2. For the general data analysis training courses, the contents learned can be basically divided into two categories: theoretical knowledge and professional tools.

Including:

(1) bachelor's or master's degree in applied mathematics, statistics and quantitative economics is required. ?

(2) Skillfully use at least one data analysis software such as SPSS, STATISTIC, Eviews and SAS.

(3) At least Acess can be used for database development. ?

(4) Mastering at least one mathematical software: matalab and mathmatics, and establishing a new model. ?

(5) Master at least one programming language.

Expand as follows:

1, big data refers to a collection of data that cannot be captured, managed and processed by conventional software tools in a certain period of time. It is a massive, high-growth and diversified information asset, which needs a new processing mode to have stronger decision-making power, insight and discovery ability and process optimization ability.

2. In The Age of Big Data, co-authored by Victor Meyer-Schoenberg and Kenneth Cookeye, big data means that all data are used for analysis and processing, and there is no shortcut to random analysis (sampling survey). 5V characteristics of big data (proposed by IBM): volume (mass), speed (high speed), diversity (diversity), value (low value density) and authenticity.