The rise of the Python language makes everyone very interested in web, crawlers, data analysis, data mining, etc. What are the employment prospects for data mining? Regarding the answer to this question, everyone must first know what data mining is. The so-called data mining refers to the non-trivial process of revealing hidden, previously unknown and potentially valuable information from a large amount of data in the database.
Data mining is based on artificial intelligence, machine learning, pattern recognition, statistics, databases, visualization technology, etc., highly automated analysis of enterprise data, making inductive reasoning, and digging out potential patterns from it to help decision-makers adjust market strategies. Reduce risks and make correct decisions. So in today's society, is data mining promising? If mastering the technology of data mining can help us find a good job smoothly, I will talk to you today.
Why should we pay attention to data mining in the era of big data? Data mining is a business process that probes large amounts of data to discover meaningful patterns and rules. When it comes to discovering patterns and rules, it is actually a business process that serves the business. What we have to do is to make the business easier, or directly help customers how to improve their business.
Find meaningful patterns and rules in large amounts of data. In the face of large amounts of data, the acquisition of data is no longer an obstacle, but an advantage. At present, many technologies perform better on large data sets than on small data sets-you can use data to generate wisdom, or you can use computers to do what they do best: asking questions and solving problems. The definition of patterns and rules: to find patterns or rules that are beneficial to the business. Discovering patterns means targeting retention activities as the customers most likely to churn. This means optimizing customer acquisition resources, considering not only the short-term benefits of the number of customers, but also the medium and long-term benefits of customer value.
Now companies have more requirements for data mining skills. Currently, the positions on the market are generally divided into three types: algorithm model, data mining, and data analysis. Algorithm model positions have the highest requirements for mathematical statistics knowledge, need to study existing models and propose improvements, and it is best to be familiar with a programming language.
Data mining positions have lower requirements for mathematical statistics knowledge than algorithm positions, but it is better to also be a mathematical statistics major, who can understand the formula derivation process, understand the principle of the algorithm, understand the meaning of the parameters, and have a certain programming ability and proficiency Using java or python, you can write codes that meet industry requirements by calling third-party machine learning libraries. For data analysis positions, basic statistics are enough, and certain SQL skills are required, that is, the requirements for mathematics and programming are low, but the business is also demanding. You need to understand the industry, understand the business, and be able to come up with good ideas. The three positions need to cooperate and complement each other, each with its own focus.
Opinion supplement:
Python emphasizes programmer productivity, allowing you to focus on logic rather than the language itself. Can you imagine a simple search engine starting from 0 in an afternoon? C++ obviously doesn't work.
Most of your time will be spent implementing basic data structures and debugging language errors.
With python, all you have to do is to truly understand the search algorithm, and the subsequent implementation is really simple.
I think python is very suitable for algorithm research, not just data mining. Rapid development allows you to quickly verify your ideas, instead of wasting time on the program itself (Imagine you write a week of C++, adjust a lot of pointer errors, and finally find that the idea itself is wrong...) You know that you already have a correct algorithm. To make it run faster, you only need to rewrite the performance bottleneck in C++ and embed it.
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