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The Ultimate Data Visualization Guide For Beginners

 




Hello and welcome to the data science blog site. today I am going to talk about some other sides of the data science field which you might have been aware of or not, that is nothing but 'the arts'. yeah! you heard it right artistic skills are really important to present your data science solutions that you have figured out from data modeling or crunching your data from various sources etc. if you can't present and tell your story to your audience then your solution has no meaning at all. you have to sell your story effectively and visually compellingly way, and that's where data visualization comes into the picture.


Here's what we are going to cover:


1) Ideas on visualizations

2) Storytelling

3) Visual display of data




1) Ideas on visualizations:


What is data visualizations:  data visualization is nothing but visualizing structured, raw, and numerical data in various forms of charts and graphs to let your audience understand data. that's no big deal, a simple yet conscious way to present data stories.

Your audience: the audience is everything, what you are trying to say, to whom you are saying, who they are, how easily you can communicate your ideas in simple and visually compelling aesthetics.

Understanding your data: one of the most important parts is that understanding your data before any data science task. for what this data is all about, what they are talking about. is it statistical data? does it contain outliers, is it a categorical dataset? what are the categories and subcategories in the dataset?

Explotory visualization:  essentially it is a combination of 'exploratory' and 'explanatory' data visualization. it is a common task in data science practice to explore your data to know its features and attributes etc.



2) Storytelling: 


In the end, it is all about storytelling, how your audience will come across your data, what story you are trying to tell and how you share your findings from your data.

there are some points one should keep in mind before crafting your data story:

1) The 6 Ws: whenever you creating visualizations ask yourself 6W questions like 1) What 2) who 3) where 4) how 5) why 6) when by asking these questions about your audience you will be able to craft a story more relevant to your audience.

2) Defining your narrative: what is the flow of information or knowledge you are telling to your audience? what are the key objectives of your story, what is that one information you are willing to empathize on?
these are the basics questions you can to yourself when you are crafting your story.

3) make things relatable: you want to make things relatable in a way your audience can easily understand your point. you can compare your data size to some landmark that your audience already familiar with.



3) Visual display of data
 
This section is the final stage of your data story, where you take your curated, ready-to-present data into some visuals that you think your audience understands.

there are some key points you should keep in your mind while you are creating visuals like, the typography of your text, position, size, color, shape, and contrast in your visualization in a way that your audience can catch easily and understand your story effectively.

1)  Basic charts: the basic charts are like bar charts, pie charts, scatter plots, line charts, area charts,  bubble charts, and timelines.

2) Alternative charts: box plot, heatmap, radar/spider chart,  parralo-coordinates, treemaps for hierarchical data, maps, tree diagrams, fisheye distortions, etc.

there are thousands of ways we can visualise data and present it, it is more of a creativity and art as we have been discussing since the beginings. 


I hope you find this blog insightful and informative. there are a number of resources available, which you can refer to on the internet. like Bill Shander's courses on Linkedin etc.

if you want to connect with me. here's my email address: avikumar.talaviya@gmail.com




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