Four Types of Machine Learning Analytics

Analytics is the systematic scientific analysis of statistics or data. It’s use is mainly used for the exploration, interpretation, and visualization of statistical data sets. It also involves applying statistical patterns towards efficient online decision-making by making decisions that are affected by statistical variables. Analytics helps business people to find and choose what is valuable and important to their businesses. In essence, it is an economic study of value.


Data science is one of the emerging fields in analytics. It is the use of large-scale data analytics to support business decision-making and product selection. Data science deals with the extraction of meaningful information from large databases using traditional databases and techniques such as linear and logistic regression, supervised and unsupervised learning, decision trees, etc. Businesses therefore prefer to outsource data analytics to companies specialized in providing tools and services for data analysis.

There are four types of analytical techniques used in analytics. These include descriptive analytics, key performance indicators (KPIs), quantitative metrics, and contextual analytic techniques. Descriptive analytics refers to those techniques that focus on studying how people interact with a service or product. This type of analytics generally makes use of numbers in order to describe user behavior.

Key performance indicators (KPIs) are metrics that can be used to measure the quality and quantity of a product or service. Typically, these KPI’s identify trends rather than individual occurrences. The main advantage of using key performance indicators (KPIs) in analytics is that it provides information on what people want and what they need. For example, a retail store should know its sales trends in order to know what products are selling well and which are not.

Quantitative metrics on the other hand are measures of value that can be determined by statistical analysis. This type of analytics makes use of complex mathematical equations in order to determine value. Some examples of quantitative metrics used in analytics include customer satisfaction surveys, health and wellness management, financial measures, product life cycle, and product setup.

A new way to analyze and make better business decisions has been developed in the field of big data analytics. Machine learning allows computers to analyze large sets of unstructured data, making it easier for these machines to find relationships and associations that would otherwise be very hard to identify. Machine learning makes it easy for systems to extract the meaningful insights from large amounts of unprocessed data. These insights can then be used to make better business decisions.

Another way to visualize the impact of analytics is to visualize it as a four types of X. These are: reinforcement, guidance, intuition, and knowledge. As new technologies are introduced, the capabilities of computers to recognize patterns and make intuitive leaps in understanding become increasingly powerful. Eventually, a computer may be able to understand everything from the smallest connection between two neurons in the brain to complete your dinner shopping.

When describing the capabilities of an analyst, it is important to understand the difference between traditional analytical processes and data analytics. Traditional analytics focuses on the collection, organization, interpretation, management, and reporting of information. Data analytics focuses on making the analysis as transparent and easy to access as possible. The combination of the two can provide the greatest potential ROI.

As organizations continue to evolve into a more digitally driven culture, they will face new opportunities in analytics platforms. In fact, this is already happening. Data mining was introduced just a few years ago and its goal was to mine information from massive databases to find patterns and relationships. While this was a great solution for finding unknowns or anomalies in large organizational networks, it was problematic because it required manual work and often resulted in missing trends and connections. An analyst must still manually verify all the results or risk being blindsided by something that could dramatically change business.

With the advent of a hosted software platform and the implementation of data analytics, analysts are now able to apply a more definitive approach to solving business intelligence problems. In fact, data analytics is defined by four types of technology. These types include event processing, big data, web services, and big data visual analytics. These four types can be combined using a visual computing technology that allows one to define and modify a network of visual models that are then fed into an existing business intelligence database. This new approach allows for a tremendous improvement in insight without the constraints that exist with event processing and web services.

Another key advantage of using a predictive analytics is that it helps provide business analysts with data that helps them make strategic decisions about how to proceed. While traditional techniques like market surveys or elicitation may be useful in suggesting changes to strategic directions, these methods are often time consuming and labor intensive. By contrast, machine learning enables analysts to quickly and accurately derive and apply insights from large databases and then generates recommendations for making those changes.

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