![\"\"](\"http:\/\/www.finereport.com\/en\/wp-content\/uploads\/2019\/07\/2019072601L.png\")
<\/figure>\n\n\n\nWhat is the process of data analysis?<\/strong><\/h3>\n\n\n\nData analysis is mainly divided into three\nsteps<\/p>\n\n\n\n
1. Data Collection<\/p>\n\n\n\n
That is to take raw materials, we can’t\nanalyze without data.<\/p>\n\n\n\n
2. Data Mining<\/p>\n\n\n\n
Data mining is the value of the entire\nbusiness. The core of data mining is to mine the commercial value of data,\nwhich is what we call business intelligence.<\/p>\n\n\n\n
3. Data Visualization<\/p>\n\n\n\n
Simply put, let us intuitively understand the results of data analysis.<\/p>\n\n\n\n![\"\"](\"http:\/\/www.finereport.com\/en\/wp-content\/uploads\/2019\/07\/2019072602L-1024x533.png\")
<\/figure>\n\n\n\nTalking like this may be too simple, let me introduce you to these three steps in detail.<\/p>\n\n\n\n
Data collection<\/strong><\/h4>\n\n\n\nIn the data collection section, you usually\nwork with different data sources and then use tools to collect them.<\/p>\n\n\n\n
On the web you can collect a wide variety of data sets. There are also many tools that can help you automatically scrape data. Of course, if you write a Python crawler, it will be even more efficient. The fun of mastering Python crawlers is endless. It not only allows you to get hot reviews on social media, automatically downloads posters with keywords, but also automatically adds fans to your account, giving you the thrill of automation.<\/p>\n\n\n\n![\"\"](\"http:\/\/www.finereport.com\/en\/wp-content\/uploads\/2019\/07\/2019072603L-1-1024x567.png\")
<\/figure>\n\n\n\nData mining<\/strong><\/h4>\n\n\n\nThe second part is data mining, which can\nbe compared to the “algorithm” part of the entire data analysis\nprocess.<\/p>\n\n\n\n
First you need to know its basic flow, the\ntop ten algorithms, and the mathematical foundation behind it.<\/p>\n\n\n\n
In this part, we will come into contact\nwith some concepts, such as association analysis, Adaboost algorithm, etc. You\nmay just have a little knowledge of these concepts. It doesn’t matter. I will\nintroduce this knowledge to you in detail later.<\/p>\n\n\n\n
Mastering data mining is like holding a\ncrystal ball. It uses historical data to tell you what will happen in the\nfuture.<\/p>\n\n\n\n
Of course it will also tell you how confident this is. I will also explain the definition of confidence in a later article.<\/p>\n\n\n\n![\"\"](\"http:\/\/www.finereport.com\/en\/wp-content\/uploads\/2019\/07\/2019072604L-1-1024x592.png\")
<\/figure>\n\n\n\nData visualization<\/strong><\/h4>\n\n\n\nThe third is data visualization, which is a\nvery important step that we are particularly interested in. Data is often\nimplicit, especially when data is large, and visualization is a good way to\nunderstand the structure of the data and the presentation of the results. How\nto visualize data? There are two ways.<\/p>\n\n\n\n
The first is to use Python. In the process\nof cleaning and mining data in Python, we can use third-party libraries such as\nMatplotlib and Seaborn to render.<\/p>\n\n\n\n
The second is to use third-party tools. If you have already generated a csv format file and want to use WYSIWYG to render it, you can use third-party tools such as Data GIF Maker, Tableau, FineReport<\/a>, etc., which can easily process the data and help you make the presentation. <\/p>\n\n\n\nThe principles of data collection and data visualization are simple and easy to understand. These two parts focus on the mastery of the tools, so I will focus on introducing the use of tools.<\/p>\n\n\n\n![\"\"](\"http:\/\/www.finereport.com\/en\/wp-content\/uploads\/2019\/07\/2019072605L-1024x479.png\")
<\/figure>\n\n\n\nOf course, these theories are relatively abstract, so I think the best way to learn data analysis is to use them in tools and deepen understanding in projects.<\/p>\n\n\n\n
Practice guide<\/strong><\/h3>\n\n\n\nJust now we talked about the data analysis\npanorama, including data acquisition, data mining, and data visualization. You\nmay feel that there are a lot of things, you can’t start, or you feel that data\nmining involves many algorithms, and some are difficult to master. In fact,\nthese are unnecessary troubles.<\/p>\n\n\n\n
Here we introduce the MAS (Multi-dimension,\nAsk, Share) learning method. With this method, learning data analysis is a\nprocess from “thinking” to “tool” to “practice”.\nToday I will share my learning experience with you from more angles. We can\ncall today’s content a “practice guide.”<\/p>\n\n\n\n
We turn knowledge into our own language,\nand it really becomes our own thing.<\/strong> The process of\nthis transformation is the process of cognition.<\/p>\n\n\n\nSo how to improve your ability of learning?\nSimply put, it is to “know and do.”<\/p>\n\n\n\n
If cognition is the brain, tools are like\nour hands, and data engineers and algorithm scientists deal with the tools\nevery day.<\/p>\n\n\n\n
If you start to do data analysis projects, have already thought about the algorithm model of data mining in your mind, please keep in mind the following two principles.<\/p>\n\n\n\n
1.Do not repeat producing wheels<\/strong><\/h4>\n\n\n\nAs an example of data collection, I have\nseen many companies that have data collection needs. They think that some tools\ncan’t meet their individual needs, so they decided to recruit people to do this\nwork. What happened? After more than a year of practice, the wages invested\nhundreds of thousands, found a lot of bugs, and finally chose third-party\ntools. At this time, in fact, with timely assessment of need, and cooperation\nwith FineReport, you can save losses in a timely manner.<\/p>\n\n\n\n
2. Tools determine efficiency<\/strong><\/h4>\n\n\n\n\u201cDon’t repeat producing wheels\u201d means you\nfirst need to find a wheel that can be used, which is a tool. How do we choose?<\/p>\n\n\n\n
It depends on the work you are going to do.\nThe tools are not good or bad, only suitable or not. In addition to\nresearch-type work, in most cases, engineers will choose the most user-friendly\ntools. Because: Bug is , documents are complete, and there are many cases.<\/p>\n\n\n\n
For example, Python has a lot of\nthird-party libraries for handling data mining. These libraries have a large\nnumber of users and help files to help you get started.<\/p>\n\n\n\n
In the following lessons, I will introduce\nyou to the most commonly used tools that will make your data mining more\neffective.<\/p>\n\n\n\n
After choosing a good tool, all you have to\ndo is accumulate \u201cassets\u201d. It’s hard to remember a lot of knowledge points, and\nwe can’t follow the instructions of the tools, but we can usually remember the\nstories, the projects we have done, and the problems we have done. These topics\nand projects are your first “assets”.<\/p>\n\n\n\n
How to quickly accumulate these\n“assets”? Here I send you a word: proficiency. Solving the problems\nis only the first step. The key is to train the \u201cproficiency\u201d used by our\ntools.<\/p>\n\n\n\n
As proficiency increases, your thinking cognitive model is gradually improving, and efficiency will naturally increase.<\/p>\n\n\n\n
Conclusion<\/strong><\/h3>\n\n\n\nCognitive trilogy, from cognition to tools\nto actual combat, is the learning advice I most want to share with you. After\nreading this article, be sure to start practicing!<\/p>\n\n\n\n
Finally, to learn about the next tutorial, welcome to follow <\/em>FineReport Reporting Software.<\/em><\/a><\/p>\n\n\n\nYou might also be interested in\u2026<\/h2>\n\n\n\n
How Can Beginners Design Cool Data Visualizations?<\/a><\/p>\n\n\n\nA Beginner\u2019s Guide to Business Dashboards<\/a><\/p>\n\n\n\n
<\/figure>\n\n\n\nTalking like this may be too simple, let me introduce you to these three steps in detail.<\/p>\n\n\n\n
Data collection<\/strong><\/h4>\n\n\n\nIn the data collection section, you usually\nwork with different data sources and then use tools to collect them.<\/p>\n\n\n\n
On the web you can collect a wide variety of data sets. There are also many tools that can help you automatically scrape data. Of course, if you write a Python crawler, it will be even more efficient. The fun of mastering Python crawlers is endless. It not only allows you to get hot reviews on social media, automatically downloads posters with keywords, but also automatically adds fans to your account, giving you the thrill of automation.<\/p>\n\n\n\n<\/figure>\n\n\n\nData mining<\/strong><\/h4>\n\n\n\nThe second part is data mining, which can\nbe compared to the “algorithm” part of the entire data analysis\nprocess.<\/p>\n\n\n\n
First you need to know its basic flow, the\ntop ten algorithms, and the mathematical foundation behind it.<\/p>\n\n\n\n
In this part, we will come into contact\nwith some concepts, such as association analysis, Adaboost algorithm, etc. You\nmay just have a little knowledge of these concepts. It doesn’t matter. I will\nintroduce this knowledge to you in detail later.<\/p>\n\n\n\n
Mastering data mining is like holding a\ncrystal ball. It uses historical data to tell you what will happen in the\nfuture.<\/p>\n\n\n\n
Of course it will also tell you how confident this is. I will also explain the definition of confidence in a later article.<\/p>\n\n\n\n<\/figure>\n\n\n\nData visualization<\/strong><\/h4>\n\n\n\nThe third is data visualization, which is a\nvery important step that we are particularly interested in. Data is often\nimplicit, especially when data is large, and visualization is a good way to\nunderstand the structure of the data and the presentation of the results. How\nto visualize data? There are two ways.<\/p>\n\n\n\n
The first is to use Python. In the process\nof cleaning and mining data in Python, we can use third-party libraries such as\nMatplotlib and Seaborn to render.<\/p>\n\n\n\n
The second is to use third-party tools. If you have already generated a csv format file and want to use WYSIWYG to render it, you can use third-party tools such as Data GIF Maker, Tableau, FineReport<\/a>, etc., which can easily process the data and help you make the presentation. <\/p>\n\n\n\nThe principles of data collection and data visualization are simple and easy to understand. These two parts focus on the mastery of the tools, so I will focus on introducing the use of tools.<\/p>\n\n\n\n<\/figure>\n\n\n\nOf course, these theories are relatively abstract, so I think the best way to learn data analysis is to use them in tools and deepen understanding in projects.<\/p>\n\n\n\n
Practice guide<\/strong><\/h3>\n\n\n\nJust now we talked about the data analysis\npanorama, including data acquisition, data mining, and data visualization. You\nmay feel that there are a lot of things, you can’t start, or you feel that data\nmining involves many algorithms, and some are difficult to master. In fact,\nthese are unnecessary troubles.<\/p>\n\n\n\n
Here we introduce the MAS (Multi-dimension,\nAsk, Share) learning method. With this method, learning data analysis is a\nprocess from “thinking” to “tool” to “practice”.\nToday I will share my learning experience with you from more angles. We can\ncall today’s content a “practice guide.”<\/p>\n\n\n\n
We turn knowledge into our own language,\nand it really becomes our own thing.<\/strong> The process of\nthis transformation is the process of cognition.<\/p>\n\n\n\nSo how to improve your ability of learning?\nSimply put, it is to “know and do.”<\/p>\n\n\n\n
If cognition is the brain, tools are like\nour hands, and data engineers and algorithm scientists deal with the tools\nevery day.<\/p>\n\n\n\n
If you start to do data analysis projects, have already thought about the algorithm model of data mining in your mind, please keep in mind the following two principles.<\/p>\n\n\n\n
1.Do not repeat producing wheels<\/strong><\/h4>\n\n\n\nAs an example of data collection, I have\nseen many companies that have data collection needs. They think that some tools\ncan’t meet their individual needs, so they decided to recruit people to do this\nwork. What happened? After more than a year of practice, the wages invested\nhundreds of thousands, found a lot of bugs, and finally chose third-party\ntools. At this time, in fact, with timely assessment of need, and cooperation\nwith FineReport, you can save losses in a timely manner.<\/p>\n\n\n\n
2. Tools determine efficiency<\/strong><\/h4>\n\n\n\n\u201cDon’t repeat producing wheels\u201d means you\nfirst need to find a wheel that can be used, which is a tool. How do we choose?<\/p>\n\n\n\n
It depends on the work you are going to do.\nThe tools are not good or bad, only suitable or not. In addition to\nresearch-type work, in most cases, engineers will choose the most user-friendly\ntools. Because: Bug is , documents are complete, and there are many cases.<\/p>\n\n\n\n
For example, Python has a lot of\nthird-party libraries for handling data mining. These libraries have a large\nnumber of users and help files to help you get started.<\/p>\n\n\n\n
In the following lessons, I will introduce\nyou to the most commonly used tools that will make your data mining more\neffective.<\/p>\n\n\n\n
After choosing a good tool, all you have to\ndo is accumulate \u201cassets\u201d. It’s hard to remember a lot of knowledge points, and\nwe can’t follow the instructions of the tools, but we can usually remember the\nstories, the projects we have done, and the problems we have done. These topics\nand projects are your first “assets”.<\/p>\n\n\n\n
How to quickly accumulate these\n“assets”? Here I send you a word: proficiency. Solving the problems\nis only the first step. The key is to train the \u201cproficiency\u201d used by our\ntools.<\/p>\n\n\n\n
As proficiency increases, your thinking cognitive model is gradually improving, and efficiency will naturally increase.<\/p>\n\n\n\n
Conclusion<\/strong><\/h3>\n\n\n\nCognitive trilogy, from cognition to tools\nto actual combat, is the learning advice I most want to share with you. After\nreading this article, be sure to start practicing!<\/p>\n\n\n\n
Finally, to learn about the next tutorial, welcome to follow <\/em>FineReport Reporting Software.<\/em><\/a><\/p>\n\n\n\nYou might also be interested in\u2026<\/h2>\n\n\n\n
How Can Beginners Design Cool Data Visualizations?<\/a><\/p>\n\n\n\nA Beginner\u2019s Guide to Business Dashboards<\/a><\/p>\n\n\n\n
<\/figure>\n\n\n\nData mining<\/strong><\/h4>\n\n\n\nThe second part is data mining, which can\nbe compared to the “algorithm” part of the entire data analysis\nprocess.<\/p>\n\n\n\n
First you need to know its basic flow, the\ntop ten algorithms, and the mathematical foundation behind it.<\/p>\n\n\n\n
In this part, we will come into contact\nwith some concepts, such as association analysis, Adaboost algorithm, etc. You\nmay just have a little knowledge of these concepts. It doesn’t matter. I will\nintroduce this knowledge to you in detail later.<\/p>\n\n\n\n
Mastering data mining is like holding a\ncrystal ball. It uses historical data to tell you what will happen in the\nfuture.<\/p>\n\n\n\n
Of course it will also tell you how confident this is. I will also explain the definition of confidence in a later article.<\/p>\n\n\n\n<\/figure>\n\n\n\nData visualization<\/strong><\/h4>\n\n\n\nThe third is data visualization, which is a\nvery important step that we are particularly interested in. Data is often\nimplicit, especially when data is large, and visualization is a good way to\nunderstand the structure of the data and the presentation of the results. How\nto visualize data? There are two ways.<\/p>\n\n\n\n
The first is to use Python. In the process\nof cleaning and mining data in Python, we can use third-party libraries such as\nMatplotlib and Seaborn to render.<\/p>\n\n\n\n
The second is to use third-party tools. If you have already generated a csv format file and want to use WYSIWYG to render it, you can use third-party tools such as Data GIF Maker, Tableau, FineReport<\/a>, etc., which can easily process the data and help you make the presentation. <\/p>\n\n\n\nThe principles of data collection and data visualization are simple and easy to understand. These two parts focus on the mastery of the tools, so I will focus on introducing the use of tools.<\/p>\n\n\n\n<\/figure>\n\n\n\nOf course, these theories are relatively abstract, so I think the best way to learn data analysis is to use them in tools and deepen understanding in projects.<\/p>\n\n\n\n
Practice guide<\/strong><\/h3>\n\n\n\nJust now we talked about the data analysis\npanorama, including data acquisition, data mining, and data visualization. You\nmay feel that there are a lot of things, you can’t start, or you feel that data\nmining involves many algorithms, and some are difficult to master. In fact,\nthese are unnecessary troubles.<\/p>\n\n\n\n
Here we introduce the MAS (Multi-dimension,\nAsk, Share) learning method. With this method, learning data analysis is a\nprocess from “thinking” to “tool” to “practice”.\nToday I will share my learning experience with you from more angles. We can\ncall today’s content a “practice guide.”<\/p>\n\n\n\n
We turn knowledge into our own language,\nand it really becomes our own thing.<\/strong> The process of\nthis transformation is the process of cognition.<\/p>\n\n\n\nSo how to improve your ability of learning?\nSimply put, it is to “know and do.”<\/p>\n\n\n\n
If cognition is the brain, tools are like\nour hands, and data engineers and algorithm scientists deal with the tools\nevery day.<\/p>\n\n\n\n
If you start to do data analysis projects, have already thought about the algorithm model of data mining in your mind, please keep in mind the following two principles.<\/p>\n\n\n\n
1.Do not repeat producing wheels<\/strong><\/h4>\n\n\n\nAs an example of data collection, I have\nseen many companies that have data collection needs. They think that some tools\ncan’t meet their individual needs, so they decided to recruit people to do this\nwork. What happened? After more than a year of practice, the wages invested\nhundreds of thousands, found a lot of bugs, and finally chose third-party\ntools. At this time, in fact, with timely assessment of need, and cooperation\nwith FineReport, you can save losses in a timely manner.<\/p>\n\n\n\n
2. Tools determine efficiency<\/strong><\/h4>\n\n\n\n\u201cDon’t repeat producing wheels\u201d means you\nfirst need to find a wheel that can be used, which is a tool. How do we choose?<\/p>\n\n\n\n
It depends on the work you are going to do.\nThe tools are not good or bad, only suitable or not. In addition to\nresearch-type work, in most cases, engineers will choose the most user-friendly\ntools. Because: Bug is , documents are complete, and there are many cases.<\/p>\n\n\n\n
For example, Python has a lot of\nthird-party libraries for handling data mining. These libraries have a large\nnumber of users and help files to help you get started.<\/p>\n\n\n\n
In the following lessons, I will introduce\nyou to the most commonly used tools that will make your data mining more\neffective.<\/p>\n\n\n\n
After choosing a good tool, all you have to\ndo is accumulate \u201cassets\u201d. It’s hard to remember a lot of knowledge points, and\nwe can’t follow the instructions of the tools, but we can usually remember the\nstories, the projects we have done, and the problems we have done. These topics\nand projects are your first “assets”.<\/p>\n\n\n\n
How to quickly accumulate these\n“assets”? Here I send you a word: proficiency. Solving the problems\nis only the first step. The key is to train the \u201cproficiency\u201d used by our\ntools.<\/p>\n\n\n\n
As proficiency increases, your thinking cognitive model is gradually improving, and efficiency will naturally increase.<\/p>\n\n\n\n
Conclusion<\/strong><\/h3>\n\n\n\nCognitive trilogy, from cognition to tools\nto actual combat, is the learning advice I most want to share with you. After\nreading this article, be sure to start practicing!<\/p>\n\n\n\n
Finally, to learn about the next tutorial, welcome to follow <\/em>FineReport Reporting Software.<\/em><\/a><\/p>\n\n\n\nYou might also be interested in\u2026<\/h2>\n\n\n\n
How Can Beginners Design Cool Data Visualizations?<\/a><\/p>\n\n\n\nA Beginner\u2019s Guide to Business Dashboards<\/a><\/p>\n\n\n\n
<\/figure>\n\n\n\nData visualization<\/strong><\/h4>\n\n\n\nThe third is data visualization, which is a\nvery important step that we are particularly interested in. Data is often\nimplicit, especially when data is large, and visualization is a good way to\nunderstand the structure of the data and the presentation of the results. How\nto visualize data? There are two ways.<\/p>\n\n\n\n
The first is to use Python. In the process\nof cleaning and mining data in Python, we can use third-party libraries such as\nMatplotlib and Seaborn to render.<\/p>\n\n\n\n
The second is to use third-party tools. If you have already generated a csv format file and want to use WYSIWYG to render it, you can use third-party tools such as Data GIF Maker, Tableau, FineReport<\/a>, etc., which can easily process the data and help you make the presentation. <\/p>\n\n\n\nThe principles of data collection and data visualization are simple and easy to understand. These two parts focus on the mastery of the tools, so I will focus on introducing the use of tools.<\/p>\n\n\n\n<\/figure>\n\n\n\nOf course, these theories are relatively abstract, so I think the best way to learn data analysis is to use them in tools and deepen understanding in projects.<\/p>\n\n\n\n
Practice guide<\/strong><\/h3>\n\n\n\nJust now we talked about the data analysis\npanorama, including data acquisition, data mining, and data visualization. You\nmay feel that there are a lot of things, you can’t start, or you feel that data\nmining involves many algorithms, and some are difficult to master. In fact,\nthese are unnecessary troubles.<\/p>\n\n\n\n
Here we introduce the MAS (Multi-dimension,\nAsk, Share) learning method. With this method, learning data analysis is a\nprocess from “thinking” to “tool” to “practice”.\nToday I will share my learning experience with you from more angles. We can\ncall today’s content a “practice guide.”<\/p>\n\n\n\n
We turn knowledge into our own language,\nand it really becomes our own thing.<\/strong> The process of\nthis transformation is the process of cognition.<\/p>\n\n\n\nSo how to improve your ability of learning?\nSimply put, it is to “know and do.”<\/p>\n\n\n\n
If cognition is the brain, tools are like\nour hands, and data engineers and algorithm scientists deal with the tools\nevery day.<\/p>\n\n\n\n
If you start to do data analysis projects, have already thought about the algorithm model of data mining in your mind, please keep in mind the following two principles.<\/p>\n\n\n\n
1.Do not repeat producing wheels<\/strong><\/h4>\n\n\n\nAs an example of data collection, I have\nseen many companies that have data collection needs. They think that some tools\ncan’t meet their individual needs, so they decided to recruit people to do this\nwork. What happened? After more than a year of practice, the wages invested\nhundreds of thousands, found a lot of bugs, and finally chose third-party\ntools. At this time, in fact, with timely assessment of need, and cooperation\nwith FineReport, you can save losses in a timely manner.<\/p>\n\n\n\n
2. Tools determine efficiency<\/strong><\/h4>\n\n\n\n\u201cDon’t repeat producing wheels\u201d means you\nfirst need to find a wheel that can be used, which is a tool. How do we choose?<\/p>\n\n\n\n
It depends on the work you are going to do.\nThe tools are not good or bad, only suitable or not. In addition to\nresearch-type work, in most cases, engineers will choose the most user-friendly\ntools. Because: Bug is , documents are complete, and there are many cases.<\/p>\n\n\n\n
For example, Python has a lot of\nthird-party libraries for handling data mining. These libraries have a large\nnumber of users and help files to help you get started.<\/p>\n\n\n\n
In the following lessons, I will introduce\nyou to the most commonly used tools that will make your data mining more\neffective.<\/p>\n\n\n\n
After choosing a good tool, all you have to\ndo is accumulate \u201cassets\u201d. It’s hard to remember a lot of knowledge points, and\nwe can’t follow the instructions of the tools, but we can usually remember the\nstories, the projects we have done, and the problems we have done. These topics\nand projects are your first “assets”.<\/p>\n\n\n\n
How to quickly accumulate these\n“assets”? Here I send you a word: proficiency. Solving the problems\nis only the first step. The key is to train the \u201cproficiency\u201d used by our\ntools.<\/p>\n\n\n\n
As proficiency increases, your thinking cognitive model is gradually improving, and efficiency will naturally increase.<\/p>\n\n\n\n
Conclusion<\/strong><\/h3>\n\n\n\nCognitive trilogy, from cognition to tools\nto actual combat, is the learning advice I most want to share with you. After\nreading this article, be sure to start practicing!<\/p>\n\n\n\n
Finally, to learn about the next tutorial, welcome to follow <\/em>FineReport Reporting Software.<\/em><\/a><\/p>\n\n\n\nYou might also be interested in\u2026<\/h2>\n\n\n\n
How Can Beginners Design Cool Data Visualizations?<\/a><\/p>\n\n\n\nA Beginner\u2019s Guide to Business Dashboards<\/a><\/p>\n\n\n\n
The principles of data collection and data visualization are simple and easy to understand. These two parts focus on the mastery of the tools, so I will focus on introducing the use of tools.<\/p>\n\n\n\n Of course, these theories are relatively abstract, so I think the best way to learn data analysis is to use them in tools and deepen understanding in projects.<\/p>\n\n\n\n Just now we talked about the data analysis\npanorama, including data acquisition, data mining, and data visualization. You\nmay feel that there are a lot of things, you can’t start, or you feel that data\nmining involves many algorithms, and some are difficult to master. In fact,\nthese are unnecessary troubles.<\/p>\n\n\n\n Here we introduce the MAS (Multi-dimension,\nAsk, Share) learning method. With this method, learning data analysis is a\nprocess from “thinking” to “tool” to “practice”.\nToday I will share my learning experience with you from more angles. We can\ncall today’s content a “practice guide.”<\/p>\n\n\n\n We turn knowledge into our own language,\nand it really becomes our own thing.<\/strong> The process of\nthis transformation is the process of cognition.<\/p>\n\n\n\n So how to improve your ability of learning?\nSimply put, it is to “know and do.”<\/p>\n\n\n\n If cognition is the brain, tools are like\nour hands, and data engineers and algorithm scientists deal with the tools\nevery day.<\/p>\n\n\n\n If you start to do data analysis projects, have already thought about the algorithm model of data mining in your mind, please keep in mind the following two principles.<\/p>\n\n\n\n As an example of data collection, I have\nseen many companies that have data collection needs. They think that some tools\ncan’t meet their individual needs, so they decided to recruit people to do this\nwork. What happened? After more than a year of practice, the wages invested\nhundreds of thousands, found a lot of bugs, and finally chose third-party\ntools. At this time, in fact, with timely assessment of need, and cooperation\nwith FineReport, you can save losses in a timely manner.<\/p>\n\n\n\n \u201cDon’t repeat producing wheels\u201d means you\nfirst need to find a wheel that can be used, which is a tool. How do we choose?<\/p>\n\n\n\n It depends on the work you are going to do.\nThe tools are not good or bad, only suitable or not. In addition to\nresearch-type work, in most cases, engineers will choose the most user-friendly\ntools. Because: Bug is , documents are complete, and there are many cases.<\/p>\n\n\n\n For example, Python has a lot of\nthird-party libraries for handling data mining. These libraries have a large\nnumber of users and help files to help you get started.<\/p>\n\n\n\n In the following lessons, I will introduce\nyou to the most commonly used tools that will make your data mining more\neffective.<\/p>\n\n\n\n After choosing a good tool, all you have to\ndo is accumulate \u201cassets\u201d. It’s hard to remember a lot of knowledge points, and\nwe can’t follow the instructions of the tools, but we can usually remember the\nstories, the projects we have done, and the problems we have done. These topics\nand projects are your first “assets”.<\/p>\n\n\n\n How to quickly accumulate these\n“assets”? Here I send you a word: proficiency. Solving the problems\nis only the first step. The key is to train the \u201cproficiency\u201d used by our\ntools.<\/p>\n\n\n\n As proficiency increases, your thinking cognitive model is gradually improving, and efficiency will naturally increase.<\/p>\n\n\n\n Cognitive trilogy, from cognition to tools\nto actual combat, is the learning advice I most want to share with you. After\nreading this article, be sure to start practicing!<\/p>\n\n\n\n Finally, to learn about the next tutorial, welcome to follow <\/em>FineReport Reporting Software.<\/em><\/a><\/p>\n\n\n\n How Can Beginners Design Cool Data Visualizations?<\/a><\/p>\n\n\n\n A Beginner\u2019s Guide to Business Dashboards<\/a><\/p>\n\n\n\n<\/figure>\n\n\n\nPractice guide<\/strong><\/h3>\n\n\n\n
1.Do not repeat producing wheels<\/strong><\/h4>\n\n\n\n
2. Tools determine efficiency<\/strong><\/h4>\n\n\n\n
Conclusion<\/strong><\/h3>\n\n\n\n
You might also be interested in\u2026<\/h2>\n\n\n\n