Sunday, August 12. 2012
Link roundup, week 32/2012
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A Strategic Mistake With Big Data — International Institute for Analytics
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That mistake is the development of a siloed, distinct big data strategy
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strategy for big data is a new facet of their overall enterprise data and analytic strategy
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the mess that many multi-channel retailers got themselves into through their entry into e-commerce
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retailers launched distinct e-commerce divisions. Some were even separate legal entities. As opposed to viewing e-commerce as a new facet of an overall retail strategy
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distinct processes and distinct infrastructure was created
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provide a consistent experience for customers across channels
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go into a store and grab a product and then find that same product on the retailer’s website. Guess what? They have no way to match those products
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separate, non-integrated strategies for big data will likely end up with systems and processes that are very difficult to integrate
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integrate big data into the overall infrastructure and current and future analytic processes
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Machine learning system can ID cities via pics | Cutting Edge - CNET News
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The system automatically picks out relevant architectural details from photos
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the details woven into the urban fabric that form a pattern
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computers are learning to ID your city just by looking at random photos
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Google Street View images of Paris, London, New York, and Barcelona
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features like the street signs, balconies, and lampposts of Paris to be distinct
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lack of stylistic coherence in American cities
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presented at Siggraph 2012
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emerging field of visual data mining, which is more complex than looking for patterns in text or numbers
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Alexei Efros
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we wish to automatically build a digital visual atlas of not only architectural but also natural geo-informative features for the entire planet
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Interviewing data scientist candidates? Ask these questions
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“It’s way over-hyped,” said Franks
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chief analytics officer for the data warehouse appliance vendor Teradata
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“softer skills.”
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“data artist.”
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data scientists are those that are able to understand the business problem; they’re able to apply creativity and present the results well
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intuition, which is hard to teach
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five core areas I look at
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Commitment
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1.
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Creativity
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2.
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thought process they went through deciding what to do. Someone who’s not creative is going to give me a list of the steps they went through one-by-one more from a technical perspective
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Business savvy
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3.
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what I want to hear is not just some technical reasons
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Knowing how much information to give to the non-technical people
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Presentation
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4.
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specific presentation as part of the interview process
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intuition
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5.
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art or music or some other type of creative area
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Hype Cycle for Cloud Computing 2012 (JPEG, 1221×839)
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Hype Cycle for Cloud Computing Shows Enterprises Finding Value in Big Data, Virtualization - Forbes
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8/04/2012
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Big Data will deliver transformational benefits to enterprises within 2 to 5 years
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by 2015 will enable enterprises adopting this technology to outperform competitors by 20%
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By 2014 the Personal Cloud will have replaced the personal computer
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Peta-governance? Controlling the explosion of Big Data models will prove difficult | LinkedIn
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you will soon be swamped with models
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many in-production churn models
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does it fit the latest customer data
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compared with various challenger models?
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responsibility for approval of the latest and greatest model
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more and more models
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developed in more and more tools and languages
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with more and more variables
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from more and more sources
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in more and more business processes
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demands tools such as IBM SPSS Collaboration and Deployment Services
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life-cycle management of models
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check in/check-out, change tracking, version control, and collaborative development and validation
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Machine Learning as a Service & Big Data Analytics | Licensing wise.io LLC
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wiseRF, is a fast, memory efficient, and scalable implementation of Random Forest
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C++ API hooks and Python bindings
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soon entering a private beta of our hosted platform offering Machine Learning as a Service
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And now a bunch of interesting upcoming online classes, ordered by start date:
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Web Intelligence and Big Data | Coursera
Seems to be focused on the tech level
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big-data platforms based on the ‘map-reduce’
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27 August 2012 (10 weeks long)
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Search, Indexing
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Analyzing Sentiment
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Databases and their evolution
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Classification, Clustering, and Mining
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Dealing with Uncertainty
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Predictive Analytics
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Neural Networks for Machine Learning | Coursera
Probably too specialized for my scope
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speech and object recognition, image segmentation, modeling language
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17 September 2012 (8 weeks long)
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proficiency in Matlab, Octave or Python
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understand what a probability density is
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Social Network Analysis | Coursera
SNA is *not* (necessarily) about social media. Probably too specialized as well, not sure if I should sign up.
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theory and computational tools
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September 2012 (8 weeks long)
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nodes, edges, adjacency matrix, one and two-mode networks, node degree
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Gephi
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Random network models
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breadth-first search
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average shortest path
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betweenness, closeness, eigenvector centrality (+ PageRank)
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clustering, community structure
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R statistical programming language along with NetLogo
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Gephi for visualization and analysis
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interactive demonstrations will be primarily in NetLogo
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Mathematical Biostatistics Boot Camp | Coursera
The “bio-” will not really play a role
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fundamental probability and statistical concepts used in elementary data analysis
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small amount of linear algebra and programming are necessary
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24 September 2012 (7 weeks long)
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probability distributions and their properties
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statistical likelihood
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confidence intervals
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R statistical programming language
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Computing for Data Analysis | Coursera
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learn to program in R
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modern statistical methods
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24 September 2012 (4 weeks long)
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- Software for Data Analysis by John M. Chambers (Springer)
- S Programming by Brian D. Ripley and William N. Venables (Springer)
- Programming with Data by John M. Chambers (Springer)
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two large programming assignments
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How is this course different from “Data Analysis”?
This course will focus on developing the programming skills
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will not focus on teaching properties of specific statistical algorithms
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Data Analysis | Coursera
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22 January 2013 (8 weeks long)
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Big Data
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applied statistics course focusing on data analysis
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statistical methods like linear regression, principal components analysis, cross-validation, and p-values
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R statistical programming language
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How is this course different from “Computing for Data Analysis”?
This course will focus on how to plan, carry out, and communicate analyses of real data sets
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Scientific Computing | Coursera
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To Be Announced
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MATLAB
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graphical techniques for information presentations
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Introduction to Data Science | Coursera
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basics of data science
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practical experience programming massive databases
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To Be Announced
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MapReduce
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algorithms for data mining
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statistical modeling
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Bill Howe is the Director of Research for Scalable Data Analytics
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database design
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SQL, NoSQL, and NewSQL
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Algorithm design in Hadoop
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statistical analysis at scale
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data mining
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Case studies in analytics
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programming experience with Java or Python
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familiarity with databases
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Mining of Massive Datasets
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Professional NoSQL
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two exams
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quiz and a programming assignment
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Amazon Web Services or Microsoft Azure cloud services
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SML: Scalable Machine Learning
Links to lengthy YouTube videos, recordings of slide-based classroom talks. Not sure if too useful.
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Instructor: Alex Smola
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2 Statistics
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5 GLM
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6 Kernels
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9 Graphical Models
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