What tools are commonly used for big data analysis?

Updated on technology 2024-05-07
3 answers
  1. Anonymous users2024-02-09

    Big Data Tools: Data modeling tools.

    SPSS: Mainly used for data modeling work, it is stable and powerful, which can meet the needs of small and medium-sized enterprises in the process of business model establishment. Big data tools: data visualization and analysis tools.

    ABI, a one-stop data analysis platform for YXY, provides ETL data processing, data modeling and a series of data analysis services, and provides a wealth of data analysis tools: in addition to Chinese-style complex reports, dashboards, and large-screen reports, ABI also supports self-service analysis, including drag-and-drop multi-dimensional analysis, Kanban and Kanban sets, and business users can carry out exploratory self-service analysis as they like by simply dragging and dropping. At the same time, the word impromptu report and slide report make the report more brilliant.

  2. Anonymous users2024-02-08

    Python data analysis tool.

    python pandas

    Pandas is an open-source Python data analysis library that was originally developed as a financial data analysis tool, and as such, pandas provides great support for time series analysis.

    pyspark

    A big data analytics library from the Apache Spark project. PySpark provides a number of features for analyzing big data in Python, and it comes with a shell that users can run from the command line.

    python scikit-learn

    Any discussion about big data will spark a discussion about machine learning, and fortunately, Python developers have plenty of options for using machine learning algorithms.

  3. Anonymous users2024-02-07

    Although there are thousands of tools for data analysis, they are all the same. It is nothing more than data acquisition, data storage, data management, data calculation, data analysis, data display and other aspects. SAS, R, SPSS, PYTHON, and Excel are the most frequently mentioned data analysis tools.

    python

    Python is an object-oriented, interpreted computer programming language. The Python syntax is concise and clear, with a rich and powerful class library. Often nicknamed the glue language, it makes it easy to connect various modules made in other languages (especially c).

    A common application scenario is to use Python to quickly generate a prototype of a program (and sometimes even the final interface of the program), and then rewrite the parts that have special requirements in a more appropriate language, such as the graphics rendering module in a 3D game, which has particularly high performance requirements, can be rewritten with c c and then encapsulated into an extension library that Python can call. It is important to note that you may need to consider the platform when using extension libraries, and some may not provide cross-platform implementations.

    r software. r is a complete software system for data processing, calculation and drawing. It can provide some integrated statistical tools, but more importantly, it provides a variety of mathematical calculations and statistical calculation functions, so that users can flexibly perform data analysis and even create new statistical calculation methods that meet their needs.

    SPSSSPSS is the world's earliest statistical analysis software, with complete data input, statistical analysis, reports, graphics and other functions, capable of reading and exporting files in a variety of formats.

    Excel can be used for various data processing, statistical analysis and auxiliary decision-making operations, and is widely used in management, statistical finance, finance and many other fields.

    SAS software.

    SAS integrates data access, management, analysis and presentation. It provides a variety of statistical analysis processes from the calculation of basic statistics to the analysis of variance of various experimental designs, correlation premature failure regression analysis and multivariate analysis, including almost all the latest analysis methods, and its analysis technology is advanced and reliable. The implementation of the analysis method is done through a procedure call.

    Many processes offer multiple algorithms and options at the same time.

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