Data analysis consists of three important components:
1. Data acquisition. It is our raw material, because any analysis is required data source;
2. Data Mining. It can be said that some of the most "tall", but also the entire business value. Data mining is the core business value of data mining, which is what we are talking about business intelligence BI
3. The data visualization. It can be said that the data field size fits all skills, you can let us know intuitively analyze the data results.
Detailed below summarizes these three major elements:
A data acquisition
Collection tools: octopus, automatically grab the artifact, it can help you grab 99% of the page source.
python reptile: you can write, collect, store data, and automated collection design.
Two data mining
It can be said that knowledge-based engineering, equivalent to the entire column of "algorithm" section. We must first know his basic processes, algorithms, and the underlying mathematical basis.
The basic process: business understanding, data understanding, data preparation, model building, model evaluation, on-line publishing
Algorithms: classification algorithm, clustering, association analysis, link analysis
Mathematical foundation: probability theory and statistics, linear algebra, graph theory, optimization methods
Third data visualization
When the amount of data when it is difficult to understand, visualization can help us a good understanding of the structure of these data, analysis and visual presentation of results.
There are two methods of data visualization (not all):
1.python third-party libraries: Matplotlib, Seaborn, etc.
2. Third-party tools: If you generate a csv file format, you want to see is the way was adopted for presentation, third-party tools miniaturist, DataV, Data GIF Maker may be employed.
