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  2. Data Science and Predictive Analytics - Wikipedia

    en.wikipedia.org/wiki/Data_Science_and...

    The textbook is globally available in print (hardcover and softcover) and electronic formats (PDF and EPub) in many college and university libraries [9] and has been used for data science, computational statistics, and analytics classes at various institutions.

  3. Data analysis - Wikipedia

    en.wikipedia.org/wiki/Data_analysis

    The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). [ 14 ] [ 15 ] The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of ...

  4. Exploratory data analysis - Wikipedia

    en.wikipedia.org/wiki/Exploratory_data_analysis

    Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.

  5. Data science - Wikipedia

    en.wikipedia.org/wiki/Data_science

    Topological data analysis (TDA) is a mathematical framework that uses tools from topology, including algebraic, differential, and geometric topology, to study the shape and structure of data. In summary, data analysis and data science are distinct yet interconnected disciplines within the broader field of data management, analysis, and mathematics.

  6. Topological data analysis - Wikipedia

    en.wikipedia.org/wiki/Topological_data_analysis

    Real data is always finite, and so its study requires us to take stochasticity into account. Statistical analysis gives us the ability to separate true features of the data from artifacts introduced by random noise. Persistent homology has no inherent mechanism to distinguish between low-probability features and high-probability features.

  7. Oversampling and undersampling in data analysis - Wikipedia

    en.wikipedia.org/wiki/Oversampling_and_under...

    To create a synthetic data point, take the vector between one of those k neighbors, and the current data point. Multiply this vector by a random number x which lies between 0, and 1. Add this to the current data point to create the new, synthetic data point. Many modifications and extensions have been made to the SMOTE method ever since its ...

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