Introduction to Data Visualization

                                 Introduction To Data Visualization          

                                       

                                           


In this world where raw data comes in huge chunks,it is difficult for us humans to quickly grasp necessary data (or information)...yeah you got me...Information ,cause  information is raw data which is processed,interpreted,handled and structured to give some meaning.We as humans love to see graphical visualizations rather than 2-D data which provides clear insight as what needs to be done and what not.

Size of Data

Based  on a graphic from 2015 by Ben Walker,2.5 quintillion bytes of data is created everyday which would fill 10 million Blu ray disks which surprisingly when stacked on top of each other would be of the height of 4 Eiffel Towers.Now that is some shit ton of data and that too it was 4 years ago.(Don't even think about asking how much it would be now or in the coming years!!)

Classifications Of Learning

• Supervised Learning: Deals with data assigned with class name.

• Unsupervised Learning:As the name suggests the data has no class assigned to it.

       Now as you can see that we can easily understand what supervised learning wants to tell us but we need to scratch our heads when it comes to unsupervised.But the thing is they are advantageous in their own ways.

Supervised Learning                                                  Unsupervised Learning

1. Class values are defined                                        Class values are not defined
2. User can easily understand the metrics.                Difficult for user to understand metrics
3. Useful in direct visualization.                          Needs to be converted to supervised to visualize

                             

The above visualization is the proof that the peeps are getting used to Business Analytics and accessing info is more compared to the people who do not use visualizations.

Advantages of Data Visualization

• Graphics are always attractive to the human eye.
• There is no need of excessive coding.Most of the tools just use drag and drop mechanism.
• Basically any person can perform it if they have knowledge of what data to work on.
• Even 3-D viewing can be done at ease rather than going about your business on 2-D data.

A major disadvantage of this is that people usually tend to represent variation in design rather than in data.This allows me to bring forward a quote of Mr. Edward Tufte

"Show data variation not design variation."

Now what does this mean?Given below are various graphs in relation with Antibiotics vs All Bacteria 

In the above graph,understanding and coming to a conclusion needs scratching of one's head cause there is just design variations everywhere!!

Now....

This is a simplified and clear version of how data can be visualized by varying data rather than design.

Hence Data Visualization does complement better understanding of data.

Lastly.....

Best Visualization Tools Available

               (Just click on the below links to continue)

1. QlikView                                             11. Kibana

2. Tableau                                                12. Plotly

3. Zoho Analytics                                      13. ggplot2

4. Sisense                                                 14. Chartio

5. IBM Watson Analytics                             15. Infogram

6. Domo                                                    16. Highcharts

7. Microsoft Power BI                                 17. Visme

8. MATLAB                                                 18. Geckoboard

9. SAP Analytics Cloud                               19. D3.js

10. Klipfolio                                               20. Alteryx