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

In fact, in simple terms, big data is to assist decision-making by analyzing and mining the full amount of non-sampled data.

The applications that big data can achieve can be summarized into two directions, one is precise customization, and the second is prediction. For example, searching the same content through search engines, everyone's results are very different. For example, precision marketing, Baidu promotion, Taobao like recommendation, or you go to a place, automatically recommend to you the surrounding consumption facilities and so on.

With the rapid development of the big data industry, there have also been some problems. For example, the lack of big data talents is a problem that needs to be solved urgently at present. Then many people who study big data have some problems again. What everyone is generally worried about is whether they can learn big data from zero basis and whether they will not learn well.

Big data learning is not enigmatic, although it is not so simple for zero-based students, but as long as you study hard, plus professional teacher guidance and targeted training, I believe you can also fully master big data.

Zero-based students can't rush to learn big data development. They should be completed step by step in stages, which can be roughly divided into four steps:

Stage 1: Understanding the basic concepts of big data

First of all, when learning a course, you should have a simple understanding of the course. For example, you should first learn some professional terms of the course, learn some introductory concepts, know what the course is about, and what the main learning knowledge is. Then learning big data must know what big data is. Generally, the application fields of big data are those to avoid blind learning without knowing anything about big data.

. In the process of getting started learning big data, there are encounter learning, industry, lack of systematic learning route, systematic learning planning, welcome to join my big data learning exchange skirt: 251956502, skirt file has my big data learning manual, development tools, PDF documents books, you can download by yourself.

Stage 2: Learning the computer programming language

For zero-based friends, it may not be so easy to get started. You need to learn a lot of theoretical knowledge and read boring textbooks. Because mastering a computer programming language is difficult. We all know that there are many computer programming languages, such as R, C++, Python, Java, etc.

Stage 3: Big Data Related Learning Courses

After the first two stages of basic learning, we have basically mastered the programming language, and then we can learn the big data part of the course. Here, Xiaobian wants to remind everyone in particular: real big data in the industry, 82% of the speakers are hadoop, spark ecosystem, storm real-time development, beginners must recognize whether you want to learn real big data!

Stage 4: Project Operational Stage

Practical training can help us better understand what we have learned and strengthen our memory of relevant knowledge. In the future practical application, you can get started faster, and you have experience in the use of relevant knowledge.

There is nothing difficult in the world, no matter if you have a foundation or not, as long as you study big data seriously, you will definitely learn well.

follow-up enhancement

Big data combined with artificial intelligence can reach true data scientists.

Machine learning: It is an interdisciplinary subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. It is the core of artificial intelligence, is the fundamental way to make computers intelligent, its application throughout all areas of artificial intelligence, it mainly uses induction, synthesis rather than deduction. Machine learning algorithms are basically fixed and relatively easy to learn.

Deep learning: The concept of deep learning stems from the study of artificial neural networks and has grown rapidly in recent years. Examples of deep learning applications include AlphaGo, Face Recognition, Image Detection, etc. It is a scarce talent at home and abroad, but deep learning is relatively difficult, algorithm update is also relatively fast, need to follow experienced teachers to learn.

The fastest way to learn is to learn from industry experts. After all, teachers have accumulated experience for many years. They can achieve twice the result with half the effort by taking fewer detours.

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