What does a data analyst do? What does a data analyst mainly do

1. Business. The premise of engaging in data analysis will be to understand the business, that is, to be familiar with industry knowledge, the company's business and processes, and it is best to have your own unique insights.

2. Management. On the one hand, it is the requirement to build a data analysis framework, for example, to determine the analysis idea, it is necessary to use marketing, management and other theoretical knowledge to guide, if you are not familiar with management theory, it is difficult to build a data analysis framework, and it is difficult to carry out follow-up data analysis. On the other hand, it is used to provide instructive analysis suggestions for the data analysis conclusions.

3. Analysis. It refers to mastering the basic principles of data analysis and some effective data analysis methods, and being able to flexibly apply them to practical work in order to effectively carry out data analysis. The basic methods of analysis are:

Comparative analysis, group analysis, cross-analysis, structural analysis, funnel analysis, comprehensive evaluation analysis, factor analysis, matrix correlation analysis, etc. Advanced analysis methods include: correlation analysis, regression analysis, cluster analysis, discriminant analysis, principal component analysis, factor analysis, correspondence analysis, time series, etc.

4. Use tools.

Refers to mastering the common tools related to data analysis. Data analysis method is theory, and data analysis tools are tools to realize the theory of data analysis methods, in the face of more and more huge data, we can not rely on calculators for analysis, we must rely on powerful data analysis tools to help us complete data analysis work.

5. Design. Understanding design refers to the use of charts to effectively express the analytical views of data analysts, so that the analysis results are clear at a glance. The design of charts is a major matter, such as the selection of graphics, the design of layouts, the matching of colors, etc., all of which need to master certain design principles.

The simple understanding is: improvement and optimization of the business; Help businesses identify opportunities; Create new business value. The details are as follows:

Improving and optimizing the business side is making the business better. It is reflected in two major aspects

In terms of improving the user experience of the enterprise, the original business process is optimized to provide users with a better user experience.

In terms of the rational allocation and utilization of enterprise resources, it is more reasonable to optimize the allocation of enterprise resources to achieve the purpose of maximizing benefits.

The second is the process of using data to find blind spots in people's minds and then discover new business opportunities.

Finally, a new business model is formed on the basis of data value, and the data value is directly converted into a monetary model.

1. What data to analyze

Analyzing what data is related to the purpose of data analysis, usually after determining the problem, and then collecting the corresponding data according to the problem, the corresponding decision-making support strategy is formed in the corresponding data framework system.

2. When to analyze the data

Data tracking of the whole process of business operations.

3. Data acquisition

Internal DataIt is mainly network log-related data, customer information data, business process data, etcExternal DataIt is third-party monitoring data, enterprise market survey data, industry scale data, etc.

4. Data analysis and processing

The tools used depend on the needs of the company.

5. How to do data analysis

Data follows the business, and the process of data analysis is the process of transforming business problems into data problems and then restoring them to business scenarios.

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:

Pre-work planning analysis: Analyze what is worth doing.

Before the start of the work, the analysis of the current trend is expected to be effective.

Monitoring analysis at work: monitor the trend of indicators and find problems.

Cause-based analysis at work: Analyze the cause of the problem and find the solution.

Post-work 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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The first is to help enterprises see the current situation clearly (that is, to build a common data index system);

The second is to temporarily analyze the reasons for the change of indicators, which is very common, but it is also the most headache, sometimes the reasons have not been analyzed, the indicators may have changed again, pay attention to identify the pseudo demand in this (the data itself fluctuates, what kind of changes are abnormal fluctuations?). Generally, [mean -2 * standard deviation, mean + 2 * standard deviation] is used as the reference range, and individual activities are treated differently);

The third is thematic analysis, which can be large or small, depending on the demand side (or possibly the data analyst himself), and the special analysis proposed by the big boss is relatively more difficult and level;

Fourth, the in-depth explanation of the relationship and the future, the technical difficulty and business understanding level are relatively higher. For example, what are the key factors that affect GMV? Of course, this is not the obvious number of paying users and the average order value, but the hidden factors that need to be explored; Another example, the GMV for the next quarter or even a year, and how to achieve it?

Analyze data: Analyze data and obtain useful information and knowledge through mathematical and statistical knowledge.

Visualize data: Visualize data and display data through charts, images, etc.

Develop a data strategy: Based on the results of data analysis, develop a data strategy and provide recommendations to the company about the data.

Provide data support: Provide data support to provide effective data support for the company's business decision-making and strategic planning.

Data: Leverage data models and machine learning techniques to conduct future data to provide the company with insights into future trends.

Complete data reports and documentation: Based on the results of data analysis, complete data reports and documentation, and provide detailed reports to the company's senior management and other departments.

Collaborate with team members and other departments: Work with team members and other departments to work together on projects and tasks.

Stay up-to-date with the latest technology and tools: Stay up-to-date with the latest technology and tools, and keep learning and updating your knowledge.

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.

The process of working as a data analyst is simply divided into two parts, the first part is to get the data, and the second part is to process the data. So how do you get the data? First of all, we need to know that obtaining relevant data is the premise of data analysis.

Every enterprise has its own set of storage mechanisms. Therefore, a basic SQL language is a must. If you have a basic SQL foundation and learn the syntax of the details, you can basically get a lot of data.

When each requirement is clear, the relevant data should be obtained as needed to make basic data.

Once the data is obtained, the data processing can be carried out. Getting the data and processing it into what you want is a key point. Many times, having data is not the end, but the beginning of the analysis.

The most important job of a data analyst is to process the data according to the requirements, and only by combining the data with the requirements can the value of the data be brought into play and the problems and essence of the requirements can be seen.

What is a Data Analyst Certificate?

What is a Data Analyst Certificate?

Data analytics helps make business decisions by talking about data to deliver value to their company, using data to answer questions, and communicating results. The general job of a data analyst includes data cleansing, performing analysis, and data visualization.

Depending on the industry, a data analyst may have different titles (e.g., Business Analyst, Business Intelligence Analyst, Business Operations Analyst, Data Analyst) Regardless of the title, a data analyst is a generalist who can adapt to different roles and teams to help others make better data-driven decisions.

In-depth data analysts.

Data analysts have the potential to transform traditional business methods into data-driven business methods. While data analysts are the entry level in the broad field of data, not all analysts are low-level. Data analysts are not only proficient in technical tools, but also effective communicators, and they are critical to companies that separate technical and commercial teams.

Their core role is to help others track progress and optimize goals. How can marketers use the data analytics to help them plan their next campaign? How do salespeople measure which type of people are better at?

How can CEOs better understand the underlying reasons behind recent company developments? These questions need to be answered by data analysts who analyze data and present the results. The complex work they do with data can add value to their organizations.

An effective data analyst can take the guesswork out of business decisions and help the entire organization grow rapidly. A data analyst must be an effective bridge across different teams. By analysing new data and synthesizing different reports, the overall output is translated.

This, in turn, helps organizations stay alert to their own developments.

The different needs of a company determine the skill requirements of a data analyst, but the following should be universal:

Wash and organize unprocessed data.

Use descriptive statistics to get a global view of your data.

Analyze interesting trends found in your data.

Create data visualizations and dashboards to help companies interpret descriptions and use data to make decisions.

Present the results of scientific analysis for commercial customers or internal teams.

Data analysts bring significant value to both the technology and sub-technologies of a company. Whether it's an exploratory analysis or a dashboard that interprets the state of your business. Analysts have fostered closer connections between teams.

1. Data collection

The significance of data collection is to truly understand the original appearance of the data, including the time, conditions, pattern, content, length, and constraints of the data. This will help big data analysts control the data production and collection process in a more targeted manner, and avoid data problems caused by violating data collection rules. Together, knowledge of the data collection logic increases the data analyst's knowledge of the data, especially the anomalous changes in the data.

2. Data access

Data access is divided into two parts: storage and extraction. Data storage, big data analysts need to understand the internal operation mechanism and process of data storage, the core is to know what processing needs to be done on the basis of the original data, and what kind of data is finally obtained.

3. Data extraction

Big data analysts first need to have a talent for data extraction. The first level is the ability to extract data conditionally from a single database; The second layer is the ability to grasp the ability to extract data across databases and tables; The third layer is to optimize SQL sentences, which are optimized for nesting, selecting logical levels and the number of traversals, etc., to reduce personal time waste and system resource consumption.

4. Data mining

At this stage, big data analysts should grasp, first, the basic principles and knowledge of data mining, statistics, and mathematics; The second is to be proficient in using a data to discover things, python or r are optional; Third, it is necessary to understand the commonly used data mining algorithms, as well as the use scenarios and advantages and disadvantages of each algorithm.

5. Data analysis

6. Data visualization

In this part, in addition to following the principle of unified standards for each company, the specific form of big data analysts should also be determined according to practical needs and scenarios. Data visualization is always complemented by data content, and valuable data reporting is the key.