Which department of the company should the data analyst belong to

There are many subdivisions and requirements for the position of data analyst, and the position of data analysis is divided into two categories as a whole: 1. Data analyst: 1. Professional ability growth path:

Associate Data Analyst, Data Analyst, Senior Data Analyst, Senior Data Analyst. 2. Promotion path for administrative positions: data analysis specialist, data analysis supervisor, data analysis manager, data analysis director.

Main professional skills requirements: database knowledge (SQL), basic statistical analysis knowledge, proficient in Excel, understanding of SPSS SAS, good PPT presentation ability. 2. Data Analysis Engineer:

Algorithm Engineer, Modeling Engineer. If you want to become a data analyst, it is recommended to choose Shifang Ronghai Educational Institution. The interactive intelligent teaching system independently developed by Shifang Ronghai has exclusive patented technology, creating a new model of learning and practical operation

1. Conduct data analysis according to the data analysis plan and submit it to market researchers within the specified time; 2. Able to carry out advanced data statistical analysis;

3. Management and performance appraisal of the company's entry personnel; and industry knowledge of coders.

and training on questionnaire structures;

4. The establishment of the input database, the verification of the data, the logical error check of the database, and the right part.

Verification of questionnaires;

Data Processing Companies.

There are two departments responsible for data analysis: one is customer transaction data analysis, which is generally in the brokerage business department of the headquarters; _x000d_

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The other is market data, ** data, etc., which are generally in the research department of the headquarters. _x000d_

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Generally speaking, it is the latter one. **Data Research Analyst: x000d

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1. Responsible for the data and technical analysis to the self-management department's first data reference; _x000d_

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2. Responsible for analyzing the fundamentals of listed companies in the target sector, doing some visits and research, statistically stating the company's financial data, and giving risk warnings; _x000d_

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3. Responsible for studying China's macroeconomy, market conditions and investment environment, analyzing investment industry policies, industrial policies and the company's operating conditions; _x000d_

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4. Responsible for assisting other analysts in the allocation of investment portfolios. _x000d_

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If you want to know more about data analysis, you can consult the CDA certification center, CDA is the professional abbreviation of data analysis professionals in the era of big data and artificial intelligence for the international scope of the whole industry, specifically refers to the Internet, finance, consulting, telecommunications, retail, medical, tourism and other industries specializing in data collection, cleaning, processing, analysis and can make business reports, provide decision-making of new data talents.

Department: Data Department of Market Research Company Superior position: Manager of Data Department.

This paragraph. Main work content Responsibilities Process only annihilation.

1. Conduct data analysis according to the data analysis plan and submit it to market researchers within the specified time;

2. The more advanced data statistics can be advanced and the rubber punching analysis is carried out;

3. Management and performance appraisal of the company's entry personnel; and training of coders on industry knowledge and questionnaire structure;

4. The establishment of the input database, the verification of data, the logical error check of the database, and the verification of some questionnaires;

1. Be proficient in using Excel

Excel can carry out a variety of data processing, statistical analysis and auxiliary decision-making operations, as a common data processing and presentation tool, data analysts in addition to proficient in the data in Excel charts display, but also need to master the method of formatting a series of charts generated.

2. Familiar with and proficient in at least one data mining tool and language

Taking R as an example, the R programming language has become an important tool in the field of data analysis and machine learning. As a scripting language, with its good interactivity and rich extension package resources, r can easily solve most of the problems of data processing, transformation, statistical analysis, visualization, and can reproduce all the details.

3. Ability to write reports

When writing a report, in-depth thinking, in-depth analysis, logical rigor, convincing conclusions, the ability to advance data trends, the ability to derive solutions from problems, and the ability to put forward instructive analysis suggestions are the characteristics of a good analyst.

4. Lay a solid SQL foundation

SQL fundamentals are important because most of the data analyzed by data analysts is extracted from databases. If you have a good SQL foundation and are familiar with it, you can not only extract the data you need, but also greatly improve your work efficiency.

Data analysts need to learn statistics, programming skills, databases, data analysis methods, data analysis tools, etc., and also be proficient in using Excel, be familiar with and proficient in at least one data mining tool and language, have the ability to write reports, and have a solid SQL foundation.

1. Mathematical knowledge.

Mathematics is the basic knowledge of a data analyst. For junior data analysts, it is enough to understand some basic content related to describing statistics, have a certain ability to calculate formulas, and understand common statistical model algorithms is a plus.

2. Analytical tools.

For junior data analysts, playing with Excel is a must, pivot tables and formulas must be proficient, and VBA is a plus. In addition, it is better to learn a statistical analysis tool, SPSS as a starting point.

For senior data analysts, the use of analytical tools is a core competency, a basic must for VBA, at least one of them must be proficient in the use of SPSS SAS R, and other analysis tools (such as MATLAB) as appropriate.

3. Programming language.

For junior data analysts, they can write SQL queries, and if necessary, write Hadoop and Hive queries, which are basically OK. For senior data analysts, in addition to SQL, it is necessary to learn Python to obtain and process data with half the effort. Of course, other programming languages are also possible.

Data analysts can be engaged in: IT system analysts, data scientists, operations analysts, and data engineers.

1. Data analystCommercial-oriented data analysis, operation advertising and other activity effect analysis, sales or profit**, user characteristics description, etc., need to have good statistical knowledge, need to understand 1-2 data analysis tools such as SAS, R, etc.

2. ConsultantsCustomer-oriented, to provide customers with data capture, data analysis, data reports, improvement suggestions implementation and other consulting services, need to have good communication skills, need to understand 1-2 data analysis tools such as SAS, R, etc.; (Consultants are actually divided into technical and non-technical, and the technical ones are mainly to build data platforms for customers).

3. Data product managerGenerally, it is unique to Internet companies, and companies with a large amount of data will have their own data products, such as Alibaba's Data Cube, etc., which are mainly for data products from product project establishment, development requirements, follow-up product development, testing to product launch, etc.

What is data analytics for?

Collecting, calculating, and making data available to other departments in the enterprise.

What is data analysis used for?

From a workflow perspective, there are at least 5 types of analysis that are often done:

Planning analysis before the start of work: to analyze what is worth doing before the start of the work**type analysis: ** the current trend, the expected effect of the monitoring analysis in the work:

Monitor the trend of indicators and find the cause of the problem: analyze the cause of the problem and find the countermeasure after the review analysis: accumulate experience and summarize lessons.

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So what is data analysis?

Data analysis is broadly divided into 3 steps:

1: Get data. Obtain user behavior data through burying points, and open up internal system data through data synchronization. and the construction of data warehouses to store data.

2: Calculate data. According to the analysis requirements, extract the required data, calculate the data, and make tables.

3: Interpret the data. Interpret the meaning of the data and derive some useful conclusions for the business.

So do data analysts mainly do the above three things?

It's not all, this is different in different companies. If the company is large, the acquisition of data is often done by the data development team, and their position is usually "data development engineer" or "big data engineer". Interpreting data is to write your own PPT for interpretation, leaving it to the "data analyst", which is actually a step in the middle of calculating data.

Some companies (generally doing e-commerce), the data is directly exported from platforms such as **, Tmall, Amazon, etc., and then analyzed based on these data. In some companies (generally traditional enterprises), the data is directly used in large-scale BI products, and then everyone exports data analysis based on BI products, and some companies are very small, so they directly do everything from data burying to data warehouse to data withdrawal.

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Job Responsibilities: Undertake demand research and data analysis.

Data mining, data extraction and other related work, and build data dashboards; Multi-dimensional analysis of data into Yinqiaoxing, giving data support, analysis report suggestions, and problem solutions; Intelligent report and data visualization platform design; Build a variety of analysis and models, track and monitor key data, find potential problems and opportunities, and provide data support for business decisions.

Qualifications:Undergraduate Admissions. Above degree, major in statistics is preferred;

More than 3 years of experience in data mining and analysis, proficient in using one or several analytical statistics and data mining tools, such as: python, finereport, etc.;

It can transform various business needs into suitable mathematical models.

Proficient in writing all kinds of business demand analysis and data analysis documents, the style of the documents is neat, the description is clear, and the requirements of the analysis are complete;

Have a more comprehensive technical knowledge, and can quickly grasp the technical essentials of different industries.

Job Responsibilities: Conduct data analysis according to the data analysis plan and submit it to market researchers within the established time; Able to carry out high-level statistical analysis of the elderly; management and performance appraisal of the company's entry personnel; and training for coders on industry knowledge and questionnaire structures; Establishment of the input database, verification of data, logical error checking of the database, and verification of some questionnaires.

Qualifications:Knowledge Experience: Mathematical statistics, economics, database principles and related knowledge; Proficient in the use of Excle, SPSS, Quanvert, SAS and other statistical soft sources.

Work ability: rigorous logical thinking ability, learning ability, verbal expression ability, management ability.

Work attitude: proactive, serious work, rigorous work.

A person who performs data analysis according to a data analysis scenario.

The data analyst is a person who analyzes data according to the data analysis plan, can carry out high-level data statistical analysis, is responsible for the management and performance appraisal of the company's entry personnel, as well as the training of the industry knowledge and questionnaire structure of the coder, and the establishment of the input database, the verification of data, the logical error check of the database, and the verification of some questionnaires.