The significance of data collection is to truly understand the original features of data, including the time, conditions, mode, content, length, constraints and so on. This will help big data analysts to control the data production and collection process more pertinently and avoid data problems caused by violating data collection rules; The knowledge of data acquisition logic together increases the understanding of data analysts, especially the understanding of abnormal data changes.

2. Data access

Data access is divided into two parts: storage and retrieval. Data storage, big data analysts need to understand the internal working mechanism and process of data storage. The core is to know what the requirements have been processed on the basis of the original data and what kind of data they finally get.

3. Data extraction

Big data analysts first need to have data extraction skills. The first layer is the ability to extract data from a single database according to conditions; The second level is to master the ability to extract data across database tables; The third layer is to optimize SQL statements, which can reduce personal time waste and system resource consumption by optimizing nesting, selecting logical levels and traversal times.

4. Data mining technology

At this stage, big data analysts should master: First, the basic principles and knowledge of data mining, statistics and mathematics; The second is to skillfully use a data mining thing, Python or R optional; Thirdly, we need to know the commonly used data mining algorithms, as well as the usage scenarios, advantages and disadvantages of each algorithm.

5. Data analysis

In data mining, data analysis is more biased towards business use and interpretation. When the data mining algorithm draws a conclusion, how to explain the practical significance of the algorithm from the aspects of results, credibility and visibility.

6. Data visualization

In this part, in addition to following the principle of unified standards of the company, the specific form of big data analysts depends on actual needs and scenarios. Data visualization is always the auxiliary of data content, and valuable data reports are the key.