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  2. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]

  3. A beginner’s guide to data visualization with Python ... - AOL

    www.aol.com/beginner-guide-data-visualization...

    Data visualization is a technique that allows data scientists to convert raw data into charts and plots that generate valuable insights. There are many tools to perform data visualization, such as ...

  4. Lists of statistics topics - Wikipedia

    en.wikipedia.org/wiki/Lists_of_statistics_topics

    Outline of regression analysis; Index of statistics articles; List of scientific method topics; List of analyses of categorical data; List of fields of application of statistics; List of graphical methods; List of statistical software. Comparison of statistical packages; List of graphing software; Comparison of Gaussian process software

  5. This Beginner’s Course Will Teach You The Best ... - AOL

    www.aol.com/news/beginner-course-teach-best...

    Data visualization is important in any industry as it makes it easier to identify patterns, trends and outliers in large sets of data. Whether you’re a teacher, a computer scientist or a ...

  6. Data and information visualization - Wikipedia

    en.wikipedia.org/wiki/Data_and_information...

    Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main ...

  7. Free statistical software - Wikipedia

    en.wikipedia.org/wiki/Free_statistical_software

    There are a few reviews of free statistical software. There were two reviews in journals (but not peer reviewed), one by Zhu and Kuljaca [26] and another article by Grant that included mainly a brief review of R. [27] Zhu and Kuljaca outlined some useful characteristics of software, such as ease of use, having a number of statistical procedures and ability to develop new procedures.

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