What is really data about?

For example, you are working with a dataset from your company, and you discovered a pattern nobody saw.

How to present it to your team?Data StorytellingAs a combination of data, narrative, and visuals, Data Storytelling is one of the new, hot skill every Data Scientist will need in the near future.

Data Storytelling is a fundamental technique for a Data Scientist or Data Analyst who needs to explain his or her line of thinking to others, whether they have the technical knowledge or not.

It is at that moment that your communication skills will be very important.

After all, no one likes to see a lame presentation or read an annoying text, right?Briefly, Data Storytelling is the act of explaining what you did, how you did it, and why you did it all in a way that keeps your reader or listener engaged.

People hear statistics, but they feel stories.

(Forbes)The goal of Data Storytelling is to tell the story of your data.

It is making sense of the data humanly.

Personifying the data as a living scene and let others understand the data with empathy, not only statistics.

It may involve a combination of three key elements: data, visuals, and narrative.

When the narrative is coupled with data, it helps to explain to your audience what’s happening in the data and why a particular insight is important.

Ample context and commentary are often needed to appreciate an insightfully.

When visuals are applied to data, they can enlighten the audience to insights that they wouldn’t see without charts or graphs.

Many interesting patterns and outliers in the data would remain hidden in the rows and columns of data tables without the help of data visualizations.

Source: ForbesFinally, when narrative and visuals are merged, they can engage or even entertain an audience.

It’s no surprise we collectively spend billions of dollars each year at the movies to immerse ourselves in different lives, worlds, and adventures.

When you combine the right visuals and narrative with the right data, you have a data story that can influence and drive change.

Mostly you must use the narrative, visuals, and data to make the complex simple.

Using it to make everybody understand an obscure insight and think, “how I have not saw this before?”.

Tips to be better at Data StorytellingThere are already some articles and stories about Data storytelling tips.

Some of those are below:10 Ways to Take Your Boss on a Journey Through the Data“People hear statistics, but they feel stories.

” Forbes, March 2016towardsdatascience.

comMastering Data Storytelling: 5 Steps to Creating Persuasive Charts and GraphsData storytelling is one of those "buzzwords" that in actuality is not really a buzzword-it's reflective of a necessary…www.


com10 Tips for Better Data StorytellingBy Bill Shander.


com7 Data Storytelling Tips From Centuries-Old Data VisualizationAlthough the infographic Renaissance has triggered its resurgence, data visualization is nothing new.

For hundreds of…www.


comI would add one last tip here.

I believe it is the most important one.

Read, a lot.

Reading is essential to understand the function and history of our society.

It is vital to develop our mind by discovering new things.

Also, reading is a crucial aspect when we are talking about imagination and curiosity.

The more you read, the more you understand the subject you read.

Now linking it back to Data Science, data is about everything.

A reader can learn concepts faster about anything he reads.

This can make a huge difference giving sense to data, giving the needed background to the numbers so that numbers can become ideas and insights.

The more that you read, the more things you will know.

The more you learn the more places you will go.


 SuessReferences[1] Fayyad, Usama, Gregory Piatetsky-Shapiro, and Padhraic Smyth.

“From data mining to knowledge discovery in databases.

” AI magazine 17, no.

3 (1996): 37–37.

[2] Cleveland, William S.

“Data science: an action plan for expanding the technical areas of the field of statistics.

” International statistical review 69, no.

1 (2001): 21–26.

Data Scientists Are ThinkersExecution vs.

exploration and what it means for youtowardsdatascience.


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