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June 2016 Blog Posts (123)

Machine Learning: An Analytical Invitation to Actuaries

This post highlights the various value-additions that machine learning can provide to actuaries in their analytical work for insurance companies. As such, a key problem of swapping specific risk for systematic risk in general insurance ratemaking is highlighted along with key solutions and applications of machine learning algorithms to various insurance analytical problems.

‘In pricing, are we swapping specific risk for systematic risk?’[1]

The…

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Added by Syed Danish Ali on June 20, 2016 at 1:36am — No Comments

Guide to Deep Learning

Up till recent past, the artificial intelligence portion of data science was looked upon cautiously due to its history of booms and flops.[1] In the latest stream of events, major improvements have taken place in this field and now deep learning, the new leading front for Artificial Intelligence, presents promising prospect for overcoming problems of big data. Deep learning is a method of machine learning that undertakes calculations in a layered fashion starting from high…

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Added by Syed Danish Ali on June 20, 2016 at 1:15am — No Comments

Key tools of Big Data for Transformation: Review & Case Study

Review

The challenges of big data can be captured succinctly as follows[1],[2]:

  • Volume; ever increasing volume which breaks down traditional data-holding capacity
  • Variety; more and more heterogeneous data from many formats and types are bombarding the data environment
  • Velocity; more and more data is time sensitive now; frequent updates are taking place instead of relying on historical old data and…
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Added by Syed Danish Ali on June 20, 2016 at 1:03am — 1 Comment

Intro to Bigdata

Definition:

Big data usually includes data sets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big data "size" is a constantly moving target, as of 2012 ranging from a few dozen…

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Added by m.kiransingh on June 20, 2016 at 1:00am — No Comments

Intro to Bigdata Architecture

Architecture:

In 2000, Seisint Inc. (now LexisNexis Group) developed a C++-based distributed file-sharing framework for data storage and query. The system stores and distributes structured, semi-structured, and unstructured data across multiple servers. Users can build…

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Added by m.kiransingh on June 20, 2016 at 1:00am — No Comments

Complexity Science: A Case Study

Underwriting Cycles[1]

In this case study for Complexity Science, we aim to show its value addition through agent based modeling of an important insurance problem, i. e, underwriting cycle. Underwriting in Property-Casualty/General Insurance is where insurance underwriting follows and mimics the Economy and its cycles of boom and busts. These soft and hard market swings are a key source of challenge and volatility to insurers. The main value addition of…

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Added by Syed Danish Ali on June 20, 2016 at 12:41am — 2 Comments

Sweet and Short Introduction to Complexity Science

Complexity Science

It is quite difficult at first to precisely define ‘Complexity Science’. It is a new perspective of methodology and modeling approaches that are based more on reality than assumptions. Quite simply put, Complexity Science is a new way to grasp and manage reality. It does not study systems in isolation like gambling dice or planetary motion only. It studies the complex, holistic, inter-connected reality in which we actually live such as financial stock…

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Added by Syed Danish Ali on June 20, 2016 at 12:31am — 5 Comments

A Different Breed of Mathematics: Topology

Topology is the mathematical study of the properties that are preserved through deformations, twistings, and stretchings of objects. Tearing, however, is not allowed[1]. Topology can be used to abstract the inherent connectivity of objects while ignoring their detailed form. Put simply, Topology is a mathematical discipline that studies shape and assumes that shape has meaning[2]. This post discusses topology’s applications in finance and insurance. While…

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Added by Syed Danish Ali on June 20, 2016 at 12:30am — 3 Comments

Handling missing data with MICE package

This is a quick, short and concise tutorial on how to impute missing data. Previously, we have published an extensive tutorial on imputing missing values with MICE package. Current tutorial aim to be simple and user friendly for those who just starting using R.

Preparing the dataset



I have created a simulated dataset, which you can load on your R environment by using the…

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Added by Klodian on June 19, 2016 at 12:30pm — 3 Comments

Weekly Digest, June 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. 

Announcements

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Added by Vincent Granville on June 18, 2016 at 9:30am — No Comments

End-of-Life Narrative

Canada will soon be passing legislation to allow for physician-assisted suicide.  Sometime over the course of debate between our Parliament and Senate, I found what at the time seemed like a peculiar pattern of woodchips in the back of my pick-up.  It was such an interesting image, I thought I would share it here.  On closer inspection, I discovered that in fact I was looking at hundreds of tiny flower shafts and seeds.…

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Added by Don Philip Faithful on June 18, 2016 at 8:30am — No Comments

Deep Learning Libraries by Language

Source for picture: click here

Python

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Added by Emmanuelle Rieuf on June 17, 2016 at 3:00pm — 3 Comments

Easier data analysis in Python with pandas (video series)

This blog about series of videos was created by Kevin Markham. Kevin is a data science educator and the founder of Data School. He specializes in Python and machine learning. He has hundreds of hours of experience as a data science classroom instructor, and thousands of hours of experience developing high-quality data science educational materials.…

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Added by Emmanuelle Rieuf on June 17, 2016 at 2:30pm — No Comments

New Machine Learning Cheat Sheet by Emily Barry

This blog about machine learning was written by Emily Barry. Emily is a Data Scientist in San Francisco, California. She really loves emoji. Another thing she loves is data science. The more she learns about machine learning algorithms, the more challenging it is to keep these subjects organized in her brain to recall at a later time. So, she decided to marry these two loves in as productive a fashion…

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Added by Emmanuelle Rieuf on June 17, 2016 at 11:30am — 1 Comment

Do you know that these phrases are being flagged in Goldman Sachs?

Financial institutions are under tremendous pressure to comply with regulatory and securities laws. Traditional methods of compliance relied on hand-coded rules that provided detailed control, but introduced human bias and yielded false negative results. Ensuring compliance is complicated by diverse channels such…

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Added by Amy Krishnamohan on June 17, 2016 at 8:50am — No Comments

How big data is helping to shape political campaigns

2014 was a watershed moment in Indian politics for a variety of reasons. The foremost one was the stupendous victory of the Narendra Modi led NDA in the 2014 Lok Sabha elections. Modi managed to win 282 seats for his party in the elections, the first time in 30 years that a political party had come to power with absolute majority.

Several reasons have been attributed to this victory. Political analysts have spent hours in TV newsroom debates discussing the reasons for this victory.…

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Added by Tanmay Bhandari on June 16, 2016 at 9:00pm — No Comments

Visualizing Hedge Fund Industry Performance in 2015

Contributed by Zi Jin. She is currently in the NYC Data Science Academy 12 week full time Data Science Bootcamp program taking place between April 11th to July 1st, 2016. This post is based on her first class project -  (due on the 2th week of the program).

Introduction

Hedge fund is an important…

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Added by NYC Data Science Academy on June 16, 2016 at 11:30am — No Comments

2015 Health Insurance Marketplace Data Analysis

Contributed by Ruonan Ding. She is currently in the NYC Data Science Academy …

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Added by NYC Data Science Academy on June 16, 2016 at 11:09am — No Comments

Book: Easy R Programming for Beginners

Your Step-By-Step Guide To Learning R Programming.

Do you want to learn R Programming?

Do you get overwhelmed by complicated lingo and want a guide that is easy to follow, detailed and written to make the process…

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Added by Emmanuelle Rieuf on June 16, 2016 at 10:30am — No Comments

Book: Introducing Data Science: Big Data, Machine Learning, and more, using Python tools

Summary

Introducing Data Science teaches you how to accomplish the fundamental tasks that occupy data scientists. Using the Python language and common Python libraries, you'll experience firsthand the challenges of dealing with data at scale and gain a solid foundation in data science.

About the Technology

Many companies need developers with data science skills to work on projects ranging from social media marketing to machine learning. Discovering…

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Added by Emmanuelle Rieuf on June 16, 2016 at 10:00am — No Comments

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