“The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.”
Don & Alex Tapscott, authors Blockchain Revolution (2016)
Alex Tapscott: "Blockchain Revolution" | Talks at Google
11 July 2016
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Network Structure Inference, A Survey: Motivations, Methods, and
Applications
Ivan Brugere, University of Illinois at Chicago
Brian Gallagher, Lawrence Livermore National Laboratory
Tanya Y. Berger-Wolf, University of Illinois at Chicago https://arxiv.org/pdf/1610.00782.pdf
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. https://books.google.co.in/books?hl=en&lr=&id=CAm2DpIqRUIC
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.
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.
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.
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.
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:
Skills:
* 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
Preferred:
* 3 - 5 years of experience in Financial Services/Analytics Industry/ecommerce
* Understanding of the financial services business
Simplify IT’s Six Levers
The following are the six levers for simplifying IT:
1. Intelligent Demand Management.
2. Application and Data Simplification.
3. Infrastructure-Technology-Pattern Reduction.
4. Simplified IT Organization and an Enabled IT Workforce.
5. Effective Governance and Simplified Processes.
6. A Shared-Services Model and Optimized Sourcing.
