What skills should a big data mining engineer have

1. Education, as far as the three large Internet companies are concerned, the requirements for big data engineers are all expected to be a master's or doctoral degree with a background in statistics and mathematics. Data workers who don't have a theoretical background are more likely to get into a danger zone of skills – a bunch of numbers that can always come up with results based on different data models and algorithms, but if you don't know what that means, it's not really meaningful, and that kind of result is easy to mislead you. Only with certain theoretical knowledge can we understand, reuse, and even innovate models to solve practical problems.

2.Computer coding skills, practical development skills, and large-scale data processing capabilities are some of the essential elements to be a big data engineer. Because a lot of the value of data comes from the process of mining, you have to get your hands dirty to discover the value of gold.

3.For the knowledge of a specific application field or industry, the role of a big data engineer is very important, and it cannot be separated from the market, because big data can only generate value when combined with the application of a specific field. Therefore, the experience in one or more vertical industries can accumulate knowledge of the industry for the candidate, which is very helpful for the future to become a big data engineer, so it is also a more convincing plus point when applying for this position.

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1. Programming statistical language

Data mining relies heavily on programming, and according to K.D. Nuggets, R and Python are the most popular programming languages in data science.

2. Big data processing framework

Hadoop, Storm, Samza, Spark, Flink, processing frameworks perform calculations on the data in the system, which can be divided into 3 categories: batch only, stream only, and hybrid.

3. Operating system: Linux

Linux is a popular operating system that is more stable and efficient for working with large data sets.

4. Database knowledge: relational and non-relational databases

To manage and work with large datasets, it is necessary to have knowledge of relational databases, such as SQL or Oracle, or non-relational databases, the main types of which are: columns such as Cassandra, HBase; Files: mongodb, couchdb; Key Values:

redis，dynamo。

5. Basic statistical knowledge

Basic knowledge of statistics is essential for data miners to help you identify problems, arrive at more accurate conclusions, distinguish between causality and correlation, and quantify the certainty of findings.

6. Data structures and algorithms

Data structures include arrays, linked lists, stacks, queues, trees, hash tables, collections, etc., while common algorithms include sorting, searching, dynamic programming, recursion, etc. Proficiency in data structures and algorithms is essential for data mining, which can provide you with more creative and efficient algorithmic solutions when working with large amounts of data.

Data mining engineers need to have a background in mathematics and statistics, computer coding skills, and knowledge of a specific application area or industry.

1.Data personnel who lack theoretical background are more likely to enter a danger zone of skills - some numbers, according to different data models and algorithms can always drum up some results, only with basic theoretical knowledge, can we truly understand the model, reuse the model and innovate the model to solve practical problems.

2.Practical development capabilities and large-scale data processing capabilities are some of the necessary elements to become a big data engineer. Because much of the value of the data is derived from the mining process, you have to get your hands dirty to discover the value of the gold.

Even in some teams, the responsibility of a big data engineer is primarily business analytics.

3.It is very important that the role of a big data engineer cannot be separated from the market, because big data can only generate value when it is combined with applications in specific fields. Therefore, experience in one or more vertical industries can accumulate knowledge of the industry for candidates, which is of great help to the future career of big data engineers.

The course mainly cultivates students' hard data mining theory and Python data mining algorithm skills, and also takes into account the cultivation of students' soft data governance thinking, business strategy optimization thinking, mining business thinking, algorithm thinking, and analytical thinking, so as to improve students' data insight in an all-round way. The course is based on scenario-based teaching to mobilize students' practical ability in data mining, in the business scenarios designed by the lecturer, the lecturer constantly raises business problems, and then the students gradually think and operate to solve the problem, so as to help students master the truly excellent data mining ability to solve business problems. Click here to book a free trial lesson.