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Data mining, analysis, and visualization

Data analysis, mining, and data visualization are related fields of data science that are hugely important for all kinds of disciplines and industries.

What does machine learning have to do with data analysis, data mining, and data visualization?

Data analysis is the term given to the process of trying to gain insightful information from data sets. This can involve sorting the data, modeling it, and attempting to organize it. Data mining is a branch of data analysis that refers to the process of looking for patterns and anomalies in data sets with the aim of predicting future events. Data visualization is a technique often used in data analysis to make it easier to comprehend. It involves making visual representations of data sets, such as graphs and charts.

But where does machine learning come into all this?

Machine learning is strongly linked to all three of these branches of data science because it gives data scientists a way of working with enormous sets of data that would be completely infeasible for a human alone to do. By ‘feeding’ machine learning algorithms with data we can make sense of far greater amounts of raw information, leading to more reliable insights and predictions.

As with all types of computer and data science, these three fields have all experienced massive advancements in the last couple of decades and they continue to grow in both accuracy and number of applications.

Why are they important?

The fields of data analysis, data mining and data visualization are more significant than ever simply because they are more powerful than ever. In the past, similar methods were used by researchers to find patterns and meaning in data sets, but it was very difficult to employ them on a large scale. Data science relied almost exclusively on human labor, and a great deal of expertise was needed to interpret data accurately. This meant that it was a very expensive pursuit, and largely unattainable for anybody except large corporations and governments.

Computing power advancements greatly aided our ability to process large amounts of data, and now the growth of machine learning technology is helping to increase it even further. The power to analyze, comprehend and gain information from even staggeringly large data sets, such as internet usage data, is now accessible even to SMEs and individuals.

“With these more accessible insights, people and organizations across all industries are increasingly able to understand data and even predict future events with accuracy.”

With these more accessible insights, people and organizations across all industries are increasingly able to understand data and even predict future events with accuracy. This is having huge implications on the world economy by transforming the way people view data and plan business decisions.

Casos de uso


There is perhaps no industry where the quality of data and accuracy of statistical analyses is more valued than insurance. For insurance companies, a fraction of a percentage point can mean the difference between profit and loss.

Modern data mining software is of particular significance to the insurance industry. Such software – powered by advanced machine learning algorithms – allows these companies to more efficiently recognize patterns in the huge amounts of data that they amass. Identifying these patterns enables improved fraud detection, modeling of future claims, and more. This can ultimately lead to a more precise and reliable calculation of premiums and improved profit margins.


All businesses can benefit from getting more insightful market data, and machine learning algorithms can help them to achieve this. Companies often need to look at things like the way consumers interact with products, trends in purchasing and so on to optimize marketing strategies. Using the most advanced data analysis, mining, and visualization tools can improve the accuracy of this market data. This can provide invaluable information about where to target marketing efforts, what to sell, and how to maximize profits in the future.


Banks have to deal with almost unfathomably large amounts of transaction data related to financial markets and account fluctuations. Machine learning algorithms give banks the power to automate much of the sorting, processing, and analysis of the data, helping to reduce the amount of money spent on these things and improve general efficiency.

Harness the power of data

Machine learning can change the way you use data and revolutionize your business strategy.

At Sciling, we’ve been working with AI and machine learning technologies for decades. With a strong combination of high-level academic and professional experience, our team is made up of experts in the field of modern data science, armed with the knowledge to apply it effectively to meet the demands of any business.

Change the way your business looks at data

Other use cases

Computer vision
Data mining, analysis, and visualization
Natural Language Processing
Predictive Analytics
Recommender Systems

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