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Data Mining and Machine Learning
Now that the dawn of IoT (Internet of Things) has become a reality, the need for data analysis and machine learning has become necessary. Data science, also known as data-driven science, is a field about scientific methods, processes, and systems that extract knowledge (or insights) from data in various forms.
Machine learning algorithms are the means by which a data scientist can sample the data to realize different information based on parameters requested. There are many types of algorithms that can show many different samplings of information. These methods that are programmed into a computer, allows the computer to “learn” from the sample data and extract data meeting certain requirements and issue analysis based on different factors.
Machine Learning, is on the forefront of the data science and will eventually precursor Artificial Intelligence. The need for machines to learn data patterns from their data and produce various observations based on that data, making predictions or decisions for business customers/employers, and understanding that data trends are becoming extremely important to today’s businesses. The ability to take raw data, apply a method algorithm, then observe measurable data trends have helped businesses understand data and then they apply the information acquired to marketing to those results.
The need for ways to analyze data and extreme amounts of data is fast becoming the wave of the future. IoT is connecting all devices, people and networks producing massive amounts of data and requiring massive data analysis. The field of Data Science will be the most sought-after talent in the coming years. The ability to take these massive amounts of data that comes from all the connected devices and utilize the information in the data to benefit clients and their marketing efforts is very valuable to businesses will be invaluable.
There are two main types of machine learning: supervised learning and unsupervised learning. Supervised data is usually used for predicting data outcomes and unsupervised learning is used mainly for automated data analysis. These two types are utilized in most types of data analysis one way or the other but the main reason for data analysis is problem solving. The main languages utilized in machine learning are R, Python, SQL, SAS, Java, and MATLAB. “Which is best?”, you might ask. That is not easily answered. Right now, the most popular language in machine learning is R. METLAB is running a close second. Choose for yourself which is the better way to go for you.
In conclusion, data science and machine learning have become a new sector in the information revolution. Analyzing the massive amounts of data now available thanks to the internet have created a new demand for these skills.
The more we learn, analyze and problem solve, the closer we are to creating and utilizing artificial intelligence. Data Science and machine learning are the precursors for that development. The future is upon us now!