Introduction Data analysis refers to the process of analyzing a large amount of collected data using appropriate statistical analysis methods, extracting useful information and forming conclusions to conduct detailed research and summary summary of the data. So, what is the data analysis process of a data analyst? Follow the editor to learn about it today!

1. Identify information needs

Identifying information needs is to ensure data analysis The first prerequisite for process effectiveness is to provide clear goals for collecting and analyzing data.

2. Data collection

The significance of understanding data collection is to truly understand the original appearance of the data, including the time, conditions, format, content, length, restrictions, etc. of data generation. It helps data analysts control the data production and collection process in a more targeted manner and avoid data problems caused by violations of data collection rules; at the same time, understanding the data collection logic increases the data analysts' understanding of the data, especially the problems in the data. Abnormal changes.

3. Data storage

Because data is constantly dynamically changing and iteratively updated during the storage phase, its timeliness, completeness, validity, consistency, and accuracy are often due to Problems with software, hardware, and internal and external environments cannot be guaranteed, and these will cause problems with data usage in the future.

4. Data extraction

Data extraction is the process of extracting data. The central link of data extraction is where to retrieve it, when to retrieve it, and how to retrieve it. In the data extraction stage, data analysts first need to have data extraction capabilities.

5. Data mining

There is no best algorithm, only the most suitable algorithm. The principle of algorithm selection is accuracy, operability, understandability, and usability. sex. No one algorithm can handle all problems, but knowing an algorithm can handle many problems. The most difficult thing about discovering algorithms is algorithm tuning. The same algorithm has the same parameter settings in different scenarios. Practice is an important way to gain tuning experience.

6. Data analysis

Data analysis is more about business application and interpretation than data mining. When the data mining algorithm reaches a conclusion, how to explain the results and feasibility of the algorithm? Reliability, significance and other aspects have an important impact on the actual significance of the event. How to feed the discovery results back to the event operation process to facilitate event understanding and implementation is the key.

7. Data visualization

There is a classic saying in the data analysis community: words are not as good as tables, and tables are not as good as pictures. Not to mention ordinary people, data analysts themselves have a hard time looking at data. At this time, you have to rely on the magical power of data visualization. In addition to advanced analysis such as data mining, one of the daily tasks of many data analysts is to monitor data and observe data.

8. Data application

Data application is a direct manifestation of the practical value of data. This process requires data analysts to have data communication capabilities, business promotion capabilities and project work capabilities.

The above is what the editor has compiled and shared with you today about "What is the data analysis process of a data analyst?" I hope it will be helpful to everyone. The editor believes that if you want to make a difference in the big data industry, you need to obtain some highly valuable data analyst certificates, which will provide you with more core competitiveness and competitive capital.