Author: Zen and the Art of Computer Programming
1 Introduction
Data analysis is a complex project that involves multiple links, including data acquisition, cleaning, conversion, fusion, model construction, evaluation and application, etc. The processes of data analysis are also different, but they have common patterns, such as extracting features from raw data, performing feature engineering, training models, verifying model effects, iterative optimization and feedback. So, what tools can help us automate these processes? This article will elaborate on the advantages and usage scenarios of automated data analysis tools from the following aspects:
- Data acquisition (Extract): Using crawler tools, we can quickly collect massive Internet data;
- Cleaning (Transform): With the help of the powerful SQL language, we can quickly clean the data;
- Conversion (Load): Data conversion tools can convert various data formats into a unified format to facilitate subsequent analysis;
- Model Build: Machine learning algorithms can effectively handle statistical analysis and prediction of massive data. It can help us discover patterns hidden in the data and make predictions. Automated data analysis can reduce analysis time and improve efficiency;
- Model evaluation (Evaluate): Automated data evaluation tools can help us determine the quality of the model, find out the causes of errors and improve the model;
- Results Application (Deploy): Model deployment tools can help us integrate models into the production environment and provide real-time data analysis services;
2. Concepts and terminology
2.1 Data
Data refers to a collection of numbers or text, usually in the form of structured, unstructured and semi-structured data. Structured data refers to data with a fixed structure and order, such as tables