In fact, simple terms, is a large decision support through data analysis and data mining of non-sampling the full amount.
Application of large data can be implemented may be summarized in two directions, one custom precision, the second is predicted. For example, the same content as the search through the search engine, the result is that everyone is very different. Another example of precision marketing, the promotion of Baidu, Taobao like to recommend, or you go to a place, automatically give you the recommended consumption around the facility, and so on.
With the rapid development of large data industry, also will be some problems, such as lack of urgently needed to resolve the problem is a big data talent, so many people learn big data there have been some problems, that is, we are worried is zero-based big data can not learn, will not be hard to learn?
Big Data learning is not inscrutable, though not so simple for the zero-based student who, but if you are serious about learning, coupled with a professional teacher's guidance and targeted training, I believe you also can fully grasp the big data.
Zero-based learning students big data development can not be anxious, to a phased step by step to a complete step by step, it can be divided into four steps:
The first stage: to understand the basic concept of Big Data
First, a course of time, this course should have a simple understanding, for example, you must first learn some professional terms this course, learn some introductory concepts You know what to do so the course is, the main learn what. So we must learn to know what big data is big data, the use of big data is the field general who avoid their own blind study began in the case of large data ignorant.
In the process of getting started big data have met learning, industry, the lack of systematic learning path, learning systems planning, you are welcome to join my big learning data exchange skirt: 251 956 502, skirt documents have my years of study manual sorting of large data , development tools, PDF document with a book, you can download yourself.
The second stage: to learn computer programming language
for zero-based small partners, it may not be so easy to get started, you need to learn a lot of theoretical knowledge, reading boring textbooks. Because to master a computer programming language, or very difficult. We all know that there are a lot of computer programming languages, such as: R, C ++, Python, Java , and so on.
The third stage: big data related course of study
after the first two stages of the learning foundation, we have mastered the basic programming language, then you can be part of the curriculum Big Data learning. Xiao Bian here to remind everyone: industry really big data, 82% of the speakers are hadoop, spark ecosystem, storm development in real time, be sure to recognize beginner you need to learn is not really big data!
The fourth stage: project combat phase of
combat training can help what has been learned us better understand, while strengthening the memory of the relevant knowledge. In actual use later, you can get started faster, but also have experience in the use of methods related knowledge.
Nothing is impossible for a willing heart, whether you are there or not, no basis basis or, if you seriously study big data will certainly learn.
Subsequent increase
of large data combined with artificial intelligence can achieve true data scientists.
Machine learning: more than one field is interdisciplinary, involving probability theory, statistics, approximation theory, convex analysis, algorithmic complexity theory and other subjects. It is the core of artificial intelligence, is to make computers intelligent fundamental way, throughout all areas of application of artificial intelligence, it is mainly the use of induction, rather than a comprehensive interpretation. Machine learning algorithms compare the basic fixed, relatively easy to learn together.
Deep learning: the concept of deep learning comes from studies of artificial neural network is developing rapidly in recent years. Examples of applications are deep learning AlphaGo, face recognition, image detection. Is scarce talent at home and abroad, but the depth is relatively difficult to learn, algorithm updates faster, we need to follow the teacher's learning experience.
The fastest way to learn, is under the tutelage of industry experts, after all, teachers have many years of experience, their own detours to achieve a multiplier effect