![]() ![]() ![]() As such, data science comprises more than pure statistical data analytics, but the interdisciplinary integration of techniques from mathematics, statistics, computer science, and information science. Data science is defined as a “concept to unify statistics, data analysis, machine learning, and their related methods” to “understand and analyze actual phenomena with data”. Within the last years, data science has been established as its own important emergent scientific field. Although basic charting options are commonly available, more advanced visualization techniques have hardly been integrated as new features yet. Unfortunately, there is still a clear gap between visualization research developments over the past decades and the features provided by commonly used tools and data science applications. We will highlight the differences among the libraries and applications currently available. The vast amount of libraries and applications available for data visualization has fostered its usage in data science. In some cases, visualization is beneficial, while still future research will be needed for other categories. We will outline how existing data visualization techniques are already successfully employed in different data science workflow stages. Therefore, the increasing interest in data science and data analytics also leads to a growing interest in data visualization and exploratory data analysis. Exploring data, understanding its structure, and finding new insights, can be greatly supported by data visualization. To make use of this rich source of information, more and more employees have to deal with data analysis and data science. ![]() Organizations are collecting an increasing amount of data every day. ![]()
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