Big data: what is data analysis and environment construction

1. What is data analysis

Today's world is increasingly dependent on information technology, and a large amount of data is generated every day. We often feel that there are more and more data, but it is becoming more and more difficult to find valuable information from them. The information mentioned here can be understood as the result of processing the data set, which is the conclusive thing extracted from the data set that can be used in other occasions, and the process of extracting valuable information from the original data is called Call it data analysis , and it's part of data science work.

Definition: Data analysis is the science and art of collecting, processing, and organizing data in a targeted manner, and using techniques such as statistics and mining to analyze and interpret data .

Data Analyst Responsibilities and Skill Stack

When HR releases recruitment requirements, it usually refers to positions such as data engineering, data analysis, and data mining as data analysis positions. However, according to the nature of the work, it can be divided into engineering-oriented data governance direction and business-oriented data analysis direction . , Algorithm-oriented data mining direction , development-oriented data development direction , and product-oriented data product manager . The data analysts we usually refer to mainly refer to business data analysts . Many data analysts start their careers from this position, and this position is also the position with the largest number of recruits. Business data analysts usually do not belong to the R&D department of the company but to the operation department, so this position is also called data operation or business analysis , and such personnel are usually called "BI engineers". Usually the description (JD) of this position in the recruitment information is:

  1. Responsible for the output of related reports.
  2. Establish and optimize the index system.
  3. Monitor data fluctuations and anomalies to identify problems.
  4. Optimize and drive

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Origin blog.csdn.net/ml202187/article/details/132289300