When I try to explain Data Science and Analytics to business people or those interested in these fields, I use the following example to describe the four pillars of: Data, Platform/Tools, Algorithms, and know-how.
To me, "data" is like a collection of bones (say of an animal) scattered around, some clean and some hidden in dirt. If these pieces are collected and put together correctly, it will mimic or resemble the real skeleton of that animal. Some of the bone pieces are…Continue
Added by Khosrow Hassibi on October 13, 2015 at 7:39am — No Comments
Statistical analysis and data mining were the top skills that got people hired in 2014 based on LinkedIn analysis of 330 million LinkedIn member profiles. We live in an increasingly data-driven world, and businesses are aggressively hiring experts in data storage, retrieval, and analysis. Across the globe, statistics and data analysis skills were highly valued. In the US, India, and France, those skills are in particularly high demand.
Business professionals of all levels have asked me over the years what it is that they should know that their Data Science departments may not be telling them. To be candid, many Data Scientists operate in fear wondering what they should be doing as it relates to the business. In my judgment, the questions below address both…Continue
If you’re relatively new to Machine Learning and it’s applications, you’ll more than likely have come across some pretty technical terms that are often difficult for the novice mathematician/scientist to get their head around.
Following on from a previous blog, (10 Common NLP Terms Explained for the Text Analysis Novice), we decided to put together a list of 10 Machine…Continue
This blog post was originally published as part of an ongoing series, "Popular Algorithms Explained in Simple English" on the AYLIEN Text Analysis Blog.
Picture added by the…Continue
Added by Michael Walker on June 1, 2015 at 7:30am — No Comments
Humanity is going to be okay! The big bad robots are not going to come and get you...
In a recent Reddit AMA session, Bill Gates commented, “First the machines will do a lot of jobs for us and not be super intelligent… A few decades after that though the intelligence is strong enough to be a concern. I agree with Elon Musk and some others…Continue
Ulla B. Mogensen, Hemant Ishwaran, Thomas A. Gerds (2012). Evaluating Random Forests for Survival Analysis Using Prediction Error Curves. Journal of Statistical Software, 50(11), 1-23.
Abstract Prediction error curves are increasingly used to assess and compare predictions in survival analysis. This article surveys the R package pec which provides a set of functions for efficient computation of prediction error…
Added by Diego Marinho de Oliveira on April 10, 2015 at 12:21am — No Comments
Machine learning algorithms are parameterized so that they can be best adapted for a given problem. A difficulty is that configuring an algorithm for a given problem can be a project in and of itself.
Like selecting ‘the best’ algorithm for a problem you cannot know before hand which algorithm parameters will be best for a problem. The best thing to do is to investigate empirically with controlled experiments.
The caret R package was designed to make finding…Continue
Added by Diego Marinho de Oliveira on April 7, 2015 at 6:41am — No Comments
Authors: Michael J. Lopez / Gregory J. Matthews.Continue
Journal of Quantitative Analysis in Sports. Volume 11, Issue 1, Pages 5–12.
Abstract Computing and machine learning advancements have led to the creation of many cutting-edge predictive algorithms, some of which have been demonstrated to provide more accurate forecasts…
Added by Diego Marinho de Oliveira on April 7, 2015 at 12:46am — No Comments
Good day! Sharing a news –
CERN has published an Open Data Portal where you can play with their data for education or research. And, it actually has data from experiments of the Large…Continue
Added by Mohammad Oli Ahad on November 25, 2014 at 3:03am — No Comments
This exercise was done to understand the software skills that are in high demand for Data Science. Analysis was done by extracting the job postings from popular online websites. The findings are interesting. R continues to be the most popular skill, found in 70% of the postings. Python follows as a close second. Surprisingly, in spite all the talk about "Big Data Science", SQL comes up third. This shows that traditional RDBMS still continue to be the base for machine learning work…Continue
From episode 10 of my Naked Analyst Channel on YouTube.
I think I do - and it is the ‘appification’ of analytics. What I mean by this is the reduction of a complex analytic activity such as market segmentation, down to a single button on your computer interface. Very much like the…Continue
Added by derick.jose on June 17, 2014 at 2:00am — No Comments
We establish understanding of things in terms of Data or it will be better to say in terms of Big Data, the utilization of things, matters, issues, inventions, surroundings, maps and much more throughout our everyday life cycle, all of which has a certain data type to get input, process and output for us. Sometime we understand these in almost no time as a human, where data is being originated, what are we targeting for and more, and there are times, when some thing might take longer…Continue
Added by Atif Farid Mohammad on November 29, 2013 at 12:50am — No Comments
Smart organizations are using the power of data science and data produced by embedded sensors and machine devices to better measure performance, discover patterns, prevent problems, and improve…Continue
Practicing Data science indeed a long term effort than a learning handful of skills. We ought to be academically good enough to take up this challenge. However, if you think you came a long way from your academic rebuilding, but you still have that zeal & passion to take the oil from the data and fill the skill gap of data science then here is the warm-up tips. Below points must exercised before jumping into…Continue
Added by Manish Bhoge on October 18, 2013 at 9:26am — No Comments
Text (word) analysis and tokenized text modeling always give a chill air around ears, specially when you are new to machine learning. Thanks to Python and its extended libraries for its warm support around text analytics and machine learning. Scikit-learn is a savior and excellent support in text processing when you also understand some of the concept like "Bag of word", "Clustering" and "vectorization". Vectorization is must-to-know technique for all machine leaning learners, text miner…Continue
Added by Manish Bhoge on September 25, 2013 at 9:47am — No Comments
Natural language processing (NLP) involves machine learning, artificial intelligence, algorithms and linguistics related to interactions between computers and human languages. One important goal…Continue
Added by Michael Walker on August 20, 2013 at 7:27pm — No Comments