Tag: Analytics

Writing your First JavaScript Vue.js App for Petro.ai

Getting Petro.ai installed can be an exciting time an open quite a few doors for development, especially when it comes to JavaScript apps. Custom applications become a cinch using the API. In the coming weeks I’ll be putting together some simple applications that you can make on top of the Petro.ai platform. We’ll be using an assortment of languages to communicate with the Petro.ai API so feel free to ask for an example.

Here is the HTML


<script src="https://unpkg.com/vue/"></script>
<h1>Hello, Wells!</h1>
<div id="hello-wells" class="demo">
   <blog-post 
      v-for="well in wells" 
      v-bind:key="well.id" 
      v-bind:title="well.name">
   </blog-post>
</div>

And the JavaScript (Vue.js)

Vue.component('blog-post', {
  props: ['title'],
  template: '<p>{{ title }}</p>'
})

new Vue({
  el: '#hello-wells',
  data: {
    wells: []
  },
  created: function () {
    var vm = this
    // Fetch our array of documents from the Petro.ai wells collection
    fetch('http://<your-petro-ai-server>/api/Wells?Limit=10')
      .then(function (response) {
        return response.json()
      })
      .then(function (data) {
        vm.wells = data['data']
      })
  }
})

And poof! We’ve called the first 10 wells from the Petro.ai wells collection:

Hello, Wells!

DEJOUR WOODRUSH B-B100-E/094-H-01
BLACK SWAN HZ NIG CREEK B-A007-G/094-H-04
BLACK SWAN HZ NIG CREEK B- 007-G/094-H-04
BLACK SWAN HZ NIG CREEK B-G007-G/094-H-04
BLACK SWAN HZ NIG CREEK B-E007-G/094-H-04
BLACK SWAN HZ NIG CREEK B-D007-G/094-H-04
BLACK SWAN HZ NIG CREEK B-C007-G/094-H-04
ZEAL 4-25-46-26
PEYTO WHHORSE 4-9-49-15
BLACK SWAN HZ NIG CREEK A- 096-C/094-H-04

What’s going on here is that the app is pulling directly from the Petro.ai server asynchronously. In the coming weeks, I’ll show how we can create reactive JavaScript applications that will update from the Petro.ai server so that we can watch things like rigdata or real-time production data. This data was provided by GeoLogic and we’ll be setting up a public Petro.ai instance for everyone to develop against.

Technical Director at Ruths.ai

CRISP-DM Evaluation: Train and Test Set

Last week in our Analytics Journey, we worked on variable selection in the Modeling stage of the CRISP-DM method.  Having built a model, it’s once again time to see how it did with the Evaluation stage.  One of the most important parts of evaluating a model comes in properly constructing a training and testing set for evaluation.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP-DM Modeling: Forward and Backward Selection

Welcome back everyone to our Analytics Journey series.  Those of us in Houston have been through a trying time, and our thoughts are with the community.  We will try to return to a semblance of normalcy by continuing where we left off in our journey.

With all of our hard work in understanding and preparing the data during previous steps of the CRISP-DM method–exploring data, choosing a model space, removing NULLs, removing Multicollinearity–it’s time to have some fun with the Modeling stage.  Today, we’ll look at an aspect of Multiple Linear Regression:  Forward and Backward Selection.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP DM Data Preparation: Finding and Counting NULL Values in Spotfire

Hello, good friends.  The next step in our Analytics Journey takes us to the second iteration of Data Preparation.  This is the third step of the CRISP-DM method.

Today, we are going to look at one of the most common data quality issues in Spotfire:  the NULL, aka missing values.  While there are many ways to address NULL values like imputation (a lesson for another day), the first step is simply identifying them. We will walk through Spotfire’s built in NULL identifier and also a more advanced TERR based method.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP-DM Data Understanding: Marking and Filtering

For the first Data Understanding stage installment in our Analytics Journey, we explored Simpson’s Paradox in the survival statistics from the Titanic to highlight why the Data Understanding stage proves so important in the CRISP-DM process.  This week, we will use the same dataset and demonstrate how Spotfire’s unique Marking and Filtering capabilities make the Data Understanding stage much more efficient and powerful.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

Linear Regression, the simplest Machine Learning Model

Linear Regression models are the simplest linear models available in statistical literature. While the assumptions of linearity and normality seem to restrict the practical use of this model, it is surprisingly successful at capturing basic relationships and predicting in most scenarios. The idea behind the model is to fit a line that mimics the relationship between target variables and a combination of predictors (called independent variables). Multiple regression refers to only one target variable and multiple predictors. These models are popular not only for solving the prediction task but also for working as a model selection tools allowing to find the most important predictors and eliminate redundant variables from the analysis.

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CRISP-DM Business Understanding: KPI Charts

As Rustin Cohle said in True Detective, “Time is a flat circle,” so welcome back to the beginning of our Analytics Journey!  Previously, we cycled through the CRISP-DM process from beginning to end, explaining the stages as well as the way we approach our Data Science life cycle at Ruths.ai.  We have strived to demonstrate the importance of melding the human element with quantitative rigor.  Now, we will re-iterate through the steps as all good analytics processes will do, looking for ways to strengthen our model.  This time through, we will move from the theoretical to practical with an eye towards enacting the stages in the real world.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

Calculating Call and Put Option Price Using Spotfire

We often use Excel templates to calculate the Call and Put option prices with required parameters and the relationship between the parameters are expressed in Excel Functions. One tricky problem with the Excel Function is that if you change the cell position, accidentally click on a wrong cell, or if you want to export the results and utilize the templates again, the embedded functions may change accordingly and generate wrong answers.

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Master of Statistics candidate of Rice University with undergraduate degree in Financial Statistics and Risk Management.
Strong background in R, SAS, SQL, finance, economics.
Career interests in: Data Scientist, Consulting, Investment Banking.
Energetic, enthusiastic, quick learner.

CRISP DM: Deployment

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business UnderstandingData UnderstandingData Preparation, Modeling, and Evaluation stages.  Next, we examine the final stage:  Deployment.

The.  Final.  Stage.  Now, we just have to turn this thing on and reap the rewards, right?

      

Unfortunately, Deployment does not just happen with the push of a George Jetson button.

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Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.

CRISP-DM: Evaluation

Welcome to the next installment of our Analytics Journey, which explores how we at Ruths.ai apply the CRISP-DM method to our Data Science process. Previously, we looked at an overview of the methodology as a whole as well as the Business UnderstandingData Understanding, Data Preparation, and Modeling stages.  Next, we examine the Evaluation stage.

Read More

Jason is a Junior Data Scientist at Ruths.ai with a Master’s degree in Predictive Analytics and Data Science from Northwestern University. He has experience with a multitude of machine learning techniques such as Random Forest, Neural Nets, and Hidden Markov Models. With a previous Master’s in Creative Writing, Jason is a fervent believer in the Oxford comma.