There are benefits at the end of the article

I used to work in a consulting company. When you hear a consulting company, you think of a suit and tie, shuttling around in a top office building. Ok, I admit it, but it’s not as glamorous as you think…

In fact, we are also working for our father, the client is not satisfied, everything is useless, but also have to work day and night. People always say that consulting, is to spend money to buy a beautiful PPT.

Half true, half wrong. Why? Powerpoint presentations are absolutely beautiful, but the solutions and logic they present are where your money is really going.

But to be honest, the PPT presented by our consulting industry is absolutely the most awesome in any industry in China. If you spend the same money on others, they will not be able to make it. So the question is, what is the PPT of consulting industry like? As it involves some confidential information of the company, I have found some sample drawings for you.

The most beautiful part of PPT should be the data visualization, which is also the essence of the whole PPT. It can directly see the growth point and weak point of the enterprise. As you all know, there are several common ways to do data visualization:

If Excel can master VBA with ease, can play all kinds of flowers, no matter with the help of the chart plug-in foreign aid can also kill a lot of white players in a second.

Can program, Echarts dozens of lines of code, every minute show; R language to play a first-hand data mining, visualization is no problem; Python data crawler, master matplotlib.

With all these tools, which one is better to start with? That’s what a lot of people struggle with.

In fact, data analysts don’t have to covet tools. For most of us, the need is to figure out some pattern from thousands of data, or some business problem that needs to be solved by analyzing data.

More value of analysis tools lies in assisting data processing in the process of analysis, which can show the conclusion in a visual way and finally solve the problem. If the function can avoid writing formulas and codes, easy to use, so much the better.

Tableau, Powerbi, FineBI at home and abroad are sharp tools for data visualization, which one to choose? I have also used Tableau, but it is not in line with my habit of thinking. After all, it is a foreign product. With the recommendation of my colleagues, I used FineBI.

What do FineBI data visualizations look like?

Ok, without further ado, let’s give you a hands-on experience of FineBI from analyzing data to finally making data visualization.

At the end of the article there are download links, data sources and visual materials, welcome to receive ~

FineBI data analysis actual combat

I. First acquaintance with FineBI

Download it and activate it (link at the end of this article) to go to the screen above.

Connect/import data

If you want to do data analysis, the first step must be to import data. FineBI can import data from a variety of data sources, such as the two mainstream open source platforms (Hadoop, Spark), Excel, CSV, XML, and various databases (SQL Server, Oracle, MySQL, etc.).

1. Database connection: connect to mysql

2. Import data

Step 1: Data preparation – Add the business package, which is used to unify the tables. Create a typhoon packet here. Click Add Table to create a new Excel dataset.

The following data details are obtained. Here you can automatically identify the field type of the data, and you can also modify the field type.

At this point, the data has been imported successfully, and the formal analysis begins.

Visual analysis

This data, which I pulled from the Internet, shows the information of typhoon arrivals in China over the past 60 years, including the time, landfall province and typhoon intensity.

In this way, we can summarize the provinces and cities most frequented by typhoons in the past years, which time period of a year is typhoon frequent days, and the distribution of typhoon intensity.

Analysis 1: The number of typhoons landing in China in each year

After adding the data set, enter the analysis screen. Drag the fields you want to analyze (number of records – a metric for counting typhoons, time of landfall – only the year dimension is shown here)

It should be noted that one typhoon in the source data table has multiple rows of records, because typhoons may land in two areas at the same time and record two pieces of information, so the number of records depends on CMA number statistics (the small triangle to the right of the number of records is pulled down) to avoid duplication.

Secondly, an index is added to calculate the average number of typhoons each year.

Finally, add a little more polish to the diagram, usually in graphic properties and component styles:

① Modify line color: Graphic properties – Color

② Change the line to a smooth curve, and adjust whether there are marked points

③ Modify the component title: Component style – Title, can adjust the font style

The final figure is as follows:

The number of typhoons that landed in China showed a fluctuation of 2~4 years, and the number of typhoons that directly hit China decreased slightly since 2000.

Analysis 2: Distribution of coastal cities in provinces where typhoon landfall — data map

Here is a demonstration of the production of data map, using the map to visually show the distribution of typhoon landing in China’s coastal provinces and cities.

Dimensions must be created as map characters, generating latitude and longitude.

The province (longitude) and province (latitude) fields are generated after the data is matched. Then drag the fields to the horizontal and vertical axes, respectively, to automatically generate a fill map. In addition, there are point maps, thermal maps, and so on. Here, fill maps for example.

Fill maps, as the name suggests, use the color of the area to distinguish the size of the value. Here drag the record number to the graphics property – Color to see the distinction (color can be selected in the drop down box). Then drag the record number to the label, you can display the number of typhoons landing in the province.

In component Styles – Background, you can modify the GIS map styles as follows:

Note: Styles for chart components, such as title name (font size and color), axis, color scheme, chart layout, etc., can be selected from Graph Properties and component Styles. There are so many options, please use your own aesthetic talents! Operations such as numerical calculation and filtering and sorting can be found in the drop-down boxes of index dimensions on the horizontal and vertical axes.

What? Want to make a visualization like this?

Each module is in accordance with the above operation can, followed by their favorite background color, I calculated, make the above effect, I only took 12 minutes, completely their own hands, I believe you can also!

This is just the tip of the iceberg of FineBI, and there are many other functions. Due to the limited space, we will not expand them for the time being, and we will keep updating in the future. Welcome to follow.

If necessary, you can pay attention to my column, from why enterprises do data analysis to how enterprises do data analysis, I guarantee that these are all you have not seen, a bottle of water money, can not buy a loss, can not buy deceived.

conclusion

In fact, data visualization is a science! It’s a tricky balance to strike between cool and business value. If you’ve seen some really cool effects, you know what it means to be too distracting, to distract from the content, and that’s not good.

Whether it is data analysis, or data visualization, the core or data empowerment business, otherwise it is useless.

Small screen for work, large screen for decision-making; Microsoft for office, soft for business.

Follow my public account “Data analysis is not a thing”, reply “material”, you can get data analysis gift package.