The advancement in the analytical eco-space has reached new heights in the recent past. The emergence of new tools and techniques has certainly made life easier for an analytics professional to play around with the data. Moreover, the massive amounts of data that’s getting generated from diverse sources need huge computational power and storage system for analysis.
Three of the most commonly used terms in analytics are Data mining, Machine Learning, and Data Science which is a combination of both. In this blog post, we would look into each of these three buzzwords along with examples.
By term ‘mining’ we refer to extracting some object by digging. Similarly, that analogy could be applied to data where information could be extracted by digging into it. Data mining is one of the most used terms these days. Unlike previously, our life is circulated entirely by big data and we have the tools and techniques to handle such voluminous diverse meaningful data.
In the data, there are a lot of patterns which people could discover once the data has been gathered from relevant sources. The hidden patterns could be extracted to provide valuable insights by combining multiple sources of data even if it is junk. This entire process is known as Data mining.
Now the data used for mining could be enterprise data which are restricted and secured and has privacy issues. It could also be an integration of multiple sources which includes financial data, third-party data, etc. The more the data available to us, the better it is as we need to find patterns and insights in sequential and non-sequential data.
The steps involved in data mining are –
- Data Collection – This is one of the most important steps in Data mining as getting the correct data is always a challenge in any organization. To find patterns in the data, we need to ensure that the source of the data is accurate and as much as possible data is gathered.
- Data Cleaning – A lot of the times the data we get is not clean enough to draw insights from it. There could be missing values, outliers, NULL in the data which needs to be handled either by deletion or by imputation based on its significance to the business.
- Data Analysis – Once the data is gathered, and cleaned the next step is to analyze the data which in short known as Exploratory Data Analysis. Several techniques and methodologies are applied in this step to derive relevant insights from the data.
- Data Interpretation – Only analyzing the data is worthless unless it is interpreted through the form of graphs or charts to the stakeholders or the business who would make conclusions based on the analysis.
Data mining has several usages in the real world. For example, if we take the logs data for login in a web application, we would see that the data is messy containing information like timestamp, activities of the user, time spent on the website, etc. However, if we clean the data, and then analyze it, we would find some relevant information from it such as the user’s regular habit, the peak time for most of the activities, and so on. All this information could help to increase the efficiency of the system.
Another example of data mining is in crime prevention. Though data mining has most usage in education and healthcare, it is also used by agencies in the crime department to spot patterns in the data. This data would consist of information about some of the criminal activities that have taken place. Hence, mining, and gathering information from the data would help the agencies to predict future crime events and prevent it from occurring. The agencies could mine the data and find out the place where the next crime could take place. They could also prevent cross-border calamity by understanding which vehicle to check, the age of the occupants, etc.
However, a few of the important points one should remember about Data Mining –
- Data mining should not be considered as the first solution to any analysis task if other accurate solutions are applicable. It should be used when such solutions fail to provide value.
- Sufficient amount of data should be present to draw insights from it.
- The problem should be understood to be a Regression or a Classification one.
Previously, we learned about Data mining which is about gathering, cleaning, analyzing, and interpreting relevant insights from the data for the business to draw conclusions from it.
If Data mining is about describing a set of events, Machine Learning is about predicting the future events. It is the term coined to define a system which learns from past data to generalize and predict the future events from the unknown set of data.
Machine Learning could be divided into three categories –
- Supervised Learning – In supervised learning, the target is labeled i.e., for every corresponding row there is an output value.
- Unsupervised Learning – The data set is unlabelled in unsupervised learning i.e., one has to cluster the data into various groups based on the similarities in the pattern of the data points.
- Reinforcement Learning – It is a special category of Machine Learning which is mostly used in self-driving cars. In reinforcement learning, the learner is rewarded for every correct move, and penalized for any incorrect move.
The field of Machine Learning is vast, and it requires a blend of statistics, programming, and most importantly data intuition to master it. Supervised and unsupervised learning are used to solve regression, classification, and clustering problems.
- In regression problems, the target is numeric i.e., continuous or discrete in nature. A continuous value could be an integer, float, or a decimal, whereas a discrete value is a number or an integer.
- In classification problems, the target is categorical i.e., binary, multinomial, or ordinal in nature.
- In clustering problems, the dataset is grouped into different clusters based on the similar properties among the data in a particular group.
Machine Learning has a vast usage in various fields such as Banking, Insurance, Healthcare, Manufacturing, Oil and Gas, and so on. Professionals from various disciplines feel the need to predict future outcomes in order to work efficiently and prepare for the best by taking appropriate actions. Some of the real-life examples where Machine Learning has found its usage is –
- Email Spam filtering – This is the first application of Machine Learning where an email is classified as ‘Spam’ or ‘Not Spam’ based on certain keywords in the mail. It is a binary classification supervised learning problem where the system is initially trained with a set of sample emails to learn the patterns which would help in filtering out irrelevant emails. Once the system has generalized well, it is passed through a validation set to check for its efficiency, and then through a test set to find its accuracy.
- Credit Risk Analytics – Machine Learning has vast influence in the Banking, and Insurance domain with one of its usage being in predicting the delinquency of a loan by a borrower. Defaulting a credit loan is a prevalent issue in which the lender or the bank has lost millions by failing to identify the possibility of a borrower not repaying back the loans or meeting the contractual agreements. However, Machine Learning has been introduced by various banks which takes into several features of a borrower and builds a predictive model which helps in mitigating the risk involved in giving credit card loans to them.
- Product Recommendations – Flipkart, and Amazon are of the two biggest e-commerce industry in the world where millions of users shop every day the products of their choice. However, there is a recommendation algorithm that works behind the scenes which simplify the life of the customer by displaying them the products they make like based on their previous shopping or search patterns. This is an example of unsupervised learning where a customer is grouped based on their shopping patterns.
So far, we have learned about the two most common and important terms in Analytics i.e., Data mining and Machine Learning.
If Data mining deals with understanding and finding hidden insights in the data, then Machine Learning is about taking the cleaned data and predicting future outcomes. All of these together form the core of Data Science.
Data Science is a holistic study which involves both Descriptive and Predictive Analytics. A Data Scientist needs to understand and perform exploratory analysis as well as employ tools, and techniques to make predictions from the data.
A Data Scientist role is a mixture of the work done by a Data Analyst, a Machine Learning Engineer, a Deep Learning Engineer, or an AI researcher. Apart from that, a Data Scientist might also be required to build data pipelines which is the work of a Data Engineer. The skill set of a Data Scientist consists of Mathematics, Statistics, Programming, Machine Learning, Big Data, and communication.
Some of the applications of Data Science in the modern world are –
- Virtual assistant – Amazon’s Alexa, and Apple’s Siri are two of the biggest achievements in the recent past where AI has been used to build human-like intelligent systems. A virtual assistant could perform most of the tasks that a human being could with proper instructions.
- ChatBot – Another common usage of Data Science is the ChatBot development which is now being integrated into almost every corporation. A technique called Natural Language Processing is in the core of ChatBot development.
- Identifying cancer cells – Deep Learning has made tremendous progress in the healthcare sector where it is used to identify the pattern in the cells to predict whether it is cancerous or not. Deep Learning uses neural networks which functions like the human brain.
Data mining, Machine Learning, and Data Science is a broad field and it would require quite a few things to learn to master all these skills.
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