Data science is an interdisciplinary field in which individuals extract insights from both structured and unstructured data using algorithms, scientific computing, processes, and statistics. An important aspect of receiving professional data science training to set yourself up for a career change is learning what kinds of fields and industries that training will help you find work in. Each industry will have different requirements and expectations for prospective employees, and you should consider these expectations when you start your training. This is true for students who have an idea of what kind of work they want to do and for students who aren’t yet sure what path they want to take after learning data science skills.

What is Data Science?

Data science is a broad field that encompasses the disciplines of mathematics, computer programming, and artificial intelligence (AI). Data professionals such as Data Scientists and Data Analysts use advanced techniques like machine learning algorithms to find patterns in a vast amount of information. This process can provide actionable insights to stakeholders, from Product Managers to C-suite executives.

Today nearly every sector requires data science expertise, whether public or private. Among the top sectors where data science is critical are health and wellness, retail, web and application development, banking and finance, and governmental agencies. The field continues to project dramatic growth over the next decade; Glassdoor even listed Data Scientist as number three in its 50 Best Jobs in America in 2022.

Read more about what Data Science is and why you should learn it.

What Can You Do with Data Science Skills?

Data science has so many applications in different industries that a comprehensive review could fill a book. Professionals as diverse as Business Analysts, Machine Learning Engineers, and Enterprise Architects use data science in their day-to-day activities.

Top sectors for data science include banking and finance, marketing and advertising, and healthcare:

• Banking, Financial Services & Insurance (BFSI) - Business Analysts and Data Scientists use data for everything from fraud detection to customized financial advice. Machine learning algorithms can assist with risk analytics, stock trading, and other tasks.

• Marketing & Advertising - Data Analysts and Marketing Analysts use data science in advertising to create targeted ad copy, recommend products and services, and leverage social media platforms. Programming languages like Python and R, often key to data science positions, help experts analyze data and make recommendations.

• In healthcare, Data Scientists create algorithms to create care plans and improve patient services. Using data analysis in medical imaging can help care providers with diagnoses and treatment decisions.

Data science has proven crucial to many other sectors, from retail and manufacturing to the public sector. If you want to combine challenging work with job security, start with data science.

Common Industries That Use Data Science

Data science is a skill set used in a range of industries and professions. It plays an important role at financial institutions, in retail positions, in the healthcare sector, and the automotive industry. The data science tools and processes help those working in these industries analyze pertinent data, study and predict customer behavior, manage sensitive data, reduce costs, and improve operational efficiency. The following sections will explore how data science is used in each of these professions.

Retail

Those who work in retail often need to predict what their customers want so they can supply products accordingly. Failing to do so means that a Retailer will lose their competitive edge. These professionals rely on data and the data analytics process to provide them with information pertaining to how they can ensure their customers remain happy and continue shopping with them. The data analytics process can help Retailers create a personalized shopping experience for customers that helps them feel satisfied and makes them more likely to purchase additional products or services. Because the retail industry is customer-focused, those who make the shopping experience more personal and relevant for shoppers are more likely to secure their business. This process involves studying data patterns to better understand their purchasing behavior and patterns. 

Data science helps Retailers perform the following tasks:

• Understand their customers more fully.
• Design a system for recommending and personalizing products and services.
• Study a customer’s prior purchasing patterns or search history to help them find more relevant products.
• Apply predictive analytics techniques to improve overall customer experience.
• Review market insights.

The job outlook in the retail sector in the U.S. is expected to remain steady over the next decade. Currently, more than 4 million retail jobs are available in the U.S., and an additional 600,000 are expected to be added annually until 2031. Because most of these job openings will replace openings left by workers switching jobs or exiting the workforce, overall employment rates in this field will likely remain steady over the next ten years.

Healthcare 

The majority of the data needed by those who work in healthcare isn’t structured or easy to access and analyze. This is why healthcare providers and hospitals need those with a background in data science who can work with this heterogenous, fragmented information. Data science plays an integral role in providing those who work in healthcare with improved ways to access and manage data. It has applications for improving billing practices, locating scientific articles, performing clinical trials, and managing electronic patient records and care management databases. Those with data science training can help healthcare providers create and revise healthcare strategies and policies to improve access to services, equity, and quality. 

Healthcare analytics has applications for diagnosing disease, preventing the recurrence of illness, monitoring patients post-treatment, customizing care and prescriptions, and improving overall hospital operations. Data Scientists who work in healthcare rely on their training in statistics, as well as various computer programming languages, to improve outcomes for patients and providers alike. In addition, these professionals also use soft skills like storytelling and clear communication to convey data findings to pertinent stakeholders.

The job outlook for healthcare professionals in the U.S. is expected to grow by 13% over the course of the next decade. This is significantly faster growth than other occupations and will result in around 2 million new jobs by 2031. The average pay for healthcare practitioners and those who worked in technical occupations, such as physicians, nurses, and surgeons, was $75,000 in 2021, which was about $30,000 above the national pay average. 

Automotive

Data science tools and techniques play an integral role in the automotive industry. Those who work in the automotive sector are able to remain competitive by improving design, research, manufacturing, and marketing. With the help of advanced analytics techniques, Data Scientists can create autonomous automotive systems, which include radar, cameras, global navigation satellite systems, light detection tools, and sensors. As automobiles grow in complexity, they collect more data. 

The following are just several examples of how data science is used in the automotive industry:

• It reduces the cost of repairs.
• Data science makes it easier and more efficient to design and manage schedules.
• It improves vehicle safety using cognitive IoT techniques.
• Data science techniques can be applied to the production line to increase performance.
• Predictive maintenance techniques allow Data Scientists to spot defects in products.
• Manufacturers who use data science techniques have better control over aspects of the supply chain, including logistics and management.
• Automobile designers can analyze data to learn more about fuel efficiency, vehicle wear and tear, and mileage information. 

Those who work in the automotive industry make approximately $70,000 a year, though this estimate varies significantly depending on the employer and type of work.

Finance

Those who work in the banking and finance industry are using technology more often to inform their decision-making process. Banks incorporate a range of techniques that draw from data science to provide customers with better service and support. For example, the Bank of America uses predictive analytics and natural language processing to provide customers with a tool called Erica. This virtual assistant was created to help customers search their transaction histories or locate information on approaching bills. This virtual assistant has been programmed to get smarter with each transaction. 

Data science has many other applications in the financial sector. Financial data science is used for fraud detection, consumer analytics, risk management, pricing automation, and algorithmic trading. Data Scientists working in finance create processes to collect and store data, mine it for important information, then share their data findings with others to help propose innovative solutions to vital problems. The following are a few examples of how Financial Data Scientists apply data science concepts to their everyday tasks:

• With the help of data modeling techniques, Data Scientists can make both structured and semi-structured data cohesive.
• Computer vision and natural language processing help Data Scientists analyze unstructured or semi-structured data.
• Data Scientists gather important data for their financial institution, then create, engineer, and document the infrastructure.
• Most Data Scientists work with various other teams in their financial organization to spot problems and provide data-backed solutions.
• Quantitative analysis helps Data Scientists find insights in the data that can then become viable solutions. They must follow them through to delivery and measure the outcomes to ensure a given solution is successful.
• Data Scientists work with existing data to train new machine-learning models. They also prototype systems when testing the efficacy of new approaches.
• One of the most important aspects of financial data science involves creating and coding new algorithms. These algorithms are integral in managing machine learning and data analytics processes.

Financial Data Scientists play a vital role at many banks or financial institutions. They are generally paid well for the insights they provide. The average salary for Financial Data Scientists is $130,000.

Learn Data Science Skills with Noble Desktop

Because data science is a broad field, targeted training can prepare you for a data-centered position or even help you choose a specific role. You might think you’ll need a four-year data science degree, but this isn’t necessarily so. The bootcamp or certificate educational model has become increasingly popular for data professionals, thanks to features like small class sizes, hands-on training from industry experts, and individual mentoring. Noble Desktop offers a wide range of data science programs to help get you started.

• Data Science Certificate - The comprehensive Data Science Certificate provides all the skills required for entry-level data science, data analytics, or software engineering roles. Students learn how to write complex queries and build machine learning models while preparing a portfolio on a real-world basis. Skills covered include Python, SQL, NumPy, Pandas, and Jupyter Notebook, to name a few.

• Data Analytics Certificate - The comprehensive Data Analytics Certificate program offers the perfect training ground for Data Analysts, Business Intelligence Analysts, and Marketing Analysts. With a heavy emphasis on Tableau data visualization software, you’ll learn skills like Python programming, SQL, and machine learning, among others. Registrants of the Data Analytics Certificate or Data Science Certificate can also attend Noble’s Power BI Bootcamp at no additional charge.

• Python for Data Science Bootcamp - The Python for Data Science Bootcamp covers everything from programming fundamentals to data visualization. Students can save by taking this course as part of Noble’s Data Science Certificate, Data Analytics Certificate, or FinTech Bootcamp.

Check out all the Noble data science classes and bootcamps for additional options, like the Python Data Science & Machine Learning Bootcamp, Python for Data Science Bootcamp, or Python Machine Learning Bootcamp.

Key Takeaways

• Data science is the process of gathering insights from structured and unstructured data. It involves applying statistical techniques, scientific computing, and algorithms to data.
• Those with a background in data science are in demand in many industries and fields, such as healthcare, the automotive sector, retail, and finance.
• The insights Data Scientists can provide their employers play a vital role in helping them operate more efficiently, reducing costs, providing better customer service, and anticipating product shortages.
• If you want to learn more about data science, Noble Desktop provides a range of training options in the in-person and live online format.