The explosion of information presents exciting opportunities for industries to grow through data science. Health care, finance, energy, media and several other industries are discovering insights from big data through data science that help businesses make strategic decisions and optimize outcomes.
Data science is being applied to several industries with transformative results. Operational efficiencies are significantly increasing, leading to increases in revenue margins. It is little surprise that the profession remains in high demand with an attractive average salary.
Data science professionals are highly sought by businesses in several industries. Learn how you can prepare yourself for a rewarding data science career with Meharry’s School of Applied Computational Sciences.
The following highlights some of the industries that data science is transforming.
Health Care and Pharmaceuticals
The health care industry includes a massive amount of data from the Electronic Health Records (EHR), genome sequencing, mobile health devices, social media, and other sources. This information, with the application of data science, is leading to important improvements in clinical care and treatments.
Computer vision, an application of artificial intelligence to train computers to see images like a human, acts as a second eye to improve the accuracy of clinical decisions. Natural language processing, combined with machine learning, enables physicians to gain actionable insight from pathology and other doctor’s reports.
Data science is also critical to advancements in genomics, a field that improves understanding of complex diseases like cancer, heart disease and diabetes. Researchers can efficiently integrate genomic and environmental data for an in-depth analysis of diseases. mHealth is a more recent application of data science to health care that leverages data from smartphones, wearable sensor devices, and other patient monitoring tools for improvements in personalized patient care and detecting life-threatening disease.
Data science has joined biology, chemistry and medicine as a critical component to pharmaceutical research. Machine learning and other methodologies are making drug discovery cheaper and more effective by adding a predictive element to the traditional trial and error approach.
Meharry’s School of Applied Computational Science is making advancements in health care data science through several research collaborations and in its many research labs. The School also offers a master’s degree and graduate certificate in biomedical data science.
Finance is a data-heavy industry with the omnipresent, competitive drive for improvement. It is little surprise that data science is having a significant impact on the field.
Applications of data science to finance are important to financial security, managing risk, marketing and improving trading. The communications that banks rapidly send after a suspicious transaction are made possible through machine learning. Those algorithms help prevent fraud by quickly identifying breaks in spending patterns. Illegal insider trading is difficult to detect due to the nature of the stock market. But that activity can be caught faster by using deep learning to analyze trading patterns before and after the announcement of significant company news.
Machine learning also helps companies segment customers into clusters to determine their potential value. Targeted marketing campaigns can then target those groups intelligently to promote new services. Banks avoid human error by applying predictive analytics to loan history, bank transaction, income, demographics and even data from social media to guide loan assessments and to detect early warning signs in existing loan services. Companies also increase profits through algorithmic trading that executes trades quickly, but only when specific criteria are met.
Retail and eCommerce
Suggestions for new products based on a customer’s shopping behavior are one of the most recognizable impacts of data science in retail and e-commerce. But those recommendations, the result of complex machine and deep learning algorithms, are just one example. Similarly write Plant, market basket analysis, also driven by machine learning and deep learning, informs future purchases based on customer data.
Data science helps companies set optimal prices through algorithms that analyze pricing flexibility, customer and geographic data, and other criteria. Machine learning also helps companies manage inventory by analyzing purchase patterns to develop strategies to “. . . increase sales, confirm timely delivery and manage the inventory stock.”
Other applications of data science in retail include using algorithms to assess demographic information to identify new store locations as well as assessing customer data and social media information, via Natural Language Processing, to assess customer sentiment.
Perhaps one of society’s oldest industries, manufacturing has gone through several transformations. Delivering products on time and in the right quantities is essential to manufacturing. Data science plays an important role for the industry by improving efficiency and reducing costs. According to Dr. Nagdev Amruthnath, a Data Scientist III at DENSO, “. . . we are going through the fourth Industrial Revolution, where data from machines, environment, and products are being harvested to get closer to that simple goal of Just-in-Time . . .”
Unplanned downtime, due to machine maintenance is an expensive problem in manufacturing. Data scientists apply models to data pulled from sensors placed on machines to achieve predictive maintenance rather than reacting to breakdowns. Companies are also using computer vision to improve on human quality control to avoid using defective parts.
Sales forecasting is important to manufacturing companies and requires predicting manufacturing volumes. That volume could be disrupted by the supply chain, work force issues or in the production process. Amruthnath says that “techniques ranging from linear regression models, ARIMA, lagging to more complicated models such as LSTM are being used today to optimize the resources . . .” that allow companies to reliably forecast sales.
Likewise, companies seek to ensure the quality of products they produce are predicable. Statistical process control techniques help companies gain more control in the process by letting them know when they could expect to produce bad parts.
Energy and utilities
With smart meters, grid equipment, weather, GIS, and storm data, energy and utility companies produce a vast amount of data. Consumers living in homes with intelligent devices significantly increase those volumes of data.
Utility companies run multiple models with that data to achieve power planning. Additionally, according to Energy Tech Review, energy companies draw insights from this data to “reduce costs, lower carbon emissions, and manage energy demand for end customers.” Smart power grids, with machine learning algorithms applied to their data, provide great opportunities for dynamic energy management.
Data science also has important applications for clean energy writes Discover Data Science. The efficiencies that come from harnessing data help solar and wind farms reduce costs, making renewable energy an attractive alternative to fossil fuels. Likewise, oil and natural gas companies with data science driving their refinery and distribution processes, can reduce their environmental impact.
Federal and state governments affect daily life through several functions, and data science’s impact is clear. Engler says that predictive analytics, using machine learning can help identify when citizens need public services the most. Springboard adds that government data scientists are often wrangling data from disparate sources and applying models to:
- Analyzing spending data to identify and prevent waste, fraud, and abuse
- Using business intelligence to help government at all levels make better financial decisions
- Working with researchers in studies and tracking findings that could determine the course of new drugs and consumer goods
- Collecting and analyzing defense intelligence to improve defense systems
- Working with state and local departments to coordinate data gathering, analysis, and modeling
- Creating maps and visualizations to explain data findings
- Developing and communicating recommendations rooted in data to non-technical members of government
Data science also presents new opportunities to improve public policy. Engler says that Natural Language Processing can help assess public sentiment, and stresses the need to evaluate bias in the models that determine health interventions. Additionally, with the high level of use of data in the private sector, government oversight will need data scientists to properly perform that role.
Data science adds efficiencies that reduce costs and improve safety in the transportation industry. Reliably staying on schedule and avoiding accidents are both critical, and Muthukumaran says that predictive analytics can determine the best routes and identify areas at high risks for accident.
Data pulled from sensors is harnessed to help monitor engines and other equipment. GIS and weather data lead to dramatic changes in efficiency and help ensure safety. Robins says that this operational efficiency not only reduces costs but positions transportation companies as a dependable partner and an asset to a supply chain.
There are several potential perils in the construction industry. Poor planning, budgeting or management, and cost overruns are just some of the issues that can narrow or eliminate profit margins. Thanks to applying data science, the industry can now move away from a reactive, unpredictable process and gain new control and efficiencies.
Applying predictive analytics, the construction industry is able to pull meaningful insights from vast data for forecasting. Professionals can more accurately assess risks and use new tools to track the performance of equipment and manage other valuable assets.
Data science also adds to significant gains in optimization. Companies may employ several contractors and artificial intelligence solutions provide greater control for project management and allow budgets to be managed in real time.
Communications, Media, and Entertainment
With Netflix recommendations and suggested news stories in social media, it is nearly impossible to not see data science in the communications, media and entertainment industry. However, those customer engagement strategies are just the surface of data science applications to the field.
Lippell writes that editorial experience is combined with insights from vast datasets to discover new avenues to publish content. Additionally, customer data is captured from media properties as well as social media to build engaged relationships.
Anna Anisin, founder and CEO at Formulated.by, shared reports from executives at several media companies about their applications of data science. The New York Times applies data science for forecasting, operations research, user segmentation and content recommendations—efforts that improve the reader’s experience and address business concerns.
Anisin adds from Bob Bress, head of data science for Freewheel, a Comcast Company, that “The media industry has more data available to it now than ever before and with that comes incredible opportunities to develop innovative ways to leverage that data for business impact.” Brees adds that data scientists at media companies need to maintain their expertise in an industry that is quickly changing with evolving viewing habits.
The School of Applied Computational Sciences offers two master’s degrees—M.S. Data Science and M.S. Biomedical Data Science. Explore these programs and see how they can launch your career.
Do you have questions about our programs? Contact an enrollment advisor at firstname.lastname@example.org.
365 Data Science. “5 ways data science changed finance.” 365 Data Science. Accessed April 28, 2021. https://365datascience.com/trending/data-science-finance/
ActiveWizards. Top 8 Data Science Use Cases in Construction.” Accessed April 28, 2021. https://activewizards.com/blog/top-8-data-science-use-cases-in-construction/
Amruthnath, Nagdev. “Data Science in Manufacturing: An Overview.” March 2, 2020. Accessed April 28, 2021. https://medium.com/@ODSC/data-science-in-manufacturing-an-overview-e6d648bf9c08
Anisin, Anna. “How Data is Affecting Media, Advertising, and Entertainment Careers.” August, 20, 2020. Accessed April 28, 2021. https://towardsdatascience.com/how-data-is-affecting-media-advertising-and-entertainment-careers-58b6237bf7af
Energy Tech Review. “How Big Data and Analytics Impact Energy and Utilities.” November, 25, 2019. Accessed April 28, 2019. https://www.energytechreview.com/news/how-big-data-and-analytics-impact-energy-and-utilities-nwid-22.html
Engler, Alex. “What all policy analysts need to know about data science.” April 20, 2020. Accessed April 28, 2021. https://www.brookings.edu/research/what-all-policy-analysts-need-to-know-about-data-science/
Finance Train. “Role of Data Science in Risk Management.” Finance Train. Accessed April 28, 2021. https://financetrain.com/role-of-data-science-in-risk-management/
Lippell, Helen. Big Data in the Media and Entertainment Sectors. 2016. Accessed April 28, 2021. https://link.springer.com/chapter/10.1007/978-3-319-21569-3_14
Muthukumaran, Sanjanaa Sri. “How Analytics is Transforming the Transport Industry.” March 8, 2021. Accessed April 28, 2021. https://www.latentview.com/blog/how-data-analytics-is-transforming-the-transportation-industry/
Pant, Meghna. “9 Interesting Applications of Data Science in the E-commerce Industry.” July 16, 2019. Accessed April 28, 2021. https://datascience.foundation/datatalk/9-interesting-applications-of-data-science-in-the-e-commerce-industry
Robins, Craig. “Why Big Data is so Important to the Transportation Industry.” March 15. Accessed April 28, 2021. https://www.robinsconsulting.com/why-big-data-is-so-important/
Springboard. “What does a Data Scientist in Government Do?” Accessed April 28, 2021. https://www.springboard.com/library/data-science/government-jobs/#:~:text=Other%20responsibilities%20of%20data%20scientists,levels%20make%20better%20financial%20decisions