Shulou(Shulou.com)06/03 Report--

Big data industry is currently very popular, but the development of big data is still not very mature, especially for some rookies. Understanding the systematic method of learning big data will help you to learn big data more quickly and effectively. Share how to learn from big data on Zero Basics.

First, for beginners, especially programming rookies, Linux, Java learning is necessary. But this does not mean that we have to study these thoroughly, we just need to learn the aspects that are beneficial to us big data, such as java, we only need to learn part of the knowledge of javaSE and javaEE. Because hadoop, hbase and spark in big data all run on linux, linux is also one of the required courses for us to introduce big data.

Second, to learn big data's main skills, such as Hadoop, HBase, Hive, Scala, Spark, Python and so on. It is not recommended to read books in the early stage.

Third, to cultivate the ability of self-study, many teaching resources are not readily available. We should consciously look for resource-based accounts such as communities, forums, blogs, and so on. This plays a great role in understanding the latest industry trends and opening up your knowledge.

In fact, many students learning big data responded that the efficiency of self-study is often very low, and self-study big data requires a very strong learning ability. And there are many influencing factors, so for many students, especially those with poor self-control ability, it is recommended to go to big data training institutions to study systematically. Big data in Gamigu has professional lecturers, members of the National big data Standards Group, a curriculum for enterprise practice, and real project training for front-line Internet companies.

I know that it is really difficult to learn big data by yourself, if you want to find someone to take you to learn, or learn.

If you don't know what to say in the process, you can + Q skirt: Wu Sanjie, lxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx Starting from the foundation, you can come to learn, of course, you want zero basic learning materials I can also provide free!

Big data is still becoming more and more popular in this industry, and learning from big data has gradually become a new direction in the IT field. Coupled with the continuous expansion of market demand and the continuous expansion of the talent gap, more developers, zero basic personnel, and scholars will enter this field in the future. it can be said that it is the right time to learn from big data in the "first year" when big data broke out.

Big data should have the courage to practice and not talk on paper: data science or data engineering?

Big data can produce value only by combining with applications in specific fields. Data science or data engineering is the key issue for big data to make clear in his study. To engage in academic paper data science OK, but to apply big data to the ground, if the achievements of data science are transformed into data engineering for landing application, it is very difficult, which is also the reason why many enterprises question the value of data science. Not to mention that this kind of transformation needs a process, the practitioners themselves also need to examine and think.

How does industry, including government regulatory agencies, introduce research intelligence, and how does data analysis transform and realize value? Data science researchers and enterprise big data system development engineers have to think about these key issues.

At present, the key problems to be solved in data engineering are data (Data) > knowledge (Knowledge) > service (Service), data collection and management, mining and analysis to acquire knowledge, knowledge rules for decision support and application into continuous service.

Only by solving these three problems can we calculate the landing of big data's application. From a learning point of view, DWS is the general goal of big data's study to solve problems, especially the practical application ability of data science, and practice is more important than theory. From model, feature, error, experiment, test to application, every step should consider whether the model can solve practical problems, whether the model can be explained, have the courage to try and iterate, the model and software package itself are not omnipotent, big data application should pay attention to robustness and effectiveness, greenhouse model is useless, training set and test set OK?

Big data how to get out of the laboratory and engineering landing, first, can not work behind closed doors, the model converged, of course, everything will be fine; second, to go out of the laboratory to fully dock with the actual decision-making problems of the industry; third, relevance and causality can not be less, the model that can not describe causality is not helpful to solve practical problems. Fourth, pay attention to the iteration and production of the model, continuous upgrade and optimization to solve the problem of incremental learning of new data and dynamic adjustment of the model.

Therefore, big data's study must be clear about whether I am doing data science or data engineering, what technical capabilities I need, and at what stage I am now, otherwise it is difficult for me to learn and make good use of big data for the sake of technology.

That's all for today's sharing. For more IT sharing, please follow the V letter: programmer Daniel! Forward comments and follow!

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