Three graphs I find extraordinarily useful because they are very dense and teach many useful terms in a very clear way all at once.

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A wip towards organizing and mapping out the toolset needed for my career and various projects. The focus this year has been filling in gaps around dataviz and significantly augmenting my data modeling skills. Some work left there around network and dynamic systems. Decision modeling is a new concept to me and needs maturity.

**Overview.**

- Gathering.
- Describing.
- Modeling.
- Deciding.
- Visualizing.

**Gathering.**

I put this map together of all the decisions to be made when setting up a shot:

It includes setting the scene, choosing the optics, proper exposure, shutter release and post-processing.

I have several new goals in my professional life:

- increase my productivity substantially
- communicate my points powerfully and clearly
- increase the quality of my work through insight

The only way to do this is to a) significantly increase the amount of calculations and b) automate detection and presentation of that data. To do this I need data science tools that are flexible, scalable and fast to use.

So I ran a comparison of everything that is relevant to my work and these are the flows I came up with:

What is the best method for tightly controlling accuracy in your design?

*Step 1. Determine the scope of the problem*

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Reliability, Availability and Serviceability (RAS) are related to a products ability to deliver its intended functionality. Essentially how long and how well.

**Definitions***Reliability: * is the ability (i.e. probability) of a device to provide correct outputs, given a specific operating environment.*Availability: * is the likelihood (i.e. probability) of a device to be available for usage at a given time.*Serviceability: * is the efficacy of a device to be repaired.

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*Step 1*: Establish goals, attach figures to critical requirements then brainstorm ideas. Sort fixable vs unfixable.*Step 2*: Establish detailed requirements and investigate remaining solutions. Get familiar with each technology/solution. Brainstorm implementations. Choose the best solution.*Step 3*: Dive deep, test, iterate and establish complete documentation.

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I love graphs, plots and charts. There is something almost magical about their ability to ask a question, analyze it and make a point all in one place.

I think that's why I love to visualize data. I love how powerful the perfect graph can be by allowing you to quickly and deeply grasp a concept (especially for large data sets) by telling a story and using the eye to draw your attention to the right places. Our minds are great at seeing trends and patterns in data when it is visualized.

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**Preface**

This series is aimed at providing tools for an electrical engineer to analyze data and solve problems in design. The focus is on applying calculus to equations or physical systems.

**Introduction**

This article will cover differential equations using Laplace Transforms.

There is no reference book for this entry.

This also assumes you are familiar with Python or can stumble your way through it.

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**Preface**

This series is aimed at providing tools for an electrical engineer to analyze data and solve problems in design. The focus is on applying calculus to equations or physical systems.

**Introduction**

This article will cover integrals.

There are many calculus references, the one I like to use is Calculus by Larson, Edwards and Hostetler.

This also assumes you are familiar with Python or can stumble your way through it.

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