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s|ngular Data & Analytics is the result of a long­standing history in data mining and analytics. s|ngular has provided solutions to first­rate customers in various industries such as media, telecommunications, government and financial services.

Latest news

ADAM: Automated Discovery and Analysis Machine

As data scientists, we have to deal with bad formatting data sets with missing or wrong values, and many other problems that hamper our progress. Some studies, such as the Cleaning Big Data: Most Time-Consuming, Least Enjoyable Data Science Task, Survey Says, found that data scientists spend 60% of their time on cleaning and organizing data. Once this process is completed, data analysis phase is performed. In this task, values, histograms, variables distribution, and correlations between them are studied. Most of the time, modeling phase involves repetitive analysis tasks, such as selecting the best algorithm by using automated procedures (for example, GridSearchCV in scikit-learn), or features selection process applying different predefined techniques. In the end, all Data Analytics projects are very similar regarding methodology and techniques applied. ADAM (Automated Discovery and Analysis Machine) system is developed in order to optimize our time and focus more on intellectual labors and techniques to solve the specific problem. ADAM is a framework that helps us to perform an automated analysis of the data set by applying Data Science techniques.

CRISP-DM Phase V: Evaluation

In this post, we continue describing the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, after our previous post Phase IV: Modeling. In this case, we discuss the fifth phase of the data analysis project, known as Evaluation.

Case study in Psychology: analysis of human personality profiles

In this case study, we continue describing how Data Analytics techniques can be applied to analyze human personality based on the Big Five model. The aim of the study is to analyze questionnaires in order to detect personality profiles, based on the Big Five model. In a previous post, we described the first three phases of the CRISP-DM methodology: Business Understanding, Data Understanding, and Data Preparation. In that post, we described the context of the case study, the available data and its analysis in detail. Now, we discuss the next phase of the analysis according to CRISP-DM methodology, Phase IV: Modeling. The objective is to identify personality profiles according to the Big Five model by applying data analytics techniques, especially, clustering with different algorithms.

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