Data science helps in tackling many real-world problems that businesses are facing today.
In fact, it has been widely adopted across many industries to help organization leaders and company executives make intelligent, fact-based decisions.
With more and more people relying on computers and other devices that use the Internet today, data has evolved into something even more useful in people’s daily life. Data analytics can provide sound guidance for the business’s day-to-day operational decisions.
It can also help organizations learn human behavior and work patterns. Using predictive analytics – or the process of using data to make tactical plans for the future – it has become possible for companies to make their strategies fool-proof.
Considering that data is the key to ensuring not only the survival but also the success of a company, more companies have started seeking professionals with skills in data science.
This makes it unsurprising for data science courses to become a popular online information technology course.
The Big Benefit of Data Science
Interest in data science can provide significant advantages for both companies and skilled professionals alike.
If you’re considering this career path, this article lists down the most viable industries that you can settle into after finishing a data science course.
But while almost all sectors can benefit from it, eight industries benefit the most from data science, including:
Healthcare uses data in many aspects, from managing operating costs to improving patient treatment outcomes. With the help of predictive analytics, doctors can make correct diagnoses and decide on the best type of treatments that will most likely lead to better results.
A study from Columbia Business School shows that data science can also help improve the time efficiency in emergency rooms by as much as 15 percent. This can be done by predicting potential visits and assigning the correct number of medical staff during expected rush hours.
The banking sector helps people manage their money, supplement their income with loans as needed, and plan for their future.
With the increasing number of products and services provided in this sector, the banking industry relies heavily on data science to assess consumer behaviour.
They use the information they get to deliver excellent customer service, improve efficiency, and offer better products that suit consumer needs.
Data science also helps banks deal with fraudulent activities, which can lead to costly damages to both the bank and their customers if left to fester.
By spotting unusual and potentially illegal spending behaviour, banking professionals can intervene with the processing of payments (think credit cards).
Predictive analytics also helps banks improve customer interaction, especially during loan application screenings and attempts of cross-selling, all while encouraging customer loyalty.
Online shopping has become quite popular in almost any demographic, but that doesn’t necessarily mean that stores have no need to target specific groups.
With the help of data analytics, a retail store can determine the specific group of people whom they will be speaking to in their brand messaging. Specialty and hobby stores, for example, need to attract those who like doing specific activities.
After pinpointing the target audience, retail stores need to convince these people that what the brand offers is the most suited for what they do (think running shoes for runners).
Alternatively, retailers can also identify what sells to offer just that to avoid getting left behind by the competition.
It can also help them anticipate the correct number of stocks required for their inventory, increase profits, and determine the factors that affect customer loyalty and satisfaction.
Transportation remains part of people’s basic needs, and data science can help ensure a high rate of successful journeys, be it through the use of private or public vehicles.
Public transport officials can also use the predictive analytics leg of data science to determine how much public transit trips are required to accommodate most – if not all – passengers seeking the service.
Take the transport for London, for example. It utilizes statistical data to map traveler journeys, personalize transport details, and come up with backup plans for unexpected circumstances.
Data science can also help improve the delivery of basic education in the country. In fact, data science and analytics have been found to help educational institutions consolidate data from different sources and use them on platforms not designed for such a diverse collection of data.
The University of Tasmania, for instance, has developed a learning management system that can monitor the progress of each of their 26,000 students. It can track student logins, the time each one spends on various web pages, and their overall learning progress.
Plus, big data can help measure teaching effectiveness based on the number of students they handle, the subject matter they teach, student demographics, and aspirations, among other things.
Businesses of various sizes can help prevent cyberattacks by staying updated on the latest threats and methods hackers currently use today. Back in the day, organizations only reacted after an attack was made.
Today, predictive data analysis can help deter any of that from happening by employing techniques that can detect attacks before an infiltration occurs.
Using historical data, data scientists can provide recommendations to an organization’s cybersecurity team on how to respond to suspicious activity before it causes any significant damage to the network. It can also alert the IT department of the vulnerabilities in the system.
Civil service is another sector that can benefit greatly from data science. The government can use data in performing health-related research, financial market analysis, environmental protection, and fraud detection.
They can also use historical data analysis to improve social services and to probe criminal activity within an agency.
In terms of manufacturing, data science can help in crunching the information about the supply of raw materials and natural resources so that it would be proportionate to consumer demand.
Using this diverse set of information, data scientists can come up with a more manageable ratio and make production decisions that can help produce sufficient products without unnecessarily depleting raw supplies.
Also, big data helps manufacturing companies determine the sustainability of a product and find ways to make it more eco-friendly.