To learn data mining, you must define your own development direction and goals. Many people are not clear about the direction of development in the data industry. Let us first talk about the career development direction of the data industry.

The data industry can be broadly divided into the following positions:

Data mining engineers mostly search for the existence mode of data by mining massive data, so as to solve specific problems through data mining. It is more directed at a specific problem and is oriented towards solving specific problems. For example: cluster analysis, through the analysis of various demographics and behavioral data of members, classify customers, better understand customers, and know exactly what company members are? High, medium, low and low value customers can provide guidance for the operation of the later company, improve the efficiency of the activity, and guide the company's marketing.

To do data mining projects, you must be proficient in the database. Many times, the data preprocessing of your model may be completed in the database, and the database skills you use are higher. Must have mature data mining tools, data mining algorithms, etc. Of course, if you will have one or two open source software, and will write some program code that is the best, big companies like to use open source software.

Data analysts pay more attention to the interpretation of data and data indicators, and solve business problems by analyzing data. Mainly include: (1) Business Monitoring: Is the current business diagnosed as normal? Is there a problem? Has the business development reached expectations? If the expectations are not met, where are the main questions? What is the original cause? (2) Establish an analysis system: These data analysts already have a certain understanding of the business, and are relatively familiar with the business. More help the business party to establish an analysis system, or more advanced is to make data products. For example: marketing activities. The analyst will tell the business to determine what data you should analyze before the event to develop an appropriate marketing plan. In the marketing process, what data should you look at in order to make timely adjustments to marketing activities. In marketing activities, how to conduct activity evaluation and trend analysis of the future development of the industry.

Business analysts conduct business analysis at the industry and macro level, predict the future development of the industry, the business composition of competitors, help the company to develop strategic development plans, and timely track and analyze market dynamics, so as to continuously optimize the strategy in a timely manner. Main skill requirements: Be familiar with basic statistical analysis knowledge, and may also require mastery of website analysis tools for websites related to the business.

This position is fundamentally different from data mining engineers. Data modelers are more inclined to medium and small data volumes, and their use is more statistical methods. Data modelers rarely mention the word algorithm. But sometimes, these two models have no clear division of labor. Generally speaking, people in two positions will learn the other's knowledge, so these two positions have a tendency to merge.

New students entering the data industry can choose the corresponding positions according to their own background background. The friends who learn data and statistics can be more inclined to the modeler, while the computer, especially the students who write programming, can go the data mining engineer. Maybe it's better, but it's not absolute.

Sharpening the knife does not mistake the woodworker. There are a few things to understand before learning data mining:

Data mining is not yet popular in China, just like the skills of dragons.

The initial preparation of data usually accounts for about 70% of the total data mining project workload.

Data mining itself incorporates disciplines such as statistics, databases, and machine learning, and is not a new technology.

Data mining technology is more suitable for business personnel to learn (more efficient than technicians learning business)

Data mining is suitable for areas that traditional BI (reports, OLAP, etc.) cannot support.

Data mining projects often require duplication of work that is unskilled.

If you feel comfortable with reading the above, then keep looking down.

Learning a technology should be close to the industry, and there is no industry background technology such as castles in the air. The technology development in technology, especially in the computer field, is broad and fast-changing (the company can be established in web design ten years ago), and the average person does not have all the technical details in this energy and time. But technology can be unique when it comes to the industry. On the one hand, it helps to grasp the pain points and rigid needs of users, on the other hand, it can accumulate industry experience, and using Internet thinking cross-border makes it easier for you to succeed. Don't want to be all about learning technology, it will lose your core competitiveness.

1) Data analysts: Do business consulting, business intelligence, and analysis reports in industries such as e-commerce, finance, telecommunications, and consulting with industry data.

2) Data mining engineers: Implement and analyze machine learning algorithms in big data related industries such as multimedia, e-commerce, search, and social.

3) Scientific research direction: Research on the efficiency improvement and future application of new algorithms in high-level research institutions such as universities, research institutes and enterprise research institutes.

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