The most common types of data science techniques you must know

There are several kinds of analysis that a business could do to retrieve valuable data. Every type of data science project will have varying result or impact. The type of data science technique you must use really depends on the kind of business problem that you want to address.  Different data science techniques could result in different outcomes and so offer different insights for the business. Take note that the most essential goal of any process of data science is to search for relevant information, which could be easily understood in large-scale data sets. Below are the most common types of data science techniques that you can use for your business.  Anomaly Detection Anomaly Detection refers to searching for information in a set of data, which cannot match an expected behavior or predicted pattern. Anomalies are also known as exceptions, contaminants, outliers, or surprises and they usually offer actionable and crucial information. Outliers are objects, which could considerably deviate from the general average inside a dataset or integration of data. In numerical terms, this is separate from the rest of the data, and so the outliers could signify that something is not right and needs more analysis. Detecting an anomaly in a data set can be used to figure out if there are risks or fraud inside critical systems and they all have the attributes of interest to a data analyst, who could also advance the analysis to determine what is really happening. This can help the business to find crucial situations indicating fraud, flawed process or areas where a specific strategy may not be effective. It is crucial to take note that in large-scale data sets, a small portion of anomalies is quite common. Anomalies may show bad data, but it can also be caused by a random variation or may even show something that is statistically interesting. In these situations, more analysis may be needed. Clustering Analysis Clustering Analysis refers to the process of detecting data sets with similar attributes to learn their similarities as well as differences in the data. Clusters have specific traits in common, which could be used to enhance algorithms for targeting. For instance, clusters of customer information with similar purchasing behavior could be targeted with similar services and products to try raising the conversion rate. One outcome of clustering analysis is the development of customer personas, which refer to fictional characters identified by a business to represent the various customer types within a specific demographic. This includes the behavior set or attitude of customers who are actually using the brands or products. The business can use a specific software or programming language to work on relevant cluster analysis. Association Analysis Association Analysis will allow the business to discover relevant associations between different variables in a large-scale database. This data science technique will allow you to discover concealed patterns in the data, which could be used to detect variables inside the data as well as the co-occurrences of various variables, which exist in different frequencies. This method is commonly used by retail stores to look for patterns within information from POS. These patterns could be used in recommending new products to others according to what other customers have purchased before or according to the types of products that are purchased together. When you do this correctly, you can help your business increase your conversion rate. One good example is Walmart’s use of data mining in 2004, in which the retail giant discovered that the sales of Strawberry Pops increase at least seven times before a hurricane. As a response, Walmart placed this product at the checkout counters when a hurricane is about to strike in an area. Regression Analysis In Regression Analysis, you can try to determine the dependency between attributes. There is an assumption of a single-way causal effect from one attribute to the response of another attribute. Independent attributes could be affected by each other, but this doesn’t mean that there is a mutual form of dependency. By using a regression analysis, the business can identify if one variable is dependent on another but not the other way around. A business can also use regression analysis to identify the various levels of client satisfaction and how this attribute can impact customer loyalty and how the service levels could be affected, for example, the current weather. Another good example is how dating sites use regression analysis to better offer services for their members. Many dating sites are using regression to match two members according to a list of attributes to find the best partners for them. Data science could help businesses to look for and focus on the most relevant and important information, which could be used to establish models that could help in making projections on how systems or people could behave so the business could do some projections. By gathering more data, you can better build models that you can use to effectively implement data science strategies, which will result in more business value for your business. Classification Analysis Classification Analysis refers to a systematic approach for gathering crucial and relevant information about data. This data science technique can help the business to determine which set of data can be used for further analysis. Classification analysis is often used alongside cluster analysis as classifying data is usually the pre-requisite for clustering.   Email providers are among the common users of classification analysis. They are using algorithms, which can classify email as useful or spam. This could be done according to the data that is connected with the email or the data that is inside the email, for instance, specific works or attached files that signify spam. Share this on


Follow us on