7/29/2023 0 Comments Top basic data science techniquesFor instance, imagine we own an e-commerce platform, and we want to improve our customers' satisfaction. It is a technique applied to understand how a dependent variable can be predicted and influenced by the independent variable. Regression analyses are used to identify relationships among variables. Sometimes it means that measurement or data entry error is occurring (or has occurred) and should quickly be fixed other times, it can be an opportunity to explore. Simply put, this technique shows items that significantly differ and are distant from other data points within a data set. In addition to identifying patterns, it is also essential to know if the data has outliers (or anomalies), which may provide actionable and valuable insights. In addition to identifying patterns within data sets, it can also monitor alterations in trends over time, allowing businesses to make intelligent decisions. Tracking patterns is one the most basic (yet, precious) data mining techniques. Moreover, association rules can sometimes be used by data scientists and developers to build artificial intelligence programs. Therefore, association analysis helps to identify relations between variables in databases. The association rules highlight that if consumers buy gin, then they also buy tonic water, showing that these items are associated. For instance, imagine we are executing a data analysis for a supermarket. This data mining technique considers particular attributes that are significantly correlated with another attribute. As the name suggests, this analysis classifies data into distinct classes according to attributes or characteristics that data elements may share. ClassificationĬlassification can retrieve valuable and relevant information from the data. Later we will discover how these core techniques are evolving to advanced analytics regarding machine learning models and artificial intelligence. Let's start with the math and statistic core techniques. On the other hand, the larger the data sets are, the higher the chances of finding relevant insights. On the one hand, technological advancements have contributed to more extensive volumes of data, which are more challenging and complex to handle. Data mining techniques are advantageous to predict trends and behaviors, allowing businesses and organizations (e.g., political, academic, etc.) to make informed decisions.īy using intelligent methods, data mining techniques are able to retrieve information from data, making it comprehensive and interpretable. This process enables data scientists to identify patterns, as well as relationships, within data sets. The main benefits of these technologies rely on the ability to improve operational efficiencies, understand behaviors, and gain a competitive advantage.ĭata mining is a process that embraces statistics, artificial intelligence (AI), and machine learning (ML). However, businesses only started exploring these advanced analytics technologies over the last years. In that sense, we will explain the following technologies: neural networks and deep learning, natural language processing, and dimensionality reduction.Īccording to SAS's report on " Advanced Analytics: Moving Toward AI, Machine Learning, and Natural Language Processing", technologies such as Machine Learning (ML), Artificial Intelligence (AI), and Natural Language Processing (NLP) already exist for decades. We will introduce six essential math and statistic techniques: classification, association, tracking patterns, outlier detection, regression, and clustering.įurther, we will also describe how advanced analytics technologies contribute to data mining. This article focuses specifically on data mining techniques. Besides data mining techniques, it is also vital to have a good understanding of algorithms, automation techniques, computing architectures, and so on. It comprises the systematic (autonomous or semi-autonomous) computational examination of data, aiming to identify and interpret significant patterns in data.Ĭurrently, the tech industry is increasingly seeking more deep analytical expertise to discover models and patterns from large data sets. In fact, advanced analytics relies on statistics, as well as on operations research and computer programming. Facebook LinkedinĪt the core of data science are math and statistics, which is a necessary background to understand and create advanced analytics.
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