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Constructing Role Objects and Interpreting Role Conflicts Through the Lens of Stress

In my previous post, I discussed the relationship between role conflict and performance.  I suggested that all things being equal, role conflict might be the primary determinant of employee performance.  Companies direct all sorts of resources gathering data for recruitment purposes.  All things being about the same, much of that data collection is irrelevant. …

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Added by Don Philip Faithful on July 7, 2019 at 6:11am — No Comments

Co-integration and Structural Breaks Time Series Analysis using R on 100 year bond yields

Co-Integration in Time Series Analysis is when one data points is depended on other data points or follow the pattern. Example in capital markets Industry or sector leader company stock leads the direction and many small companies follows it. Example : Crude oil and Gasoline prices. Price of gasoline is dependent on Crude oil prices. Here Crude oil price always drives gasoline prices. 

To analyze similar co-integration used Moody's corporate AAA and BBB Bond Yields. Corporate…

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Added by Kali Prasad on July 6, 2019 at 10:37pm — No Comments

Scaling Innovation:  Whiteboards versus Maps

I love watching the NBA’s Golden State Warriors play basketball. Their offensive “improvisation” is a thing of beauty in their constant ball movement in order to find the “best” shot. They are a well-oiled machine optimizing split-second decisions in an ever-changing landscape that is heavily influenced by questions such as:

  • Who is my defender?
  • What are the strengths of my defender?
  • From where is help likely to come if I make a move to the basket?
  • Who…
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Added by Bill Schmarzo on July 5, 2019 at 12:51pm — No Comments

Deploying Python application using Docker and AWS

The use of Docker in conjunction with AWS can be highly effective when it comes to building a data pipeline.

Let me ask you if you have ever had this situation before. You are building a model in Python which you need to send over to a third-party, e.g. a client, colleague, etc. However, the person on the other end cannot run the code! Maybe they don't have the right libraries installed, or their system is not configured correctly.

Whatever the reason, Docker alleviates this…

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Added by Michael Grogan on July 5, 2019 at 8:30am — No Comments

Data Science Central Monday Digest, July 8

Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.  

Featured Resources and Technical…

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Added by Vincent Granville on July 5, 2019 at 7:00am — No Comments

Top 8 Data Science Use Cases in Construction

Introduction

With every article, we keep proving that data science has found broad application in numerous business areas. Now, the turn came to the construction industry as well. The world is overloaded with data. It results in a steady improvement in…

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Added by Igor Bobriakov on July 5, 2019 at 5:00am — No Comments

Top 10 Data Science Use Cases in Energy and Utilities

The energy sector is under constant development, and more of significant inventions and innovations are yet to come. The energy use has always been involved in other industries like agriculture, manufacturing, transportation, and many others. Thus these industries tend to enlarge…

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Added by Igor Bobriakov on July 4, 2019 at 10:00pm — 1 Comment

How Retailers Use Artificial Intelligence to Innovate Customer Experience and Enhance Operations

Digitalization influences how businesses operate and build and maintain relationships with customers. With the internet open 24/7, consumers can save time and shop online at their convenience. In 2017, global eCommerce sales accounted for 10.2 percent of all retail sales ($2.3 trillion US). This figure is projected to reach 17.5 percent in 2021. Revenue from eCommerce sales is expected to grow to $4.88 trillion US.…

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Added by Kateryna Lytvynova on July 4, 2019 at 6:00am — No Comments

How to build your own Neural Network from scratch in Python

This article was written by James Loy.

 

Update: When I wrote this article a year ago, I did not expect it to be thispopular. Since then, this article has been viewed more than 450,000 times, with more than 30,000 claps. It has also made it to the front page of Google, and it is among the first few search results for…

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Added by Andrea Manero-Bastin on July 4, 2019 at 4:30am — No Comments

Data Quality Case Studies: How We Saved Clients Real Money Thanks to Data Validation

Machine learning models grow more powerful every week, but the earliest models and the most recent state-of-the-art models share the exact same dependency: data quality. The maxim “garbage in – garbage out” coined decades ago, continues to apply today. Recent examples of data verification shortcomings abound, including JP Morgan/Chase’s 2013 fiasco and this lovely…

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Added by Michał Frącek on July 4, 2019 at 4:21am — No Comments

Lightweight but effective way of documenting a group of Jupyter Notebooks

My app Qubiter has a folder full of Jupyter notebooks (27 of them, in fact). Opening a notebook takes a short while, which is slightly annoying. I wanted to give Qubiter users the ability to peek inside all the notebooks at once, without having to open all of them. Qubiter’s new SUMMARY.ipynb notebook allows the user to do just that.

SUMMARY.ipynb scans the directory in which it lives to find all Jupyter notebooks (other than itself) in that directory. It then prints for every…

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Added by Robert R. Tucci on July 4, 2019 at 3:08am — No Comments

How the Mathematics of Fractals Can Help Predict Stock Markets Shifts

In financial markets, two of the most common trading strategies used by investors are the momentum and mean reversion strategies. If a stock exhibits momentum…

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Added by Marco Tavora on July 4, 2019 at 12:30am — 1 Comment

Data Science Central Thursday Digest, July 4

Here is our selection of featured articles and resources posted since Monday:

Technical resources 

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Added by Vincent Granville on July 3, 2019 at 7:30pm — No Comments

New Book: Data Science for Healthcare - Methodologies and Applications

This Springer book seeks to promote the exploitation of data science in healthcare systems. The focus is on advancing the automated analytical methods used to extract new knowledge from data for healthcare applications. To do so, the book draws on several interrelated disciplines, including machine learning, big data analytics, statistics, pattern recognition, computer vision, and Semantic Web technologies, and focuses on their direct application to…

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Added by Sergio Consoli on July 3, 2019 at 5:45am — No Comments

Multilevel Modelling of U.S. Home Loan Data

The housing market has undergone quite a change in the past decade, with more stringent lending criteria for housing having been enforced.

A key objective of financial institutions is to minimise the risk of mortgage lending by ensuring that the debtor is ultimately able to repay the loan.

In this example, multilevel modelling techniques are used to analyse data from the Federal Home Loan Bank…

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Added by Michael Grogan on July 3, 2019 at 3:01am — No Comments

Writing/Reading Large R dataframes/data.tables -- Addendum.



After posting my most recent blog using …

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Added by steve miller on July 2, 2019 at 9:00am — No Comments

Predicting Hotel Cancellations with Support Vector Machines and SARIMA

Hotel cancellations can cause issues for many businesses in the industry. Not only is there the lost revenue as a result of the customer cancelling, but this can also cause difficulty in coordinating bookings and adjusting revenue management practices.

Data analytics can help to overcome this issue, in terms of identifying the customers who are most likely to cancel – allowing a hotel chain to adjust its marketing strategy accordingly.

To investigate how machine learning can…

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Added by Michael Grogan on July 2, 2019 at 3:00am — No Comments

How To Choose An NLP Vendor For Your Organization

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Added by Shaily Baheti on July 2, 2019 at 12:30am — No Comments

How Long Does It Take to Learn Python for Data Science?

Python is the most loved, dreaded, and wanted programming languages by most developers, according to StackOverflow survey.  Popular among most professional software developers, Python was ranked the world’s seventh popular programming language.

A study by PYPL Popularity of Programming Language Index (a study that monitors the frequency of searches regarding the popular programming languages to learn) predicted that it showed that there was a growth of 17.1% during the last…

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Added by Yoey Thamas on July 2, 2019 at 12:29am — No Comments

Open-source Logistic Regression FPGA core for accelerated Machine Learning

Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized…

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Added by Chris Kachris on July 1, 2019 at 10:27pm — No Comments

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