Tableau Saturday XI, 27th April, 2024
Our sole mission is to help people see and understand data.
— Tableau©️ 2003
Hi Everyone, I would like to thank those who made it to the Tableau©️ Saturday Colab Kaduna in-person event, especially our host Sanusi Ismaila and tutor Peter Okwukogwu aka Pablo.
Peter took us on a journey through, Tableau public, Geopandas, maps, density maps, line graphs, overlapping line graphs, multiple rows graphs, show me, sheets, measures and dimensions, and bar charts.
On Tableau, you can also represent data as discrete or continuous. We looked at sheets, Measures and Dimensions.
We would be looking at Data connection in Tableau.
We talked about Abstractions. We need to understand abstractions. As an individual who wants to be more versed in Tableau you need to have general knowledge.
Abstraction is how you take an idea and break it down to a tech interface,
One of the greatest abstractions is transportation, we understand it better, and we look at how to make it work.
When you don’t understand how something works you call it “Black-Magic”, aka Juju.
Also because Python is object-oriented, it works with object-based rules, python works well in robotics too.
JavaScript is oriented towards interactions, it is a verbose language with a compiler.
All browsers work with Compiler, C++ works well for gaming, but Python can’t do that. Our phones are abstractions, and because they have been stretched, we now have new abstractions such as the Rabbit R1.
There is a reason why machine-human interaction works.
It reduces the drudgery on the part of human beings.
For Data Visualizations, we want to connect visually. It is important to know what is happening behind the scenes in data visualization. With numerical analysis one of these is certain. Python is slow, the whole idea of making it fast, the idea of using numbers makes it fast. Tableau handles data fast.
Note:
It’s not every abstraction you must understand.
The foundation for VizQl is SQL ( Standard Query Language or Structured Query language ) SQL was created in 1970, and data scientists came together and said how about we collect data and connect them. We create a connection that done with a relationship, they will all form a relationship database management system (RDBMS). This brought about an entity relationship diagram. We talked about tools with reference to Jake Vanderplas and Robert John’s books on data.
VizQL is a visual query language for databases.
Simply put, it is the proprietary query language behind the Tableau product suite’s visual rendering. It is essentially a translator of SQL queries that traditionally return numbers and tables into an effortless visual analysis. As a user drags and drops data around the Tableau interface, each action generates a query that is subsequently translated into the VizQL language and renders as Tableau visuals.
Because VizQL is a language-based approach, it helps to facilitate 3 important goals that underlie Tableau’s design principles: Expressiveness, Incremental Construction, and Alternatives
Tableau has a proprietary language that is called VizQl ( the opposite of proprietary is open source)
Data Analysis is putting what’s in the human mind out there, it’s a human need for visualization. When we get to the business scenario, we use the Bayesian analysis, an example is divorce in marriages, Car accidents on the road are a possibility which is why there is insurance for vehicles.
Bayesian statistics is an approach to data analysis and parameter estimation based on Bayes’ theorem. One thing. Unique for Bayesian statistics is that all observed and unobserved parameters in a statistical model are given a joint probability distribution, termed the prior and data distributions.
What is the difference between Bayesian and regular statistics?
The Bayesian approach can calculate the probability that a particular hypothesis is true, whereas the frequentist approach calculates the probability of obtaining another data set at least as extreme as the one collected (giving the P value).
Our tutor took us through ChatGpt prompting,where he showed us his questions to know more about Pre-comits in Bokeh to help prevent mistakes in commit
Time series data automatically on a data set is a line graph, Tableau helps you by default with the best viz to make you understand the data.
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If you need to see data that has a date you use the line chart, line chart has a way to break it down for you. Under Marks on Tableau, you can pick other viz options available to your data.
During the workshop, questions were asked on connecting data. We discussed the various types of data available to Tableau.
We used an Excel data set to describe the process, the structure of the imported data and the steps to take to get visuals from that data.
We rounded up the event with clarity on the benefits of visualization to businesses.
I am looking forward to the next workshop on Tableau, we will be looking at more aspects of connecting data and the Tableau paradigm.
Do comment below on how your journey into Tableau has been.
Bayesian statistics
https://www.wikiwand.com/en/Bayesian_statistics
https://www.quantstart.com/articles/Bayesian-Statistics-A-Beginners-Guide/
https://statswithr.github.io/book/the-basics-of-bayesian-statistics.html
https://www.analyticsvidhya.com/blog/2016/06/bayesian-statistics-beginners-simple-english/
Multidimensional Expressions
https://galaktika-soft.com/blog/multidimensional-expressions.html
VizQL
https://www.tableau.com/drive/what-is-vizql
https://medium.com/@stanya087/what-is-vizql-in-tableau-a71dffb37a85
https://www.researchgate.net/publication/221213647_VizQL_a_language_for_query_analysis_and_visualization
https://www.youtube.com/watch?v=rgj2zyGRz7s
Jake Vanderplas
http://vanderplas.com/
https://github.com/jakevdp
https://www.linkedin.com/in/jake-vanderplas-5894937b/
https://www.oreilly.com/people/jake-vanderplas/
https://www.amazon.com/Python-Data-Science-Handbook-Essential/dp/1491912057
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