As we all know, Excel doesn’t mean you’re good at data analysis, talking about beer and diapers doesn’t mean you’re good at data analysis, and powerpoint doesn’t mean you’re good at data analysis… We do data analysis in order to be able to analyze business problems in a quantitative way and draw conclusions. There are two key words: quantitative and business.

Quantification is to unify cognition and ensure that paths are traceable and reproducible. After unified cognition, people from different levels and departments can be guaranteed to have equal discourse right and discuss and cooperate in the same direction, and only in this way can people in the company avoid judging the current business situation by “I feel” and “I guess”. At the end of the day, analytics can only create value if you solve the business problem.

Many colleagues and friends have been asking me how to become a data analyst and data product manager. What kind of knowledge is needed? Today xiaoyi will share with you what is a data analyst, and how to learn these skills and knowledge quickly and deeply?

What is a data analyst?

Data analyst refers to professionals in different industries who specialize in collecting, organizing and analyzing industry data, and make industry research, evaluation and prediction based on the data.

In other words, data analyst is a position. Anyone who has mastered data analysis methods and thinking, and does technology and business can be called a data analyst. In essence, its work is to analyze business value or model and discover knowledge from data, so as to promote business and assist decision-making.

How can data analysts create value for the enterprise?

A complete enterprise data analysis system involves many links: collection, cleaning, transformation, storage, visualization, analysis and decision-making, and so on. Among them, the work content of different links is different, and the time consumed and value produced are also quite different, as shown in the figure.

For example, the Internet enterprise data analysis system has at least three aspects of data: user behavior data, transaction order data and CRM data. Engineers collect data from different sources, and then unify the data platform through cleaning, transformation and other links; Then a special data engineer puts forward the data from the data platform. This work takes up 90% of the time, but generates only 10% of the value.

At the top of the pyramid, data analysis is closely combined with business practice to support business decisions in forms such as reports and visualizations, covering all first-line departments of product, operation, marketing, sales and customer support. This segment takes up only 10% of the time, but generates 90% of the value.

A good business data analyst should be value-oriented, closely integrating product, operations, sales, customer support and other practices to support each line of business to identify problems, solve problems and create more value.

What are the common types of data analysts?

1. Data Product manager

Add data thinking to the capabilities of product managers. The data product manager not only understands the principle of burying points, but also can capture data and analyze it through tools such as packet capture. At the same time, I can also participate in the production of data products, such as BI report, CRM system, AB Test test background, etc.

For example, the boss wants to build a user behavior monitoring platform. At this time, according to the boss’s needs, it needs to be transformed into detailed technical requirements and presented to the technology for development. This is the daily life of the data product manager.

2. Data analyst

This is what we call the commercialization of the data analyst, is mainly responsible for 0-1 structures, visual monitoring report, the use of data mining, and insight into the business, to provide data support for demand department, analysis report, commercial models, such as service, here is the core, value and service monitoring, mining, in the eyes of the leadership in the company and the role of the brain.

3. Data modeler

Data modelers, also called algorithm engineers, are model masters who integrate mathematical statistics knowledge, programming and business thinking. By establishing mathematical models and using algorithms to achieve growth, they can be said to be the soul workers of a product, such as recommendation algorithms for information flow products, anti-fraud and credit rating in the financial industry.

4. Data engineer

The function of data engineer more technology projects, main responsibility is to build a data warehouse, create, ETL, data management, data security, etc, through the ascending speed, optimize the structure of data, better service to use data, such as data analyst and data and data modeler product manager.

5. Data scientist

Comprehensive talents, with data analysis ability, statistical basis, business ability, algorithm and communication ability. Include all of the above skills and capabilities.

What competencies do you need to be a data analyst?

1. Business ability

In the end, data analysis can only truly create value by solving business problems. In other words, data analysts need business capabilities, and each business of an enterprise is essentially the support of the overall strategy of the company, because data analysts must first understand the strategy to choose the right direction of analysis.

Secondly, they should be sensitive enough to their own industry and fully understand the industry. That is, communicate more with the core team of the business department, pay more attention to the industry website, read more industry data analysis reports and do a good job of accumulation, such as what stage they are in, where they are, where the current key business direction, what challenges they have encountered, and what the overall solution is.

Finally, it is also necessary to have practical experience in business positions. The understanding of business is not simple to read the documents. It must come from a full understanding of the actual process, mechanism, platform, data and so on of the company’s business.

2. Data capability

As a data analyst, you will first need to understand the data index of the enterprise, each business enterprise a KPI index system, around the KPI index and the execution of a series of monitoring index, as a data analyst must to enterprise’s core index system has a deep understanding of, to distinguish index of difference from the essence, is the generation process of index have a thorough understanding of, Including from which table, which field layer calculation summary.

The second is to have a global data perspective. In most companies, a data analyst’s job is to specialize, but the data you analyze is all over the place. There are no professional boundaries. In practice, data analysts often do not know how much data there is, and the depth and breadth of their data analysis are limited due to their narrow vision. Data analysts should systematically study the data dictionary. Bottom-up practice is important, but top-down learning is also necessary.

Finally also need to know the depth of the data, data dictionary of data is often just the surface meaning, if you want to analyze more flexible, you need to understand the data dependencies between and story, because each data table by table association are documented in the next level, but the summary mean a loss of information, only have the ability to trace, You are more likely to have greater analytical freedom based on more information, such as seeing what the function of a menu on a business system needs to correspond to the data in the system.

3. Technical skills

As a data analyst, you still need to have the necessary skills, such as proficiency in SQL, database principles, Excel/ reporting /BI tools. In addition, upstream and downstream technical areas, such as data warehouse, data architecture, ETL, need to understand and even be able to use, such as:

(1) SQL is the most flexible language to operate data, any database will provide SQL support, it builds a bridge between business and data, easy to learn, cost-effective, but also data analysts must learn the language.

(2) EXCEL provides the most flexible ability to process and present lightweight data. Mastering EXCEL is the basic skill of any data analyst. Perspectives, charts, formulas and calculations are all extremely convenient tools.

(3) BI, to a large extent, is the art of using some visualization techniques to compare indicators, which can help you find and locate problems faster and more intuitively. After all, the human brain is more sensitive to graphs and images. BI Tool Intelligent Data Analysis Demonstration:

(4) Data mining technology, such as clustering, classification, prediction and so on. With the lowering of the threshold to use machine learning and artificial intelligence tools, data analysts should master at least one mining method. Know how to build models, especially in industries with high data maturity such as finance, carrier, Internet, retail, etc.

Communication skills

For data analysts, communication ability is very important, because many projects need to be promoted by the upper level, and then the leaders of each business department need to cooperate with you to clarify the data in requirements, and the implementation needs the cooperation of technology and the whole business chain.

The essence of communication is to solve problems. Clear communication purpose, clear logical expression, and then stand on the other side to consider what the other side wants, communication is not so difficult.

For example, in the communication, you should seize every opportunity to communicate clearly what the purpose of analysis is and what the leaders expect. At the same time, you also need to face different positions, meet different roles, use different languages, express your requirements and get what you need, such as how to understand the business? How to get data faster? How to identify the cause of data problems as soon as possible? Are tests of your actual contacts and authority.

In addition, another important expression of the data analyst is to report the results of the data analysis, to learn to tell the story of the problem and analysis scenarios, to be able to quantify the value of data and vivid scenarios.

How can I quickly become a data analyst?

1.Excel data analysis

Every data analyst needs Excel. It is the most commonly used tool for everyday work, and it can handle the vast majority of analysis, regardless of performance and data volume. While machine learning is now commonplace, Excel is still the undisputed number one tool. Excel is a tool you must be familiar with if you are inexperienced. It is the most commonly used tool for everyday work, and it can handle the vast majority of analysis, regardless of performance and data volume.

2.SQL database language

As data analysts, we first need to know how to get data, the most common of which is to get numbers from relational databases, so you can’t know R, you can’t know Python, but you can’t know SQL.

In the DT era, data is growing exponentially. Excel has no problem with data processing within 100,000 pieces, but to put it mildly, whenever a product has a little scale, the data is millions. This is where you learn about databases. For example, in the recruitment conditions of many enterprises, more and more product and operation positions, will SQL as a priority points. SQL is one of the core skills of data analysis, and moving from Excel to SQL is a big improvement in data processing efficiency.

Main knowledge of database query language, where, group by, orderby, having, like, count, sum, min, Max, distinct, if, join, left join, limit, and and or logic, time conversion function, etc. The fastest way to learn SQL is to download a database management tool and get some data practice. MYSQL is recommended for clients. Recommended book: MYSQL Must Know

3. Data visualization & Business intelligence

Data visualization is not only a technology, but also an art. The same data in the hands of different people will show different effects. Mastering this technology will be a plus in the workplace. There are many tools for visualization, such as BI Peas.

Wandou BI is a self-service data analysis platform for business personnel, which provides a complete solution from data import, data preprocessing, automatic modeling and data visualization. Users can quickly create an Agile Kanban board with a simple drag and drop. Recommended book: “Talk in Charts” – McKinsey Pictures

4. Mathematical statistics

Statistics is one of the most important foundations of data analysis, and is the cornerstone and methodology of data analysis. Statistical knowledge requires us to look at data in a different way. When you know how silly the difference between AB and AB looks on average, your analytical skills will improve dramatically.

Here we need from basic statistical theory (descriptive statistics, interval estimation, hypothesis testing, etc.), to the basic statistical analysis (T test, analysis of variance, etc.), and finally to business commonly used model (regression analysis, variance analysis, etc.), learning the logic behind the data analysis, and grasp the concept of practical statistics to use statistical thinking. Recommended books: The Statistical Basis of Zero-advance Data Analysis – Cao Zhengfeng, Statistics – Jia Junping

5. Data analysis and software application

SPSS is an introduction to statistical analysis, and I recommend using SPSS if you want to get started quickly without learning programming. SPSS software is one of the three major statistical analysis software in the world. It has been favored by data analysts for its advantages of easy operation, easy entry and easy reading of results. Generally, simple data analysis, including chart drawing, simple regression, correlation analysis and so on, can be done with SPSS after short-term learning.

The key point of learning SPSS is not the software itself, but the relevant statistical knowledge, which is also suggested in the previous section, that is, you should learn how to analyze “the results presented by the software after the input data”. Recommended books: “Add a tiger’s wing data processing SPSS/SAS EG implementation” – Xu Xiaogang, “The SPSS/SAS EG advanced data analysis with bamboo in mind” – Chang Guozhen, “SPSS statistical analysis basic tutorial + advanced tutorial” – Zhang Wentong

6. Data analysis industry application and data analysis thinking

For data analysts, understanding the business is more important than data methodology. Unfortunately, there are no shortcuts to business learning. Recommended books: Growth Hackers, Lean Data Analysis

06 — Summary

Different levels of data analysts in China work in different scenarios every day.

Basic data analyst, every day is basically to sort out data reports, write SQL, check data. No data analyst can skip this stage and needs to start at the bottom.

As a middle-level data analyst, I have the ability to work independently. In addition to doing some data report work, I will undertake special analysis on some independent problems, such as why the sales decline and the status quo of operation, and then build a set of data index system to describe the status quo and analyze problems.

Senior data analysts, or department heads/directors, are basically in meetings every day. Meetings of management and other business departments will be pulled up. They don’t do much with data. Most of their work is communication. Of course, in addition to meetings, I will also analyze problems, and sort out decision-making suggestions from a high-level perspective.

However, no matter what position you are in, data analysis is just the starting point. Using data driven business to drive enterprise management is the real end point of value. So get to know the business. See how the various departments carry out their work, get familiar with the business process, look at the reports, take the initiative to think and find problems, see how they turn problems into concrete actions. I believe that learning along this route, you will learn on the road of data analysis. Research data analysis software architecture diagram, can better digest and improve data analysis capabilities: YixinHuachen big data analysis, data governance product architecture diagram