Data science is present and future technology tool for the successful evolution of many industries. Probably every field already involves some level of data science elements such as statistics, machine learning or more pronounced AI tools. However, there are a few important industries analyzed by well-designed data science course.
Healthcare
The medical field already heavily relies on data. Extensive amounts of information stored in medical databases require tools to analyse this data and apply discovered knowledge to practical use. Each patient is followed by a data stream, which needs to be explained, classified for better disease prediction or more effective rehabilitation. Standard statistical tools are minimal when more in-depth knowledge discovery is required.
The task for data scientists is to apply smart AI algorithms for best information discovery and converting into a convenient form which can be included in medical decision support systems. The key directions of data science in medicine are:
- AI-based medical image analysis. Automatic classification and segmentation of X-Ray, MRI, CT and ultrasonic scans. Algorithms can release doctors from intensive image examination by highlighting trouble spots.
- Genetic data mining. Genetics is a rapidly evolving field, which brings new possibilities in disease prediction and finding defects. Data science technologies also is a critical helper in genome sequencing.
Drug discovery . Machine learning toolscan help to speed up drug discovery by analysing multiple outcomes andeven provide a prognosis ofthe effectiveness of new drugs. Monitoring and disease prediction. Data analysis tools have a vast potential toprovide highlights of evolving diseases in the early stages when analysinglarge medical databases.- Monitoring and disease prediction. Data analysis tools have a vast potential to provide highlights of evolving diseases in the early stages when analysing large medical databases.
E-commerce
Probably business is already using all the newest data science tools as it directly affects the success and profits. There is no secret that all major e-commerce sites are using big-data technologies for capturing user behaviour data to provide targeted ads and recommendations for best yield.
Giants such as Google or Facebook are steadily building large potential user databases that include even the smallest possible bits of information that can be used to target, forecast, classify and optimise strategies.
Even physical stores are always collecting client data through loyalty cards and other tools to understand and create better behavioural models.
Gaming and betting
Gambling and betting industries are depending on vast amounts of data analysis. Gaming industry generates billions of dollars each year and success directly depend on successful strategies and risk management. Both sides – the house and player, use data science.
The gaming houses, like 188bet casino, are intensively supported by data science. They continuously analyse client behaviour, history and trends and thus adjust games in real-time for the best experience. Deep learning algorithms are capable of detecting potential frauds or illegal activities such as systematic attempts to break the system. Data science technology allows casinos to be more individualised by analysing lots of personal information via loyalty cards and gaming habits. The data includes the behaviour profiles, spending habits, preferred gaming machines and even facial expressions. In addition to personal data, there is other information such as temperature, humidity, music atmosphere at the time, people crowing and many more that help to make individual decisions.
While people are playing from home devices, such as PCs or mobiles, there is also a massive potential for AI. Artificial intelligence is capable of learning how to play casino games. Making a software program that knows how to keep pressing ‘spin’ on a mobile slots game would be straightforward. If AI could learn how to play poker, there would likely be no way to bluff it. It would also formulate an algorithm, which tells it the statistical likelihood that you are bluffing. The processing power of a standard home computer is high enough to apply data science algorithms for more effective gaming. Eventually, there might be a time soon when gambling will become more or less self-driven. Gaming houses also have to be prepared to maintain a balance and pleasant user experience.
Banking and finances
Today bank and finance sectors are heavily automated. To perform bank transactions, you do not need to visit the real place. All operations can be done quickly from the home computer. With more freedom and flexibility there is always more risks of fraud and misusage.
Machine learning tools are extensively used to predict frauds from behavioural information, transaction flow and risk models. By analysing patterns in the data flow, banks can predict risky credit card operations, insurance patterns and investment activity. Data analysis also help to segment customers for better financial product targeting.
Finance and market sectors are heavily depending on data science. Trading is mostly based on algorithmic decisions that are much faster than human actions. Machine learning algorithms are analysing risks, predict the outcome and provide arrangements for the best possible outcome.
Transportation
Transportation depends on data science more than you can imagine. It starts with smart traffic control systems and ending with self-driving cars. Roads and streets are always loaded with millions of vehicles, and the flow depends on optimal control. Simple solutions as adaptive traffic lights may help to avoid traffic jams. Data systems are constantly analysing traffic patterns to predict the disturbance and take measures ahead of the trouble.
Another field, as we mentioned, is autonomous driving. This is a rapidly evolving technology, which heavily depends on data science algorithms. Self-driving technology not only depends on on-board learning technologies but also relies on on-road logistics, sighs and position data.
Manufacturing
Modern manufacturing processes are heavily automated. Data science is extensively used in production for best optimisation and cost-effectiveness. Data science has enabled the companies to predict potential problems, monitor systems and analyse the continuous stream of data. Furthermore, with data science, industries can monitor their energy costs and can optimise their production hours.
Many routine tasks are performed by robots, and they need to be programmed or trained to be effective. Real-time data analysis and machine learning algorithms are very helpful in boosting the production of manufacturing lines.
Also, in the industry, data science helps to monitor, schedule and predict potential problems. Automation and data analysis is a crucial element to a successful business.
Conclusion
There are arguably more fields where data science methods play a significant role. The military is one of the leading players, with lots of breakthrough data-driven technologies inaccessible to the outside world due to obvious reasons.
Another field, which processes loads of data, is space exploration. Large data streams of raw data are collected continuously from multiple telescopes, observatories and satellites. Data needs to be processed, classified and compressed for storage. You can hardly find an area that is not affected by data science. It appears that the future will be more dependent on presently collected data, which will help to build more effective and robust applications with less human intervention.