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The Do’s and Don’ts of Data Mining
Leading data mining and analytics experts give their favorite do's and don'ts, from "Do plan for data to be messy" to "Do not underestimate the power of a simpler-to-understand solution".
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10 Most Influential Analytics Leaders in India
Analytics India Magazine’s annual ranking of the 10 Most Influential Analytics Leaders in India, in terms of Impact, Leadership, Entrepreneurship and Analytics evangelism.
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Alpine Data Science Periodic Table
One of the most clever giveaways at the recent Strata Conference in Santa Clara was a Periodic Table of Data Science from Alpine.
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Anaconda: Free enterprise-ready Python for Big data, Predictive Analytics
125+ cross-platform tested and optimized Python packages for advanced analytics totally free, even for commercial use.
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10 Emerging Analytics Startups in India
India is becoming a powerhouse in Analytics, and here are 10 emerging Indian Analytics startups to watch in 2014: Crayon Data, Flutura, Axtria, Flytxt, Sapience Analytics, SIBIA Analytics, Ideal Analytics, FORMCEPT, IQR Consulting, and StatLabs.
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Deep Learning Wins Dogs vs Cats competition on Kaggle
A Deep learning expert wins Kaggle Dogs vs Cats image competition with an almost perfect result.
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CMSR Data Miner and Rule-Engine Software Suite – free academic use
CMSR - Cramer Modeling, Segmentation and Rules - is data miner and rule-engine suite having rule-engines as a unique feature. Rule-engines provide rule-based predictive model evaluation.
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More Data Mining with Weka
This online course teaches both principles and practical data mining techniques, lets students work on very big datasets, classify text, experiment with clustering, and much more.
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Determining the Value of Insights
With the value of Consumer Insights being questioned to justify ROI, the Market Research professionals need to figure out ways to quantify the value of those insights. Determining the value of insights is no easy task and requires focus on three key components.
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Viewpoint: Why your company should NOT use “Big Data”
Hardcore analytics (and Big Data) can add value, but only marginally and only for companies that have already mastered using the data they already have. The ‘obvious’ information from your own data can get you 90%+ of the total impact, so start there. The hard part is executing the basic insights across the organization.
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