07 Sep 2022
Data Mining Vs Machine Learning: Key Difference | Ekeeda
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Data mining isn’t a new-age invention that has come with the digital age. The concept was introduced way back in the 1930s when Alan Turning introduced the idea of a universal machine that will perform computations similar to that of modern-day computers. We’ve come a long way and now modern-day businesses are harnessing data mining, data processing, data visualization, and Machine Learning to improve their performance. Right from the sales process to operations, and interpreting financials for investment purposes – anything and everything needs data mining and coming up with potential data with statistics. As a result, data scientists have become the most important employee at any organization across the globe because companies look to achieve bigger goals with data science.
No wonder it's termed as the sexiest job of the 21st century! Over 11.6 million data science jobs are in demand by 2026. It’s the most promising career of the decade.
Are you excited to know what data scientists do? Read: What Is The Typical Life For Data Scientists
Data Science: The Father Of Data Mining & ML
With big data being so prevalent in the industry, a lot of data terms tend to be thrown around, wherein many are simply complicated. What is data mining? What is data science? Is there any difference between Machine Learning Vs Data Science? How to connect these two terms to each other? All of these are questions, and discovering their answers can be quite intriguing and provide a deeper understanding of data science and data analytics. Data science is the father of data mining and machine learning & it mainly falls under the same umbrella. They often intersect and confuse us, but they are two different identities. Here’s a look at what data mining and machine learning difference and how it can be utilized.
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Use Of BIG Data
One of the key differences between Data Mining and Machine Learning is how they’re used and applied in daily lives. For instance, data mining is used by ML to see the connections between the relationships. Uber uses Machine Learning to calculate the Estimate time of arrival (ETA) for rides or Zomato for meal delivery times. Data mining could be used for various purposes such as financial research, logistics and shipping, bitcoins, insurance, etc. Investors use data mining and web scrap to look at a start-up's finances and help determine if they want to offer the fund. A company will use data mining to help collect the data on recent sales trends to inform everyone about marketing to inventory and secure new leads. Data mining will be used to sort through social media profiles, websites, and digital assets to compile information on a company’s new leads and get started with an outreach campaign.
Machine learning will represent the principle of data mining, and will also make automatic correlations and learn how to apply to new algorithms. It’s the technology behind tesla self-driving cars and insurance policies being accurately predicts your insurance needs. Machine learning also offers instant recommendations when a buyer purchases a product from Amazon. These algorithms & analytics are constantly meant to improve so that the result will get accurate over time. Machine learning is the ability to learn and improve at an impressive feat.
Banks are already using & investing more in data science and machine learning concepts to track transactions, fraudulent when CC is swiped at vendors. For instance, Citibank feeds on Freedzai – global data science enterprise to identify & eradicate financial fraud in real-time through online or offline personal banking transactions. Are you interested in joining the team of skilled data scientists and making the lives of people easier with technology? Start learning Data Science today.
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Pattern Recognition
Collecting Data is quite a challenging job and it's all the tougher to bring sense to it. The right tools, software, and of course data science skills, are needed to be able to analyze and interpret massive amounts of information. Data scientists will collect and find recognizable patterns to act upon. Else, the data will be large and unusable unless data scientists devote their time to these complex and seemingly random patterns of their own. Anyone who knows the fundamentals of data science and data analytics knows this will be a painstaking and time-consuming task. Businesses use data to shape their sales forecast, and determine customer spending patterns, likes & dislikes, and experience. For instance, D-Mart collects points of sales from over 300+ locations for its data warehouses. Vendors can see the information and use it to identify buying patterns & guide their inventory predictions to process for the future.
Data Mining reveals patterns through classification and sequence analysis. However, ML takes this concept a step further by using the same algorithms data mining use to automatically learn from and adapt data collection. When malware comes an increasingly pervasive problem, ML can look for patterns in how the data in systems or the cloud can be accessed uninterruptedly. Machine learning will look at patterns to help identify which files are actually malware, with a high level of accuracy. All this gets done without any constant monitoring by a human. When abnormal patterns are detected, an alert will be sent to prevent the malware from spreading.
Better Accuracy
Both data mining and ML can improve the efficiency of data collection. But, data mining and how it gets analyzed will generally depend on how the data is organized and collected. Data mining will include extracting and scraping software to pull from hundreds of resources and examine the data that researchers, data scientists, investors, & stakeholders use to look for patterns and relationships to improve efficiency. One of the main foundations of ML is data mining. Data mining can be used to extract more accurate data. It helps refine your machine learning to achieve good results. A person might miss the various connections and relationships in data, but Machine Learning will pinpoint all the moving pieces to draw accurate conclusions to help shape the machine’s behaviour.
Machine Learning will enhance the relationship intelligence in CRM systems and help the sales team better understand their customers to make a good connection with them. If you combine company CRM with ML it can analyze the past and present actions that will lead to a conversion or customer feedback. It even helps to learn how to predict which products & services could be sold in the best form and how to shape marketing messages to those customers.
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Data Mining & Machine Learning: The Future
The future is quite bright for data science since the amount of data is bound to increase in leaps and bounds. As per Forbes, digital data will grow exponentially from 4 zettabytes to 45 zettabytes. There will be 2 megabytes of new information every second for each human being around the globe. As we amass more and more data, the demand for skilled data scientists with advanced data mining and Machine Learning techniques will be on an upsurge. There will be more overlap between data mining and Machine Learning as the two will intersect to enhance the collection & use of large data for analytics purposes.
The future of data mining points to predictive analysis, as we see advanced analytics across several industries like research, medical, insurance, banking & finance, etc. Scientists will be able to use predictive analysis to look at factors being associated with disease and predict which treatment will work for you the best. We just scratch from the top and see what machine learning can do and how it will spread to help scale up with analytical abilities and improve our technology. Our machines become connected and everything from hospitals to mobile networks, ship vessels and highways could be improved through ML and IoT technology that will learn from other machines.
However, some experts have different thoughts on data mining and machine learning altogether. Instead of focusing on the differences, we can rather focus on thing that they concerned on: How we can learn from data? In the end, how we acquire & learn from data is will stand to be the foundation of emerging technology. It’s one of the golden times for not just data scientists but even for those who use data in some or the other form. Data science skills are the need of the hour and potential candidates who can turn the tables for the companies will be required at a massive scale in the industry.
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