1 thought on “I want to switch to data analysts, how to get started?”
Carroll
Need a certain knowledge reserves and the basic understanding and use of business. For the zero -based white white who wants to switch to data analysis, in terms of what to learn from the data analysis industry, how the data analysis industry compensation and treatment, how to better master the skills of the field of data analysis, are all confused. Therefore, this article gives some suggestions, for the friends who want to transfer data to the ranks and are zero -base transfer friends. . Knowledge reserves It data analysis, as a cross -disciplinary discipline, needs to master many aspects of knowledge. Curadamia and statistical basics, data analysis refers to the use of statistical methods and analysis tools to analyze a large amount of data, tap its potential laws and values, and provide scientific and rigorous rational basis for business decisions. Among them, the knowledge of mathematics and statistics is inseparable from knowledge reserves related to calculus, linear algebra, probability theory and mathematical statistics. The storage of data is inseparable from the use of the database. It is necessary to master the SQL database language for additional, deletion, modification and investigation in the relational database system. The advanced data analysis needs to use one or more programming languages, such as Python and R, which will make your data analysis more efficient. If you need it, you can learn common algorithms commonly used, regression, clustering, and dimension reduction, as well as their advantages and disadvantages and usage scenarios. This will be the bonus item you switch to the company. . Industry analysis The knowledge of learning data analysis can not be lost to understand each data position. Next, introduce the data analysis industry positions, roughly divided into four directions: data analysis, data mining , Data development, data products. The data analysts engaged in data collection, sorting, analysis, found problems, analyzed problems, and concluded to provide data support for the company's decision -making layer. Like business. Data mining engineers or algorithm engineers use model training data to dig laws from massive data, predict or classify objects, mainly biased programming and algorithms, and have high requirements for statistical theoretical knowledge. Data development engineers design, build and maintain infrastructure to provide platforms such as data collection, storage, processing, and calculation. The development of code needs to be made up for code capabilities, but it requires a certain business logic compared to programmers in pure technology stacks. . Mental experience 1. Be sure to use careful, patient, and calm mentality to analyze data. Data analysis is a detailed work. According to the principle of the 28th, the processing of data will account for 80 % of the data analysis. If the data processing is improper, it will affect the following analysis, and a good mentality will be reduced to reduce the probability of making mistakes. 2, have the ability to think independently and in discharge. Data analysis is not just to complete the data requirements and arguments on some business. Data analysts should have their own unique insights on the basis of understanding business, expand their scope of thinking, and enhance insight. At the same time, think in other ways, look at data and use data from multiple angles. Different people can get different effects at different angles. If attention: 1. The Internet itself has digital and interactive characteristics. This attribute characteristics bring revolutionary breakthroughs to data collection, finishing, and research. In the past, data analysts in the "Atomic World" spent higher costs (funds, resources, and time) to obtain support research and analysis data. The richness, comprehensiveness, continuity and timeliness of the data were much worse than the Internet age. 2. Compared with traditional data analysts, data analysts in the Internet era are not data lack of data, but excess data. Therefore, data analysts in the Internet era must learn to use technical means for efficient data processing. More importantly, data analysts in the Internet era must continue to innovate and break through in data research methodology. 3. In the industry, the value of data analysts is similar to this. As far as the press and publication industry is concerned, whether in any era, whether media operators can accurately, detailed and timely understanding the situation and change trends are the key to the success of the media. 4, in addition, for the content industries such as press and publishing, more importantly, data analysts can play the function of consumer data analysis, which is a key function that supports press and publication agencies to improve customer service.
Need a certain knowledge reserves and the basic understanding and use of business. For the zero -based white white who wants to switch to data analysis, in terms of what to learn from the data analysis industry, how the data analysis industry compensation and treatment, how to better master the skills of the field of data analysis, are all confused. Therefore, this article gives some suggestions, for the friends who want to transfer data to the ranks and are zero -base transfer friends.
. Knowledge reserves
It data analysis, as a cross -disciplinary discipline, needs to master many aspects of knowledge.
Curadamia and statistical basics, data analysis refers to the use of statistical methods and analysis tools to analyze a large amount of data, tap its potential laws and values, and provide scientific and rigorous rational basis for business decisions. Among them, the knowledge of mathematics and statistics is inseparable from knowledge reserves related to calculus, linear algebra, probability theory and mathematical statistics.
The storage of data is inseparable from the use of the database. It is necessary to master the SQL database language for additional, deletion, modification and investigation in the relational database system. The advanced data analysis needs to use one or more programming languages, such as Python and R, which will make your data analysis more efficient. If you need it, you can learn common algorithms commonly used, regression, clustering, and dimension reduction, as well as their advantages and disadvantages and usage scenarios. This will be the bonus item you switch to the company.
. Industry analysis
The knowledge of learning data analysis can not be lost to understand each data position. Next, introduce the data analysis industry positions, roughly divided into four directions: data analysis, data mining , Data development, data products.
The data analysts engaged in data collection, sorting, analysis, found problems, analyzed problems, and concluded to provide data support for the company's decision -making layer. Like business. Data mining engineers or algorithm engineers use model training data to dig laws from massive data, predict or classify objects, mainly biased programming and algorithms, and have high requirements for statistical theoretical knowledge. Data development engineers design, build and maintain infrastructure to provide platforms such as data collection, storage, processing, and calculation. The development of code needs to be made up for code capabilities, but it requires a certain business logic compared to programmers in pure technology stacks.
. Mental experience
1. Be sure to use careful, patient, and calm mentality to analyze data. Data analysis is a detailed work. According to the principle of the 28th, the processing of data will account for 80 % of the data analysis. If the data processing is improper, it will affect the following analysis, and a good mentality will be reduced to reduce the probability of making mistakes.
2, have the ability to think independently and in discharge. Data analysis is not just to complete the data requirements and arguments on some business. Data analysts should have their own unique insights on the basis of understanding business, expand their scope of thinking, and enhance insight. At the same time, think in other ways, look at data and use data from multiple angles. Different people can get different effects at different angles.
If attention:
1. The Internet itself has digital and interactive characteristics. This attribute characteristics bring revolutionary breakthroughs to data collection, finishing, and research. In the past, data analysts in the "Atomic World" spent higher costs (funds, resources, and time) to obtain support research and analysis data. The richness, comprehensiveness, continuity and timeliness of the data were much worse than the Internet age.
2. Compared with traditional data analysts, data analysts in the Internet era are not data lack of data, but excess data. Therefore, data analysts in the Internet era must learn to use technical means for efficient data processing. More importantly, data analysts in the Internet era must continue to innovate and break through in data research methodology.
3. In the industry, the value of data analysts is similar to this. As far as the press and publication industry is concerned, whether in any era, whether media operators can accurately, detailed and timely understanding the situation and change trends are the key to the success of the media.
4, in addition, for the content industries such as press and publishing, more importantly, data analysts can play the function of consumer data analysis, which is a key function that supports press and publication agencies to improve customer service.