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A data analysis of cattle career planning


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My childhood ideal was to be a mathematician in the future. Unfortunately, when I grew up, I found that my talent was not enough, and my ideals gradually drifted away. So I started to think about reality and started to do some life planning. I have been thinking about what kind of career I will be engaged in in the future. A field that redefines one's own career ideals. My current career ideal is relatively simple, just to be a data analyst.

Why do data analysts?

In the communications, Internet, financial and other industries, there is a huge amount of data every day (long-term accumulation of a large amount of rich data, such as customer transaction data, etc.), it is said that by 2020, the annual amount of data generated worldwide will reach 3,500 trillion GB. Whether the massive historical data is valuable, can it be used to provide a reference for leadership decision-making? With the rapid development of software tools, database technology, and various hardware devices, it is possible to analyze massive amounts of data.

Data analysis is also receiving more and more attention from the leadership. Using the report to tell the user what has happened, using OLAP and visualization tools to tell the user why it happened, through the dashboard monitoring to tell the user what is happening now, telling the user through the forecast What might happen. Data analysis will extract and mine valuable knowledge and potential knowledge from business data, identify trends, provide a strong basis for decision-making, and play a positive role in the development direction of products or services, and effectively promote the internal science of enterprises. And information management.

for example

(1) Facebook advertising is connected with users of online communities such as Weibo and SNS. Through advanced data mining and analysis technology, it provides advertisers with more accurate positioning services. The precision advertising model has been well received by advertisers. According to market research firm eMarketer, Facebook's annual revenue exceeds $2 billion, making it the largest online display advertising provider in the United States.

(2) At the Hitwise conference, John, the person in charge of Asia Pacific, explained: 30% of Amazon's sales are from its system's automatic product recommendation, through customer classification, test statistics, behavior modeling, delivery optimization, four steps, operating customer behavior. Data brings a competitive advantage.

In addition, there are many, data analysis, applications in marketing, finance, Internet, etc. are very broad: for example, in the field of marketing, there are database marketing, precision marketing, RFM analysis, customer segmentation, sales forecast, etc.; in financial Predicting stock prices and their volatility, arbitrage models, etc.; in Internet e-commerce, Baidu's precision advertising, Taobao's data cube and so on. There will be more and more successful cases, so that data analysts are getting more and more attention.

However, reality is another situation. Let's look at a message from Weibo: There are currently 140,000 to 190,000 professionals with data analysis and management capabilities in the United States, and 1.5 million managers with understanding and decision-making capabilities (based on research on massive data). There is a shortage of talent for analysts. In China, the professionally trained and experienced data analysis talents, in the next three years, the gap between the supply and demand of analytical talents will gradually enlarge, and senior analysts are hard to find. In other words, the demand for data analysis is growing, but there are very few data analysts who are qualified to make analytical decisions. Many people want to do data analysis but don't know how to start, or they don't know how to clean the data, and use the data directly; or the model is set up, the head of the analysis is the road, in fact, it is not the case. As the saying goes: I have seen pigs running and have not eaten pork.

My career planning

For data analysis, there is a saying that is very good: software such as spss/sql, decision tree, time series and other methods, these are just tools, the most important is the grasp of the business. Without the correct business understanding, the theory of the cow, the tool of the cow, is no good. Be a qualified data analyst, in addition to a good sensitivity to the data needs, a deep understanding of the background of the relevant business, a clear understanding of the needs of customers or business units. Identify which data is available and which is not applicable based on actual business development, rather than analyzing it in a “vacuum environment” in isolation.

The specific plan is like this

The first step: master basic data analysis knowledge (such as statistics, probability, data mining basic theory, operations research, etc.), master basic data analysis software (for example, VBA, Matlab, Spss, Sql, etc.), master the basic business Economic common sense (such as macro-microeconomics, marketing theory, investment basics, strategy and risk management, etc.). These basic knowledge, I try to learn in school, and I came to Jun Business School, so that I can understand something in business analysis and economic analysis, and enhance my data analysis ability.

Step 2: Participate in various internships. At the beginning of the study, I had a class at the time, but I was fortunate enough to find a part-time job that only took one or two days a week. The content was to analyze the competitors for Samsung. Of course, the analysis framework was given by the leader. I just did the integration of information and The work of the content filled in ppt, but through part-time, I came into contact with the consulting industry, and also learned a lot of business analysis, thinking logic and the like to the formal employees. After that, I went to Siemens to do things with VBA. Although the work was not related to data analysis, the company often used VBA to do some automated processing work and laid the foundation for its own data analysis tools. After that, I went to the Easy Car, where I worked part-time for more than a month, and participated in the short-term forecast of Volkswagen sales data. After a small project, the data analysis method has mastered a lot, and I also learned how the company used some time. The sequence model is used to participate in the prediction, how to select a fitting curve as the predicted value. Now, I came to a new place for internship, and I was very fortunate to participate in the design of a terminal yard optimization system for a central enterprise. In fact, it is also a kind of data analysis. It implements scheduling through the data of the terminal and makes decisions through the data of the terminal. Finally written as an operational automation system. The most important thing about this project is the grasp of the business process. I also participated in the initial demand research of the project, and developed the work task specification SOW, which has a lot of experience.

The third step: the first job, is expected to be 3-5 years. I guess I would choose a consulting company or an IT company, mainly to do a strong analysis of data companies such as Fico, Accenture, Gaowo, Rainier, IBM, AC and so on. Through the first job, I will make my knowledge more solid, learn to apply what I have learned in practice, learn the process of data analysis, and let myself grow up.

Step 4: Go to an industry that you like, learn more about the industry, and apply data analysis to this industry. For example, I can go to e-commerce as a data analyst. I think that I choose e-commerce because the future is bound to be the era of the Internet. E-commerce will replace traditional business. The most obvious phenomenon is that traditional retailer boss Wal-Mart is being challenged by Amazon. In addition, e-commerce has better data collection and management capabilities than traditional retailers, which can better track users, tap potential users, and tap potential products.

Step 5: Unknown. I have no idea for the time being, but I hope that I am making progress.

Summary: Data Analyst's Capabilities and Goals

1. Be sure to understand the strategy and combine the business;

2, must be beautiful presentation, can buy;

3, must have a global view, in order to make a single;

4, must understand the business, in order to combine the market;

5, must have a few tools to work;

6, must learn well, in order to be efficient;

7, must have a strong theoretical basis, to get started;

8, must work hard to make money; the most important:

9, must be pragmatic, only have reputation;