logistical routes.

Prominent taxi companies like Uber use data science to optimise cost and completion routes by combining a variety of elements like customer behaviour, location, economic data, and logistic providers. Airlines, meanwhile, use data science to predict delayed flights, choose which aircraft to purchase, plan routes, manage flight delays, and create loyalty programmes.

Job outlook and salary expectations

In the United Kingdom, the median annual pay for a data scientist is £55,747, according to Glassdoor (August 2023) [1]. This figure is based on reported data scientist salaries from Glassdoor users in the UK and represents the midpoint between the 25th and 75th percentiles for the salary range. 

As the amount of data continues to grow, the need for qualified data scientists can barely keep up. This need spans many sectors, so as a data scientist, you would have a variety of workplace options.

Job opportunities in data science

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

1. Data analyst

2. Machine learning engineer

3. Data engineer

4. Data scientist

5. Database administrator

6. Business analyst

7. Product analyst

8. Financial analyst

9. Data system developer 

Educational requirements

To qualify as an entry-level data scientist, you'll most likely need an undergraduate qualification in data science or a related field, such as computer science. Some jobs, though, may require a postgraduate qualification.

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 programme 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

Recommended 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: 

Human skills

• Receptivity/open-mindedness

• Communication

• Empathy

• An approach to business

• 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 unorganised and organised data. To do it, data scientists employ a variety of tools and computer languages, including SAS, Excel, Tableau, and Apache Spark.  

Next steps

Start building job-ready skills for an in-demand career in data with the Google Data Analytics Professional Certificate. Learn at your own pace from anywhere with an internet connection, and earn a credential for your resume in less than six months.