Dry Goods: Several tool options for visualizing data (Tools + programming language)

Non-programming articles/tools that can be used directly

1. Excel

Excel is the easiest charting tool to handle fast, small amounts of data. Combined with a pivot table, the VBA language makes it possible to make tall visual analysis and dashboard dashboards.

Single-or single-graph Excel is the rule, and it can show results quickly. But the more complex the report, Excel, whether in the template production or data calculation performance is slightly insufficient, any large enterprises will not use Excel as the main tool for data analysis.

2. Visualize bi (Power BI \tableau \ Sail Soft Finebi, etc.)

Perhaps Excel is also aware of its limitations in data analysis and the current trend of self-help analysis, and Microsoft has launched BI tools Power bi in recent years. As with the visualizer tableau and the domestic soft bi tool, it encapsulates the programming code for all possible analysis operations, which are implemented by clicking and dragging, and the positioning of several tools is slightly different.

Power BI

The biggest obvious is the provision of interactive, drillthrough dashboards that use power pivot to directly produce pivot reports, eliminating the pivot table.

Tableau

The visualization chart is rich, is a class, the operation is more simple.

Sail Soft Finebi

Enterprise-Class BI applications, more practical, due to the 2B market is concerned about the big heat. Petabyte level data performance can be guaranteed, business attributes are heavier, can be linked to various types of business.

For individuals, easy to get started, you can free up more time to learn the analysis of business logic.

Programming Chapter

For the search for a higher-level data analyst or data scientist, if you master visual programming skills, you can use the data to do more things. Master Some programming skills, give the data analysis work more flexible ability, all kinds of data can adapt. Most of the most innovative and stunning data graphs can be implemented in almost any code or drawing software.

As with any language, you can't start a conversation right away. Start with the basics and build your learning style gradually. It's likely that you've started writing code before you realize it. The coolest thing about programming is that once you have mastered a language, it's easier to learn other languages because their logical thinking is common.

1. Python language

The greatest advantage of the Python language is its ability to handle large volumes of data, which does not cause downtime. Especially suited for complex computational and analytical work, and Python's syntax is clean and readable, and many modules can be used to create data graphs that are more popular with IT staff.

Charts generated with Python

2. PHP language

The language of PHP is loose but very conditioning, with good function is very powerful. In the field of data analysis, PHP can be used as a crawler to crawl and analyze millions other web data, and Hadoop can be combined with a large amount of statistical analysis of data.

Because most WEB servers have pre-installed PHP open-source software, eliminating the deployment of such work, can be directly handwritten.

For example, the Sparkline (micro-grid) library, which allows you to embed small-size miniature charts in text, or add visual elements to a numeric table, as in the following picture:

The micro-Line table generated by PHP graph function Library

PHP is typically used in conjunction with MY SQL database, which makes it possible to work with large datasets.

3. HTML, JavaScript, and CSS languages

Many visual software is Web-based, visual development, these kinds of language work. And as people rely more and more on browser work, Web browsers are becoming more and more sophisticated, with HTML, JavaScript, and CSS to run the visualization program directly.

Interactive calendar, also a heat map for users to use Your.flowingdata

However, there are a few points to note. Due to the relatively new software and technology, your design may differ in the display in different browsers. In older browsers such as Internet Explorer 6, some tools may not work correctly. For example, some banking units still use IE, regardless of their own use or development of the time to consider such a problem.

4. R language

The R language is the most favorite analysis software for most statisticians, open source is free and the graphics function is very powerful.

Speaking of the history of the R language, it is designed for data analysis and is also for statisticians, data scientists. But because the data analysis is more and more popular, the use of R language is not too thin so many restrictions.

The use of R is very concise, there are many tools supporting R, just load the data into R, write one or two lines of code to create a data graph. For example, use the Portfolio Toolkit to quickly create the following plate-level charts.

Like a heat map.

Heat map generated by R

There are, of course, many traditional statistical charts.

Dry Goods: Several tool options for visualizing data (Tools + programming language)