What Is Data Science?

Written by Coursera • Updated on

Data science is an in-demand career path for people with an aptitude for research, programming, math, and computers. Discover real-world applications and job opportunities in data science and what it takes to work in this exciting field.

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Data science focuses on the creation of cutting-edge tools and procedures that uncover hidden patterns, generate useful data, and inform appropriate actions from large amounts of data. To create prediction models, data scientists use advanced machine learning algorithms to sort through, organize and learn from structured and unstructured data. 

This is a growing career field with many opportunities across different industries. If you’re interested in research or computing, a career in data science may be right for you. 

Real-world applications

Data science is highly technical. However, there are familiar examples of data science or data science-powered things all around us. 

1. Advertising and marketing

The digital marketing sphere is based on the most essential data usage. In other words, when you Google a term, algorithms create relevant search results as well as targeted ads related to your search. From display advertisements on numerous websites to digital posters at airports, data science models are essential in modern advertising.

This is why digital advertisements have a far higher CTR (call or click-through rate) than traditional advertising. Businesses can customize them based on the user's previous behavior.

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. 

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. In the health care industry, data science is making significant progress by the day. It is employed in a wide range of areas, which include:

  • Medical image analysis

  • Genomics and genetics

  • Pharmaceutical research and development

  • Bots for health and virtual assistants

Operations including identifying malignancies, artery stenosis, and organ demarcation employ a range of methods and frameworks, including MapReduce, to find suitable parameters for duties like lung texture classification. Support vector machines (SVM), content-based health care image indexing, and wavelet analysis are among the machine learning approaches used to classify solid textures.

  • Genomics and genetics

Data science implementations also provide a better level of therapy customization 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 this approach, giving a new perspective at every stage, from the initial screening of medicinal substances to the forecast of the rate of success based on biological characteristics.

Besides "lab tests," these algorithms use sophisticated mathematical modeling and simulations to forecast how the 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, which are commonly 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.

3. E-Commerce

Natural language processing (NLP) and recommendation engines, for example, are extremely beneficial to the e-commerce industry. E-commerce companies could employ such methodologies to analyze customer purchases and comments to get convenient data for their business growth.

Texts and online surveys are analyzed using NLP. It is used in content-based and collaborative screening to analyze data and provide quality services to its users.

Data science has impacted the e-commerce sector in a variety of ways, including understanding the target market, anticipating goods and services, determining the form of common products, and optimizing price formations. 

4. Transportation

The emergence of self-driving vehicles is by far the most notable data science development in the world of transportation. Data scientists have developed a foundation in transportation through a thorough examination of fuel usage statistics, driver conduct, and vehicle monitoring.

Data science is key in establishing safe driving conditions, boosting vehicle performance, providing drivers more flexibility, and more. By combining reinforcement learning with automation, car manufacturers may create smarter vehicles with better logistical routes.

Prominent taxi companies like Uber use data science to optimize cost and completion routes, as well as allocation of resources, by combining a variety of elements like customer behavior, location, economic data, and logistic providers.

Airlines use data science to predict delayed flights, choose which aircraft to purchase, plan routes, manage flight delays, and create loyalty programs.

Those are just a few of the industries using data science to develop everyday strategies for success. Science, research, and data are everywhere and touch nearly every aspect of modern life.

Job outlook and salary expectations

As per the US Bureau of Labor Statistics, the average annual salary for data scientists is $103,930 [1].

Data scientists as well as other computational and data research jobs are expected to grow 15% between 2021 and 2029, per the US Bureau of Labor Statistics. It is indeed a substantially quicker rate of job expansion than the national average of 4% for all professions [1].

Job opportunities in data science

The following are common job opportunities in the data science sector:

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

Recommended skill sets

Successful data scientists are proficient in a variety of human skills and technical skills. Having a full skill set will prepare you for a successful career in data science. 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 that are in charge of evaluating and manipulating massive amounts of unorganized and organized data.

To accomplish so, as a data scientist, you'll need a variety of tools and computer languages for data science to help him fix the day, exactly as you or your client wants it. The most common data science tools include programs like SAS, Excel, Tableau, and Apache Spark [2].  

Educational requirements

To qualify as an entry-level data scientist, you'll want to have a bachelor's degree in a data science or computer science area. However, many data science jobs require a master's degree.

Common certifications

Regardless of 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

Next steps

If you are wondering how to become a data scientist and what the next steps are, you first need to focus on your education. For many data scientists, the investment in their education is one of the best investments that they ever make.

Degrees in computer science, data science, IT, math, or business can all help. Consider the degrees and courses offered by top universities and organizations on Coursera that can help you develop the skills you will need as a data scientist, like the Google Data Analytics Professional Certificate.

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Article sources

  1. US Bureau of Labor Statistics. “Occupational Employment and Wage Statistics, https://www.bls.gov/oes/current/oes152098.htm.” Accessed March 25, 2022. 

  2. Data Flair. “14 Most Used Data Science Tools for 2022- Essential Data Science Ingredients, https://data-flair.training/blogs/data-science-tools/.” Accessed April 8, 2022. 

Written by Coursera • Updated on

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