How to become a data analyst with an annual salary of more than 500,000

A data scientist is an engineer or expert (different from a statistician or analyst) who can use scientific methods and data mining tools to digitally reproduce and understand complex amounts of data, symbols, text, audio or other information, and find new data insights. The qualities that an excellent data scientist needs to have are: understanding data collection, understanding mathematical algorithms, understanding mathematical software, understanding data analysis, understanding of analysis, and understanding of market applications.

CDA - Data analyst mainly plays the role of strategic staff in the enterprise, analyzing various operation, sales, management, strategy and other data of the enterprise, which can effectively avoid operational risks and improve cost utilization. CDA Data Scientist is ultimately a data scientist.

First of all, let's talk about the importance of analysis, we must pay attention to analysis when we conduct data analysis, just imagine, if we don't pay attention to analysis, then how can we do a good job of data analysis? The improvement of data analysis capabilities requires data analysts to pay attention to data analysis, which requires us to take stock and sort out the existing analysis resources within the organization before conducting data analysis. It is also necessary to nominate a dedicated person in charge of the field of analysis, so as to ensure that there is a good atmosphere for good data analysis.

Secondly, let's talk about the analysis phase of data analysis. The whole process of data analysis is the key to this stage and must be paid special attention. We need to not only conduct in-depth data exploration and modeling, but also consider model modification, deployment, and overseeing applications. Through a detailed review of the entire process of analysis, it is necessary to reflect on the deficiencies and which areas need to be improved, and then form the rules and regulations related to data analysis and related processes.

Let's talk about forming an analysis team, if you have a good data analysis team, then you can brainstorm ideas and do a good job of data analysis when you conduct data analysis. Establish an internal spontaneous analysis team, so that the analysis experts in the group can conduct each other's **, make suggestions on the construction of data analysis within the organization, and promote the construction and development of analysis capabilities through effective maintenance.

Finally, I would like to talk about adjusting the analysis plan, this stage is also more important, we can organize the needs of business changes with the help of powerful analysis capabilities to make rapid responses. This allows us to build and deploy applications very quickly, with more accurate results than ever before, so that we can provide more accurate information. At this stage, the purpose of the analysis should shift from simply answering tactical questions to more forward-looking strategic questions, so that the data analysis work can be effectively improved.

1.Knowledge combing, comprehensive coverageThe content of data analysis is complex and difficult to sort out, so we disassemble the data analysis process and cover the skills of each stage of data analysis from "business needs - data acquisition - data processing - data analysis" to help you quickly improve your data analysis capabilities.

2.Templates of practical tools, close to the real sceneMost of the data analysis in the market is too theoretical, and there is no content and reference that is truly in line with the actual business bridge, so it cannot be used directly. Through real cases, our Kaida course summarizes a set of methods suitable for beginners to quickly master skills.

3.Develop a data analytics mindset that is ready to useIf you want to achieve breakthrough growth in data analysis ability, you need to improve your analytical skills, and this process is a long-term cycle, not just by memorization, but by continuous practice. Especially if you don't know the skills related to the data analysis process, it is even more necessary for us to learn every skill without following the course.

First, for product managers, domestic product managers do not understand data analysis, and the competitive intelligence analysis of new products, product agile testing, etc. need to be completed with the help of data analysts, and later product iteration optimization still needs data analysts to collect user behavior, habits, evaluation and other data to complete;

The second is to serve the operation, and the user flow, customer relationship management, etc. in the product operation need to be completed with the help of data analysts; Third, the company's data formulation and standard construction, data integration of various departments, data management and other work need to be completed by data analysts;

Fourth, data intelligence and data ** serve the high-level.

From the above four aspects, business analysis ability and business knowledge ability are particularly important, this time is to test the analyst's business understanding ability and ability to solve practical problems for enterprises through data. For example, the analyst's analytical process, analytical thinking, analytical skills, and demonstrated persuasion. You can consider entering a professional company in this area, or if you are lucky enough to meet an experienced teacher to take you for a while, like I was lucky enough to meet a teacher to take me when I entered Cassia Ming, and I made rapid progress, so now I am basically very proficient in this set.

Nowadays, as data is more and more valued by people, the position of data analyst is also more and more favored, especially in first-tier cities such as Beijing, Shanghai and Guangzhou, the supply of data analysts is in short supply, but wanting to become a qualified data analyst is a process of continuous accumulation and precipitation.

1. First of all, you must have relevant statistical knowledge, most data analyst positions will tend to recruit people with mathematics majors, because people who study mathematics have basically systematically learned data analysis algorithms, or have strong logic, and can quickly grow into a data analyst.

2. Data processing ability, in order to become a qualified data analyst, you must have basic data processing skills, such as Excel SPSS or R language and SAS, master the use of databases, be able to call data from the database, query data, export data, and then analyze.

3. Business comprehension ability, if any data is separated from the analysis of the actual situation, then these analyses will have no effect, and can only be rhetoric. Therefore, a qualified data analyst should be able to grasp the close connection between the analysis and the market or product, so as to analyze the valuable relationship.

4. The ability to obtain data, a qualified data analyst should be able to obtain data from the outside world for his own use, and there are many software on the market that can collect data, such as locomotives, Jisoke gooseeker, etc., which can easily collect a lot of data and accept it for their own use.

The above are some of my understandings of how to become a data analyst, in short, the road of a data analyst is a long way to go, and you need to persevere, pay, and precipitate in order to truly grow into a valuable data analyst.