How to learn data analysis for beginners How to learn data analysis

If you want to engage in the data analysis industry, you still need to learn systematically, and generally cooperate with many project cases to learn in the learning process, which is easy to learn and understand, and you can also accumulate experience.

In general, the basics are learned first, then the theory, and finally the tools.

1. Learn the basics of data analysis, including probability theory, mathematical statistics 2, and relevant theoretical knowledge of your target industry. For example, in finance, it is necessary to learn various knowledge such as **, banking, and finance.

3. Learn data analysis tools, such as SAS, SPSS, and even Excel (the data analysis module is very powerful).

Remember, the first step is essential and is the foundation of data analysis.

Look up some information on the Internet, or go to a bookstore to find it.

First of all, we should start with the data, most of the general data analysis is mainly the analysis of sales data, that is, the reason behind the number is analyzed according to the difference in the number. To put it bluntly, I personally think it is causal analysis.

To start, start with the concept first, understand what is the month-on-month comparison, year-on-year, and many ratios. In the later stage, it all depends on the individual's sensitivity to numbers.

A good data analyst needs to have the following qualities: have a solid SQL foundation, be proficient in Excel, have a statistical foundation, master at least one data mining language (R, SAS, PYTHON, SPSS), have good communication and presentation skills, be ready for continuous learning, have strong data sensitivity and logical thinking ability, have a deep understanding of the business, have a manager's thinking, and be able to consider problems from the perspective of a manager.

1) Have business sensitivity, quick response, and be able to communicate well; 2) Project practical experience in data analysis and data warehouse modeling; 3) At least 3 years of experience in data analysis, with experience in Internet product and operation analysis; 4) Familiar with R, SAS, SPSS and other statistical analysis software, proficient in the use of Python, proficient in use.

SQL, Hive, etc.; 5) Bachelor degree or above, major in mathematics, statistics, computer science, operations research and other related majors; So for students who are in the introductory stage, how should they correctly grasp their learning direction?

If you plan to become a data analyst, you need to have basic knowledge in all three fields: statistics, databases, and economics; CET-4 or above, familiar with the English name of the indicator; Knowledge of Internet product design.

To answer the question, a data analyst needs to learn statistics, programming skills, databases, data analysis methods, data analysis tools, etc., be proficient in 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.

Data programming: Data programming tools include Python, R, SAS, etc., and the most popular ones are Python and SQL, which are relatively simpler.

Data storage: mainly databases, data modeling, these to understand may be a little difficult to understand, you can find free online courses on the Internet to assist learning.

Data visualization: Data visualization is not very difficult, this part can be completely self-taught, Tableau, Quich BI, etc.

Big data technology: machine learning is still relatively difficult, but I will deepen my understanding and learning in my work, so I can take a rough look at it and learn it when I encounter it in my work.

Royal Oak 1. Statistics. 2. Programming ability. 3. Databases. 4. Data warehouse.

5. Data analysis methods. 6. Data analysis tools.

One thing to focus on: language.

These are the most basic tools, Python is the best language to get started with data, while R language tends to be statistical analysis, graphing, etc., and SQL is the database. Since it is data analysis, I usually spend more time dealing with data analysis and data collection.

A series of data analysis tasks such as data cleaning and data visualization need to be completed by the above tools.

2.Business Capabilities, Data Analyst.

The meaning of existence is to help enterprises achieve business growth through data analysis, so business capabilities are also a must. The company's products, users, the market environment in which it is located and the employees of the enterprise are all content that must be mastered, and through the establishment of these contents, it helps the enterprise to establish specific business indicators and assist the enterprise in making operational decisions.

Of course, these are the most basic things that data analysts need to focus on learning if they want to change careers, and if they want to have a better development in the future, they also need to learn more skills, such as business management, artificial intelligence, etc.

Statistics, Programming, Data Database, Data Warehouse, Data Analysis Methods and Tools.

The choice of tools is very important for data analysts, and most state-owned enterprises and private enterprises are paying more and more attention to data security, and the data industry is beginning to tend to be localized. A large number of domestic manufacturers have also emerged in data analysis tools: a new data analysis and decision-making system led by the Shenzhen Institute of Mathematics - Guanhe Causal.

It is a relatively distinct representative in the field of data analysis, with technology, products and software all self-developed and produced in China; And the performance superiority is also in the forefront of the world; The product features can be found on the official website.

The first aspect is the basis of mathematics, the second is the basis of statistics, and the third aspect is the basis of computers. If you want to go further on the road of data analysis, you must pay attention to the study of mathematics and statistics. In the final analysis, data analysis is to find the rules behind the data, and finding the rules requires the ability to design algorithms, so mathematics and statistics are very important for data analysis.

If you want to become a data analyst quickly, you can start with computer knowledge, specifically from data analysis tools, and then in the process of learning tools, assist in algorithm and industry lethal learning. Learning data analysis tools often start with excel tools, excel is currently the most commonly used data analysis tool in the workplace, usually in the face of less than 100,000 pieces of structured data, excel is still competent. For most professionals, mastering the data analysis function of Excel can cope with most common data analysis scenarios.

After mastering Excel, the next step is to learn more about databases, starting with relational databases, with an emphasis on SQL language. After mastering the database, the data analysis ability will be greatly improved, and the amount of data that can be analyzed will also be significantly improved. If the database and BI tools are combined, the results of data analysis will be richer, and there will be a more intuitive presentation interface.

The last step of data analysis is to learn the programming language, at present learning the python language is a good choice, the python language is widely used in the field of big data analysis, and the python language itself is relatively simple and easy to learn, even people without programming foundation can learn it. Python is a popular way to analyze data by using machine learning.

Data analysis is a systematic knowledge structure, not just a tool, there are many students who have learned Python and SQL, after entering this field, they can only engage in some statistical work such as BI reports, he is not equal to data analysts.

According to the business of the enterprise, generally speaking, data operation is mainly to complete the work of data processing, such as calculating ROI, reports, data collation, data query and some statistical work, etc., while the work of data analysts not only needs to master the basic operation of some tools, but also needs to understand the business, be able to combine business knowledge and data, and be able to find business problems in the process of enterprise operation through various data of the enterprise and help the enterprise solve problems.

Therefore, it is necessary to avoid becoming a cousin and cousin, and to know which part of learning data analysis is the focus, data analysts, as an important hub of the enterprise, connect the company's product and operation departments, and play a vital role in the enterprise. This can be seen that the data analyst industry is still relatively special, because this position is not based on the actual things that can be seen, but a kind of "soft power", the requirements for the use of programming and tools are not high, it is not capable of using some tools, it needs to combine data and business knowledge, and also needs to do more projects to accumulate experience, Jiudaomen Data Analysis College believes that data analysts need to master some data processing tools, and need to have a business knowledge structure, You need to be able to combine business knowledge and data, and at the same time need to develop good analytical thinking habits, including some soft skills, so as to use the value of data to help enterprises solve problems and promote the development of enterprises.