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Data Science Central Thursday Digest, May 23

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

Technical Resources

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Added by Vincent Granville on May 23, 2019 at 10:30am — No Comments

Free Book: Foundations of Data Science (from Microsoft Research Lab)

By Avrim Blum, John Hopcroft, and Ravindran Kannan (2018). 

Computer science as an academic discipline began in the 1960s. Emphasis was on programming languages, compilers, operating systems, and the mathematical theory that supported these areas. Courses in theoretical computer science covered finite automata, regular expressions, context-free languages, and computability. In the 1970s, the study of algorithms was added as an important component of…

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Added by Capri Granville on May 23, 2019 at 9:00am — No Comments

Free Textbook: Probability Course, Harvard University (Based on R)

A free online version of the second edition of the book based on Stat 110, Introduction to Probability by Joe Blitzstein and Jessica Hwang, is now available here. Print copies are available via CRC Press, Amazon, and…

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Added by Capri Granville on May 23, 2019 at 8:30am — 1 Comment

Price Forecasting: Applying Machine Learning Approaches to Electricity, Flights, Hotels, Real Estate, and Stock Pricing

When you give customers advice that can help them save some money, they will pay you back with loyalty, which is priceless. Interesting fact: Fareboom users started spending twice as much time per session within a month of the release of an airfare price forecasting feature. This tool continues to grow conversion for our partner.

Besides travel, price predictions find their application in various scenarios. Commodity traders, investors, construction developers, or energy generators…

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Added by Kateryna Lytvynova on May 22, 2019 at 7:30am — No Comments

An Introduction to Python Virtual Environment

Data Science, Machine Learning, Deep Learning, and Artificial Intelligence are some of the most heard about buzzwords in the modern analytical eco-space. The exponential growth of technology in this regard has simplified our lives and made us more machine dependent. The astonishing hype surrounding such technologies has prompted professionals from various disciples to hop on to the ship and consider analytics as their career option.

To master Data Science or Artificial Intelligence in…

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Added by Divya Singh on May 21, 2019 at 9:30pm — No Comments

29 Statistical Concepts Explained in Simple English - Part 13

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, decision trees, ensembles, correlation, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, cross-validation, model fitting, and many more. To keep receiving these articles, sign up on…

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Added by Vincent Granville on May 21, 2019 at 5:30pm — No Comments

Implementing Knowledge Graphs in Enterprises - Some Tips and Trends

Tips

  1. Don't try to put the cart before the horse: realize that efficient data preparation (and thus interoperable standards) and data quality, especially in the enterprise environment, are a basic requirement for…
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Added by Andreas Blumauer on May 21, 2019 at 5:33am — No Comments

Prediction of Customer Churn with Machine Learning

Machine Learning is the word of the mouth for everyone involved in the analytics world. Gone are those days of the traditional manual approach of taking key business decisions. Machine Learning is the future and is here to stay.

However, the term Machine Learning is not a new one. It was there since the advent of computers but has grown tremendously in the last decade due to the massive amounts of data that’s getting generated, and the enormous computational power that modern-day…

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Added by Divya Singh on May 20, 2019 at 10:30pm — No Comments

Deep Learning Explainability: Hints from Physics


Nowadays, artificial intelligence is present in almost every part of our lives. Smartphones, social media feeds, recommendation engines, online ad networks, and navigation tools are some…

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Added by Marco Tavora on May 20, 2019 at 11:46am — No Comments

Should You Be Recommending Deep Learning Solutions in Your Company?

Summary:  If you are guiding your company’s digital journey, to what extent should you be advising them to adopt deep learning AI methods versus traditional and mature machine learning techniques.

 

By now everyone is at least familiar with using AI/ML as a required cornerstone of company strategy.  Frequently…

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Added by William Vorhies on May 20, 2019 at 8:33am — 1 Comment

A Complete Machine Learning Project Walk-Through in Python: Part One

This article was written by Will Koehrsen.

 Reading through a data science book or taking a course, it can feel like you have the individual pieces, but don’t quite know how to put them together. Taking the next step and solving a complete machine learning problem can be daunting, but preserving and completing a first…

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Added by Andrea Manero-Bastin on May 20, 2019 at 6:30am — No Comments

Data Science Central Monday Digest, May 20

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.  

Announcements

  • Machine…
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Added by Vincent Granville on May 19, 2019 at 3:00pm — No Comments

Quantum Simulator Qubiter now has a native TensorFlow backend



I am pleased to announce that my quantum simulator Qubiter (available at GitHub, BSD license) now has a native TensorFlow Backend-Simulator (see its class `SEO_simulator_tf`, the `tf` stands for TensorFlow). This complements Qubiter's original numpy simulator (contained in its class `SEO_simulator`). A small step for Mankind, a giant leap for me! Hip Hip Hurray!

This means that Qubiter can now…

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Added by Robert R. Tucci on May 19, 2019 at 11:30am — No Comments

A Telecom Data science project from data gathering to model selection

One of our business units wants to target the competitors’ customers with personalized product/offer. To do that, Business needs to understand who are the prepaid/postpaid customer of the competitor to push the relevant and personalized product/offer and they don’t have this data. Now, this is a binary classification problem and we want to apply machine learning machine method to predict the likeliness of competitor customer to be prepaid or postpaid.

Steps in Data Science…

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Added by Ariful Islam on May 19, 2019 at 9:00am — No Comments

Unsupervised learning and its role in the knowledge discovery process

Unlike supervised learning, unsupervised learning not working with labeled data, it is not showing the machine the correct answer. Instead, it is using different algorithms to let the machine create connections by studying and observing the data. Learn much of this through study and observation. Learning and improving by trial and error is the key to unsupervised learning.

 

However, the Knowledge Discovery process is the field of data mining is concerned with the development…

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Added by Ariful Islam on May 19, 2019 at 8:42am — No Comments

Utilizing Artificial Intelligence Potential in Financial Services & Insurance Sectors

Gone are the days when insurance and nbfc sector was relying completely on manual processes. Today with the emergence of AI and data-driven business decision making coupled with the application of IoT technologies there is a radical transformation of business processes in insurance and other financial services, like straight through processing becoming mainstream. 

Let us look at different use cases:

1. Insurance Models:

Consider the recent proliferation…

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Added by Mahesh Kumar CV on May 19, 2019 at 7:11am — No Comments

A Beautiful Result in Probability Theory

This is another spectacular property of the exponential distribution, and also the first time an explicit formula is obtained for the variance of the range, besides the uniform distribution. It has important consequences, and the result is also useful in applications.

Theorem

The range R(n) associated with n independent random variables with an exponential distribution of parameter l…

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Added by Vincent Granville on May 19, 2019 at 6:30am — 1 Comment

Telecom Customer Analytics

I was deputed to work at Lagos, Nigeria in 2011 to work for a telecom giant there. The project in hand was to develop customer analytics modules using SAS on customer's newly built Oracle data warehouse. We thought about developing following modules.

  1. Customer Churn Analysis
  2. Calculating Product Propensities
  3. Customer Lifetime Value Calculation
  4. Customer Segmentation

Customer Churn…

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Added by Dr. Moloy De on May 17, 2019 at 4:31pm — No Comments

Confidence Intervals in One Picture

Confidence intervals (CIs) tell you how much uncertainty a statistic has. The intervals are connected to confidence levels and the two terms are easily confused, especially if you're new to statistics. Confidence Intervals in One Picture is an intro to CIs, and explains how each part interacts with margins of error and where the different components come…

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Added by Stephanie Glen on May 17, 2019 at 10:00am — No Comments

How to make ML engineers 5x more efficient

Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.

Salaries and…

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Added by Chris Kachris on May 17, 2019 at 4:50am — No Comments

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