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

This article shows you Python data acquisition and analysis as well as visual example analysis, the content is concise and easy to understand, can definitely brighten your eyes, through the detailed introduction of this article, I hope you can get something.

I had the same trouble when I studied data analysis at that time. I read a lot of materials, but I always had little knowledge. I didn't have a teacher to answer questions for you all the time, so I often felt like I was going to give up.

However, I have to say: it is absolutely right to learn data analysis, if you stick to it, you will see a completely different self.

How on earth should we learn data analysis? To tell you the truth, it's not hard! After working for so long, I have summed up three experiences:

"

1. Find a "coach" with excellent strength and experience, and take you "learn and use" from thinking, tools, and actual combat.

two。 To make a correct learning plan and path, what you really need is good method, not brute force.

3. The use of effective tools will make you get twice the result with half the effort.

"

The data analysis is divided into three parts: data collection, data visualization and data mining.

The data project of hand training

OK, let's share with you the knowledge that needs to be mastered in data collection, data visualization and data mining in the above picture.

data acquisition

You can use Python to collect data automatically, or you can use a third-party platform, such as octopus, to collect data.

Data visualization

Mainly use the Matplotlib tool of Python to do data visualization. Matplotlib is the visual basic library of Python, which is very suitable for starter learning. After learning the column, I can also make the following pictures, which are very eye-catching.

data mining

When you have mastered the basic operations in data analysis, it is time to formally process the data. In order to carry out data mining tasks, data scientists have proposed a variety of algorithms, which are divided into four categories according to their purpose:

Classification algorithm: C4.5, naive Bayesian (Naive Bayes), SVM,KNN,Adaboost,CART clustering algorithm: Kmure Meansline EM association analysis: Apriori connection analysis: PageRank

The ability of data analysis must be possessed by every Internet person. only by understanding data can we use data-driven, scientifically optimize our work, exercise our strong combat effectiveness and core competitiveness, and widen the gap with others.

The above content is Python data acquisition and analysis as well as visual example analysis. Have you learned the knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

Welcome to subscribe "Shulou Technology Information " to get latest news, interesting things and hot topics in the IT industry, and controls the hottest and latest Internet news, technology news and IT industry trends.