The end of the year is once again the season of business analysis, summaries and reports, which means it's time for the bosses to start their massive "check-up" exercise.

Got a bunch of data and don't know how to start analyzing it? Got some conclusions and don't know how to present them? Boss tells you to talk with data not sure where to start? ......

When a year of operational work has produced a certain accumulation of data, how do you translate them into persuasive reports, and valuable conclusions and recommendations? The value of visualization comes into play. Graphical information is obviously much more accessible to the average person than cold data.

As a simple example, how to assess the performance of.

{'Alice':'3765',

'Cherry':'2464',

'Celina':'3258',

'Louise':'4832’,

'Kimi':'7628',

'Miya': '5564',

'Fiona':'4133',

'Aletta':'6498'}

Text, data ...... It is often not intuitive enough, and not only is it abstract and difficult to understand, there is a huge redundancy of information in the same field. What if it was represented graphically? Isn't that better!

The vast majority of the data, in fact, could have been presented in a better form that would have helped the reader understand while drawing more implied information and conclusions to the data analysis.

To give another example, to describe to the leader what users are saying about the product, POing all the reviews is one way, but of course, you can also give a word cloud graph based on word frequency statistics, where the tone and sentiment can be seen at a glance.

We see more and more of these word clouds in many analytic reports, and for the analysis and presentation of textual information, it can make a more powerful statement in a more aesthetically pleasing form and with a minimum of space.

For example, it's much more intuitive to show sales data for a product by location, as opposed to listing dozens of regions in a table, with a direct map visualization.

Visualization forms like maps are also being used more and more - after all, for geolocation-related data, map displays are unmatched. The user data, most of which now have geographical information, also provides a good data resource for map visualization.

Another example is that we want to show the active history of a user, a calendar graph is enough. Different colors are used to differentiate between user behaviors, shades of color are used to differentiate the level of behavior, and specific data can be viewed by clicking on specific dates.

On the one hand it enables a better presentation of data and on the other hand it gives a better way of interaction to the user/reader.

Data visualization, thus, is not just the skill of drawing graphics, but also a reliable ability to express, not limited to scenarios, not limited to the form of data sources, it trains a logic of expression.

It can help you both visualize tedious and huge amounts of data and output any information you want to express graphically. In the real world of work, one encounters many such scenarios as well.

For example, daily/weekly business data, a large screen with real-time data updates, clear and interactive, and friendly enough for information presentation.

If you need a report, or to describe one thing through a lot of data (e.g. how the business has grown this year, user follow-up processing records, etc.), an infographic can help you sort out a good logic.

Another example is backend statistics, user interface statistics, and business analytics, data analysis and visualization can give you a better presentation and dramatically improve the user experience.

So, data visualization/information visualization is a versatile and cost-effective skill that can be migrated in any position. How can you learn and improve this skill when it has an important role in office, reporting, presentation and other scenarios?

Then first we need to go through the basic implementation process, the basic process of implementing data visualization is as follows.

1.For operations, what data are available and what data are still needed.

2.The graphs through which the data can be visualized and analyzed.

3.What conclusions can be drawn from the exploration of visualizations.

4.Visual presentation of conclusions and construction of appropriate story/debriefing logic.

This process actually corresponds to the following skill points.

1.Drawing of graphics: tools and code capabilities

There are many tools for visualization, Tableau, Echarts and Illustrator are recommended, covering many job requirements and enough to cope with the vast majority of visualization scenarios: data analysis reports, interactive visualizations, infographics.

2.Visual presentation: design and aesthetic skills

In addition to representing the information graphically, data visualization requires some effort in terms of layout and color schemes to make the graphics easy to read, attractive, and aesthetically pleasing.

3.Visual representation: logic and storytelling skills

When reporting and presenting conclusions, how to go about constructing a logic for the narrative is based, on the one hand, on familiarity with the business and, on the other hand, on thinking about the overall structure.

Great visualizations are a combination of information, storytelling, and visual form, but it's really not that hard to make great work once you master the use of the tools and basic visual representations.

We summarize the most common visualization scenarios and the skills needed in these scenarios (including tools and logical expressions, graphic design, etc.), and distill systematic visualization projects, fusing them into this all-encompassing, hands-on data visualization course.

This is probably the most comprehensive data visualization course on the web, with three tools, corresponding to three dimensions, and also three ways of data analysis and visual representation.

We take the three most commonly used tools Tableau, Echarts, and Illustrator as the core, and focus on training the most common visualization projects at work including data analysis reports, dynamic visual interaction pages, and storytelling infographics to cope with most visualization scenarios.

Even if you don't have the basics, can't design, and don't understand data analysis, you can still make professional visual charts with some specific methods and routines.

/ What to study in the course /

1. Tips for drawing common shapes

Includes techniques for drawing more than a dozen charts such as bar charts, pie charts, line charts, scatter charts, box line charts, heat maps, river charts, maps, calendar charts, and more, enabling better exploratory data analysis and data visualization.

2. Three visualization tools

Learn Tableau, Echarts, and Illustrator, three tools that cover most of the job skills needed and focus on training important skills such as visual analysis reports, interactive pages, and infographics, enough to handle different job scenarios.

3. Aesthetic and design skills

You will be more comfortable with basic design principles (visual coding and visual channels), color schemes and typographic solutions, daily work reporting, and output of information to BOSS and clients.

/ Large-scale real-world projects /

Addressing a variety of zero-based issues, the course will take you through systematic data visualization training from scratch to be able to analyze data in your work business and use charts to visually output charts. We have prepared some of the most common examples of analysis at work and the most common methodological techniques to go for real analysis and visualization.

1.

>>Tableaucase： Supermarket sales analysis

2.

>>Echartscase： Travel user behavior analysis

3.

>>Illustratorcase： Medical Data Visualization

4.

>> Project Practical I： Food safety testing

5.

>> Project Practical II： Education User Behavior Analysis

/ You will get /

This is by no means a course that just teaches you to use tools, it is a methodology for data visualization, a comprehensive training in information representation and data analysis. So in addition to the necessary tools to operate, the course will focus more on the logic of business analysis, data analysis and visualization in a real business implementation of ideas.

/ Course outline /

/ Suitable population /

1.Newcomers to the workplace who want to improve their overall aesthetic, design, and data analysis skills

2.Analysts, product, marketing, operations, etc. involved in data analysis work

3.People in the workplace who need to report frequently to their superiors and clients

4.Good lovers who want to tell stories and express themselves in the form of data visualization

5.Students who have written analytical proposals, reports and papers in their studies

/ Course Instructor /

Yifan Wu Full Stack Engineer

Data Visualization Engineer, University of Electronic Science and Technology Big Data Center

Worked as a front-end engineer at Tencent CDG

/ Reservation of places /

[Course Information

"Course Title

Data Visualization

"Course validity

6 months, extendable to 12 months by credit

"Class format

Recorded lessons that can be started immediately and watched repeatedly

"For people

Zero-basic white guy, negative-basic white guy

"Question and answer format

Study group teachers are always available to answer questions and give feedback on occasional issues

"Course Materials

Includes key notes, operational details, case references

"Course Program

Analysis and visualization of supermarket sales

Travel user behavior analysis and visualization

Food Safety Analysis and Visualization

Medical Data Visualization

Visualizing the behavior of educational users

New class debut with limited floor price

¥339 (original price ¥499), limited to 100 people

Long press the QR code below， Learn more& Reservation of places

For course inquiries and free trials, please join the group chat below

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