Non-programming articles/tools that can be used directly
1. Excel
Excel is the easiest charting tool to use, and it is good at handling quick and small amounts of data. Combined with pivot table and VBA language, you can create high-level visual analysis and dashboard dashboards.
Excel is the only rule to make a single table or a single graph, it can quickly show the results. However, the more complex the report is, excel is slightly insufficient in both template creation and data calculation performance, and any large enterprise will not use Excel as the main tool for data analysis.
2. Visual BI (Power BI \Tableau \ FanRuan FineBI, etc.)
Perhaps Excel is also aware of its limitations in the field of data analysis and the current trend of self-service analysis. Microsoft has launched the BI tool Power BI in recent years. Like the visualization tool Tableau and the domestic FanRuan BI tool, it encapsulates the programming code for all possible analysis operations. The operations are implemented by clicking and dragging, and the positioning of several tools is slightly different.
Power BI
The biggest obvious one is to provide an interactive, drill-through dashboard, using Power Pivot to directly produce pivot reports, eliminating the need for pivot tables.
Picture
The visual charts are richer and can be called first-class, and the operation is simpler.
FanRuan FineBI
Enterprise-level BI applications, with strong practicability, have attracted attention due to the popularity of the 2B market. The petabyte-level data performance can be guaranteed, the business attributes are heavy, and it can be linked to various businesses.
For individuals, it is easy to get started and free up more time to learn the analysis of business logic.
Programming articles
For the data analyst or data scientist looking for a higher level, if you master visual programming skills, you can do more with data. Familiar with some programming skills, endow data analysis work with more flexible capabilities, and can adapt to various types of data. Most freshly designed, stunning data graphs can almost be implemented with code or graphing software.
As with any language, you can't start a conversation right away. Start with the basics and build up your own learning style. Chances are, before you know it, you're already writing code. The cool thing about programming is that once you master one language, it's easier to learn other languages because they share the same logic.
1. Python language
The biggest advantage of the Python language is that it is good at processing large amounts of data, and its good performance will not cause downtime. It is especially suitable for complex calculation and analysis work. Moreover, Python's syntax is clean and easy to read, and many modules can be used to create data graphics, which are more popular with IT personnel.
Charts generated with Python
2. PHP language
PHP is a loose but well-regulated language, and it is very powerful when used properly. In the field of data analysis, php can be used as a crawler to crawl and analyze millions of web page data, and it can also be combined with Hadoop for statistical analysis of large amounts of data.
Because most Web servers have PHP open source software installed in advance, the work of deployment and the like is omitted, and it can be directly written by hand.
For example, the Sparkline (microline table) library, which allows you to embed small-size micro-charts in text, or add visual elements to a table of numbers, like this one:
Micro-line table generated by using PHP graphic function library
Typically PHP is used in conjunction with a MY SQL database, which makes it ideal for handling large datasets.
3. HTML, JavaScript and CSS languages
Many visualization software are based on the web, and these languages are indispensable for the development of visualization. Moreover, as people rely more and more on browsers, the functions of Web browsers are also becoming more and more perfect. With the help of HTML, JavaScript and CSS, programs that can be displayed visually can be directly run.
An interactive calendar that is also a heatmap for users using your.flowingdata
There are a few things to note though. Since the related software and technologies are still relatively new, your design may appear differently in different browsers. Some tools may not work properly in older browsers such as Internet Explorer 6. For example, some banking units still use IE, and they must consider such issues whether they are using it themselves or developing them.
4. R language
R language is the most favorite analysis software for most statisticians. It is open source and free, and has powerful graphics functions.
Talking about the history of R language, it is designed for data analysis, and also for statisticians, data scientists. However, due to the increasing popularity of data analysis, the use of R language is not so limited.
The use process of R is very simple, and there are many toolkits that support R. You only need to load the data into R and write a line or two of code to create a data graph. For example, use the Portfolio toolkit to quickly create the following section hierarchy diagram.
such as heatmaps
Heatmap generated by R
Of course there are many traditional statistical charts.