Learning from Data is a modern-day concept and is a phrase which is connected to the computers and a greater technological field. To understand the concept, what is primarily important is the understanding of the broader concept of data. Data is the source of any information and without data, there is no background of any type of information or knowledge. The concept of data was first used in the late 17th century and was derived from the term ‘datum.’ However, the concept gained popularity only when the world went through the phase of globalization. With the development of data and its concepts, the next major line of understanding must be attached to the difference between data, information and knowledge. When data is collected and processed, the same becomes some sort of useful information and when a whole lot of information is joint, one can gain a huge level of knowledge. This is the connection of data, information and knowledge and storing such raw data is very important for companies for major types of purposes. The techniques of storage vary from company to company.

Data is not just anything but can be classified into different groups and types. The broader way to divide the concept of data is into two types – Qualitative and Quantitative Data. As the name suggests, any data which is capable of being explainable in terms of calculations or can be said to be useful for measurement comes under quantitative data. However, not every type of data is measurable and such type of data can be termed to be qualitative. Such type of data is moreover feature-filled and is vague.

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Further, the two broader types of data are divided into groups. Quantitative data is divided into discrete data and continuous data. Discrete Data is a whole number sum and is useful for sections where importantly only whole numbers can be counted. For instance, the number of customers in a store will always be a whole number. Continuous data, on the other hand, can even be specified in terms of data which is necessarily not required to be in whole numbers. For example, the distance between any two cities is not always a whole number and can be decimal too. On the other hand, Qualitative data can be divided into binomial with only two options, nominal which is naturally existing and ordinal, which has a range to select from. The types of data is necessary to be understood to reach to the conclusion of how the same can be useful in learning.

There is a whole lot of field where data comes very handy. With the growing amount of population, the importance of data is undeniable, especially in certain fields. The foremost important field relates to medicine and around lakhs of people are purchasing medicine every day. To keep a count is hard and this data is very useful in keeping a count of how well the residents of an area are capable of buying medicines and what are the most sold varieties. Another major field is banking. Today, governments have worked very well for the progress of its citizens and the net result is the number of account holders and maintaining their data is of importance as only then the banks can know as to what offer to give to whom. On a similar note exists the other fields like construction and infrastructure, transportation and commerce also accumulate data and there has to be a greater level of usage of data to predict what is kept for the people in future. The saving of data has resulted in huge changes and even bought changes to the economies.

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The concept of Big Data is yet another important milestone in the understanding of how to learn from data. Big Data is a term which is coined and is commonly used for data which is very huge in numbers and gets piled up every day. Such data’s are common in many fields. Within Big Data, there are three major types of data namely structured data which is structured and can be saved in a format, unstructured data which is huge in numbers and is randomly available and semi-structured data which is the combination of both the two types. To analyze such data, some of the common techniques are machine learning, data mining and deep learning. Machine learning is a part of Artificial Intelligence and has been becoming more and more popular. With slight differences comes data mining through which data is extracted and saved and then used for many purposes. Deep learning, on the other hand, is a recent concept useful for the gaming industry.

In whatsoever way, the data can be extracted, saved and made use for learning, without protection, data is nothing. All the companies are currently striving hard to protect whatever data is with them and there are some common ways applied by them to save the data. The same can be encryption, putting restrictions on data to be accessed by everyone, educating the employees especially lower and higher-level employees and imparting them with knowledge how breaches can end and the importance of same and framing stricter rules to avoid breach. Even after the same, data breach has been observed true at many places and awarding protection has been the main motive of companies. There is no denial that in the future, the data is going to double up and to make them useful for learning, protecting the same in one way or other is even more pertinent.

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Would you like to read more about this topic? This book might interest you: Learning from Data.