1. IKIGAI is a Japanese concept for a reason for being, or in French, une raison d'être. Also IKIGAI provides a framework for analyzing the relationships between 4 traits of life:

    1.What do you love
    2.What are you good at
    3.What can you be paid for
    4.What does the world need

    If you are doing what you love (1) and what you are good at (2), you are living your passion.

    If you are doing (1), (2) and (3), then you are combining your passion and profession. You are satisfied but you might feel a sense of uselessness.

    If your life satisfies all four, then you have reached the state of IKIGAI, that is the ultimate reason of being.

    IKIGAI has a number of visualizations done before. I for one would like to see its visualization being done in Tableau. Voila, here you go.

    Note that the analysis of IKIGAI inspires me. It helps me understand myself and my life a little better. It's the kind of vizzes I love to create.

    This is probably the last viz of the year for me. Wish everyone a great and happy new year!
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  2. After Poisson distribution and Triangular distribution calculators, jamais deux sans trois, I decided to give Normal distribution a run. Certainly it is the most important distribution of all.

    So here you go. Click image to go to the interactive version.

    Note there two ways to calculate, given the same mean and stdev:
    1.enter the parameter in the box X.
    2.mouse over X.

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  3. It seems there is no such a calculator for TriAngular Distribution on the web. So I created one.

    There are 4 parameters:
    Min,
    Mode,
    Max
    and Bin Size.
    Change the parameters according to your need.
    Click image to view the interactive version.
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  4. Poisson distribution is so important that I had to learn it in a recent project. Learnt to use it to be exact.

    Thus came an idea to visualize it in Tableau, because I couldn't find a good visual reference on the web about it.

    Here you go. It shows two charts:
    1.Probability distribution per K (the number of occurrences).
    2.Cumulative probability for all events where the number of occurrences <=K.
    3.Both λ and the range of K are in parameters.
    Click image to view the interactive version.

    You might notice that we need to calculate the factorials of K in Poisson distribution. The initial calculation formula failed because the numbers were too big for integer operations. A workaround is found using floating point calculations.

    [Update]Added an alternative view that highlights the complementary property of <=K and >K probabilities:
    Click image to view the interactive version.
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