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. Data scientists use advanced machine learning algorithms to sort through, organise and learn from structured and unstructured data to create prediction models. 

The Government of India launched a programme called Digital India in 2015 with the mission to increase digital access and promote digital growth across the country. As the relevance and accessibility of big data have skyrocketed, so has the growing need for professionals who can work with the available data and make tangible impacts. Because of this, data science offers a variety of job opportunities—from researching to computing. 

Discover how data science is used in the real world, as well as salary expectations for jobs in the field, required skills, and what credentials you need to get started in a data science career.

What is data science?

Data science is the field of study that uses scientific methods, algorithms, and programming to extract knowledge and insights from data. It's the process of collecting, analyzing, and interpreting large amounts of data to uncover hidden patterns and trends. Data scientists construct questions around specific data sets and then use tools such as data analytics, machine learning, and artificial intelligence to find patterns, create predictive models, and develop insights that guide business decision-making.

Read more: Data Science vs Data Analytics: Which Should You Choose?

Data science real-world applications

Data science is often concerned with making sense of abstract data points, but you can see its impact in concrete, everyday situations. Consider these familiar examples of data science or data science-powered services found in the world around you: 

1. Advertising and marketing

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

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 algorithms 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.

Read more: 7 Machine Learning Algorithms to Know: A Beginner's Guide

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. Data science employs various methods and frameworks, such as MapReduce, to identify malignancies, artery stenosis, and organ demarcation. Support vector machines (SVM), content-based health care image indexing, and wavelet analysis are machine-learning approaches used to classify solid textures.

Read more: Health Care Analytics: Definition, Impact, and More

Genomics and genetics

Data science implementations also 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 understand better how DNA affects human health.

Pharmaceutical research and development

Data science techniques and artificial educational algorithms ease and streamline pharmaceutical research and development, 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.

Read more: What Is Machine Learning in Healthcare? Applications and Opportunities

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. 

Read more: Digital Health Explained: Why It Matters and What to Know

3. E-commerce

Data science has impacted e-commerce in various 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 highly beneficial to 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.

Read more: E-commerce Platforms: How They Compare + How to Choose One

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 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 ride-hailing apps like Ola use data science to optimise cost and completion routes by combining various 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.

How much does a data scientist earn?

According to Glassdoor, the mean annual salary for data scientists is ₹12,00,000 in India [1]. However, reported salaries range from ₹8,00,000 to ₹18,70,000 depending on location, experience, company, and job responsibilities.

Job opportunities in data science

Many jobs are 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 

How to get started in data science

Embarking on a career in data science involves acquiring the right education, certifications, skills, and tools. Follow this guide to help you navigate your career path.

Educational requirements

To qualify as an entry-level data scientist, you'll typically need a bachelor's degree in data science or a related field, such as computer science. Some jobs, though, may require a master's degree.

Data science certifications and certificates

Certifications and certificate programmes are excellent ways to gain in-demand data science skills and add compelling credentials to your resume. Whether you want to get a certification or certificate through an approved provider, 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. Explore these commonly acquired certifications and certificates for a career in data science:

• Cloudera Certified Professional (CCP) Data Engineer

• Dell EMC Data Science Track (EMCDS)

• Google Data Analytics Professional Certificate

• 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

As a data science professional, you need to have a well-honed set of human and technical skills to ensure you do the best possible job. Some top skills you’ll need to be successful include: 

Human skills

• Receptivity and open-mindedness

• Communication

• Empathy

• Team spirit

• Innovation

Technical skills

• Linear algebra

• Machine learning techniques

• Programming languages

• Data visualisation tools

• Multivariable calculus

• Statistics

• Identifying algorithms

• Creating and maintaining algorithms

• Information retrieval data sets

Does data science require coding?

Yes, data scientists typically need programming skills to manipulate and analyse data, build models, create visuals, and automate tasks. However, although advances in user-friendly data analysis tools and graphical interfaces make it easier to complete basic data projects without writing code. Still, some of the most useful programming languages for data science professionals to learn include Python, R, SQL, Java, and Scala.

Data science tools

Data scientists are vital decision-makers who evaluate and manipulate massive amounts of unorganised and organised data. To do so, data scientists employ various tools and computer languages, the most common of which include such programmes as SAS, Excel, Tableau, and Apache Spark.  

Getting started with Coursera

Data science empowers businesses to glean valuable insights from massive data sets using sophisticated algorithms. This analysis fuels informed decision-making across various sectors like health care, e-commerce, and transportation. If you're interested in a data science career, you can pursue a variety of job roles by earning a bachelor's degree, earning certifications and professional certificates, and developing your technical and workplace skills. The first step to entering the data science field is to gain the technical skills you need for the job. If you're just starting, consider completing the Google Data Analytics Professional Certificate on Coursera. In less than six months, you can gain in-demand data analysis skills, learn how to organise and manage big data sets, and build job-ready data science skills.