Friday, October 28, 2016

Network Analysis and Social Network Analysis - Bibliography and Videos



Network Structure Inference, A Survey: Motivations, Methods, and
Ivan Brugere, University of Illinois at Chicago
Brian Gallagher, Lawrence Livermore National Laboratory
Tanya Y. Berger-Wolf, University of Illinois at Chicago

Social Network Analysis: Methods and Applications

Stanley Wasserman, Katherine Faust
Cambridge University Press, 25-Nov-1994 - Social Science - 825 pages

Social network analysis, which focuses on relationships among social entities, is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

The structure and function of complex networks
M. E. J. Newman
Department of Physics, University of Michigan, Ann Arbor, MI 48109, U.S.A. and
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, U.S.A.

Analyzing Participation of Students in Online Courses Using Social
Network Analysis Techniques
Reihaneh Rabbany k., Mansoureh Takaffoli and Osmar R. Za¨ıane,
Department of Computing Science, University of Alberta, Canada

Social Network Analysis and Mining to Support the
Assessment of On-line Student Participation

Big Data over Networks

Shuguang Cui, Alfred O. Hero, III, Zhi-Quan Luo
Cambridge University Press, 14-Jan-2016 - Computers - 457 pages

Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike.

Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Full Book
David Easley
Dept. of Economics
Cornell University

Jon Kleinberg
Dept. of Computer Science
Cornell University
Cambridge University Press, 2010
Draft version: June 10, 2010.

Fundamentals of Predictive Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang
Springer, 07-Sep-2015 - Computers - 239 pages

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies.

This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation.

Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material.

Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships

Koen W. De Bock
Routledge, Mar 23, 2016 - 348 pages

Sunday, October 2, 2016

IBM Bluemix - Cloud Computing Platform

How IBM's Bluemix Garages Woo Enterprises And Startups To The Big Blue Cloud
The locations let IBM teach both startups and big companies how to harness its cloud services.

IBM started its Bluemix Garages to go close to startup entrepreneurs.  Bluemix Garages are IBM establishments  typically embedded within incubator or coworking spaces popular with startups. developers fo startup firms can get assistance from IBM engineers in exploring its Bluemix cloud platform. The first Bluemix Garage was opened in 2014 at the San Francisco branch of Galvanize, a company offering workspace and tech training at locations across the country.

Saturday, September 17, 2016

IBM IoT - Products and Systems

Cognitive IoT is the use of cognitive computing technologies in combination with data generated by connected devices and the actions those devices can perform.

Get started with Watson Analytics

3 June 2016  uploaded



IBM wants to replace the spreadsheet with Watson Analytics

IBM Bluemix

What is Bluemix
The cloud platform powered by the world’s most popular open source projects

Products on Bluemix

Data and analytics
Application services
Internet of Things

Bluemix and Internet of Things

Experience a fully managed, cloud-hosted service designed to simplify and derive value from your IoT devices. See how companies are using Watson Internet of Things to transform their business from the inside out.
How it all fits together
Connect your device, send data to our cloud, set up and manage your devices, and use APIs to connect apps to your device data.

Start with your device – whether it’s a sensor, gateway, or something else – and let us help you connect it with one of our recipes.

Your device data is always secure when you connect to the cloud using open, lightweight MQTT messaging protocol or HTTP.
IBM Watson IoT Platform

The hub of the IBM IoT approach – set up and manage your connected devices so your apps can access live and historical data.

REST and real-time APIs
Use our secure APIs to connect your apps with data from your devices.

Your application and analytics
Create applications within IBM Bluemix, another cloud, or your own servers to interpret data.

How much will it (IBM Watson IoT Platform)  cost?

The IBM Watson IoT Platform charges on three metrics.

The average number of devices you connect over the month. You get 20 free devices a month with each plan.

The amount of data that devices exchange with the IBM Watson IoT Platform and associated applications.
You get free 100 MB data traffic a month with each plan (equivalent to 50,000 messages)
*assuming an average message size of 200 bytes

The amount of data stored in a historical database.
You get 1 GB free storage a month with each plan

Updated  20 September 2016,  24 March 2016

Sunday, July 24, 2016

Senior Analytics Scientist - Risk Analytics - Job Specification

A Analytics-driven e-commerce company is looking for:

Job Title: Senior Analytics Scientist - Risk Analytics

Role Outline
Senior Analytics Scientist - Risk Analytics and reports to the Sr. Mgr / Director leading the team.

The key requirement for the role is the ability to understand the business, develop data driven solutions to address business problems and provide analytic support to the risk analytics group. The individual will possess the ability to work in teams and display a proactive learning attitude.

Job Description
Job Title : Senior Analytics Scientist - Risk Analytics
Department : Risk Analytics
Reports To : Sr. Manager / Director

Key responsibilities
* Key responsibilities include
o Building models to predict risk and other key metrics
o Coming up with data driven solutions to control risk
o Finding opportunities to acquire more customers by modifying/optimizing existing rules
o Doing periodic upgrades of the underwriting strategy based on business requirements
o Evaluating 3rd party solutions for predicting/controlling risk of the portfolio
o Running periodic controlled tests to optimize underwriting
o Monitoring key portfolio metrics and take data driven actions based on the performance
* Business Knowledge: Develop an understanding of the domain/function. Manage business process (es) in the work area. The individual is expected to develop domain expertise in his/her work area.
* Teamwork: Develop cross site relationships to enhance leverage of ideas. Set and manage partner expectations. Drive implementation of projects with Engineering team while partnering seamlessly with cross site team members.
* Communication: Responsibly perform end to end project communication across the various levels in the organization.

Candidate Specification:
* Should have solid understanding of probability and stats; Bayesian methods, probability distributions, Central limit theorem etc.
* Should be familiar with some of the following GLM, logistic regression, Random forest, Gradient boosting trees, CART, Naïve bayes, Linear Program, Mixed Integer program, etc.
* Knowledge of analytical tools such as R/Python/SAS/SQL
* Experience in handling complex data sources
* Dexterity with MySQL, MS Excel
* Strong Analytical aptitude and logical reasoning ability
* Strong presentation and communication skills.
* Strong process/project management skill

* 3 - 5 years of experience in Financial Services/Analytics Industry/ecommerce
* Understanding of the financial services business