How to conduct data analysis, how to conduct data mining, and establish data analysis thinking

what is data collection

what is Data mining

what is data visualization

How to learn data analysis and data mining

Data mining process

Quickly process data with NumPy

Several important concepts of data analysis

How to label, how to make user portraits

How to automatically collect data

How to use crawler to automate

How to perform data cleaning

How to perform data integration, the method of data integration

python in visualization

Visual chart

5

Basic code reading ability

Install python development software

Do you have this question: I also know that the ability to analyze data is very important, but is it difficult to analyze it How do you learn it In fact, there are some misunderstandings here. Data analysis is not out of reach. It is not difficult. It is important to master efficient learning methods.

In the final analysis, the core of learning data analysis is to cultivate data thinking, master mining tools, practice proficiently and accumulate experience. In order to bring you a better learning effect, I have designed four modules in the course.

1. Preview articles. I will introduce you to the panorama of data analysis and further explore the best learning path with you. I also specially prepared 3 Python introductory content. If you don't have a Python foundation, I hope to help you get started quickly. If you have mastered Python, you can use it as a review. This arrangement is because Python is a well-deserved trump language in the field of data science, and many data analysis tools are also based on Python. Next, I will take you to practice data thinking, from the basic concepts of data analysis to data collection, data processing and data visualization. Together, we understand all aspects of data from the entire process of data preparation.

2. Algorithms Algorithms are the essence of data mining and the focus of our course. I have selected 10 algorithms, including classification, clustering and prediction. We understand each algorithm from the two dimensions of principle and case to achieve the purpose of learning and using.

3. Practical project actual combat is an important level for us to learn. I have prepared 5 projects to bring you a real experience. For example, in the financial industry, how to use data analysis algorithms to analyze credit card default rates Today's Internet products have entered the stage of artificial intelligence with thousands of people. How to build a video recommendation algorithm for a video website

4. For the job article, I have selected a few workplace issues that everyone cares about most, such as what to pay attention to during the interview, what is the path of job promotion, etc., to help you.

I hope that through this course, you will have the following gains.

1. Data and algorithm thinking is not only a technical thinking mode, but also a way of thinking about problem solving. If you regard data as wealth and data analysis as a tool to obtain wealth, then in the era of big data, you will gain a broader perspective.

2. Tools Make good use of tools, you will have the ability to collect data, process data, and get results. It will make you more comfortable at work.

3. Better job opportunities and value Whether it is the current popular artificial intelligence or the market of data algorithm eeers, data analysis and data processing capabilities are highly valued. From "thinking" to "tools" to "practice", follow this path to expand the boundaries of their capabilities and have stronger competitiveness.

Students learning IT skills

Data analyst




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