From Data to Insight: Uncovering the Hidden Costs of Data
Here are nine of the most common ways that data can quickly overwhelm your budget if you’re not careful.
1. How many geographic regions do you want to cover? How local do you need to go?
2. What kind of level of detail or content granularity do you want the data to have?
3. How many sources or types of data do you want to use?
4. What kinds of collection processes are you going to have to use to get your data into your system?
5. How far back do you want the data to go? How much data do you want or need to keep over time?
6. How much effort is it going to take to process the data?
7. How frequently will you need to process the data?
8. How is the quantity and magnitude of the data going to affect your bandwidth, and flow through your pipelines?
9. How frequently will your users want to access the data – how far back will they want to go when accessing? Or are your users really going to want this data, or the insight you assume you can get from it?
Complete & Comprehensive Life Cycle of Data Science
To take your complete & comprehensive infographics on Life Cycle of Data Science in High Resolution Jpeg File or in Original Pdf Format, visit my blog. And then, I wish to hear your opinions. What do you think about this research-based-infographics on Data Science Life Cycle?
Visualize and Handle Traffic Information with GraphHopper in Real-time for Cologne (Germany, Köln)
As our Directions API currently does not include traffic data we still show in this blog post that it is possible to integrate traffic data into GraphHopper if you have the data. A few days ago I’ve blogged about a simple way to feed GraphHopper with generic traffic data from elsewhere. Today we look into one specific example: Cologne.
Data Scientists Automated and Unemployed by 2025?
Will Data Scientists be unemployed by 2025? Majority of voters in latest KDnuggets Poll expect expert-level Data Science to be automated in 10 years or less.
corrected MCMC samplers for multivariate probit models
Xiyun Jiao and David A. van Dyk arXived a paper correcting an MCMC sampler and R package MNP for the multivariate probit model, proposed by Imai and van Dyk in 2005. Earlier versions of the Gibbs sampler for the multivariate probit model by Rob McCulloch and Peter Rossi in 1994, with a Metropolis update added by Agostino Nobile, and finally an improved version developed by Imai and van Dyk in 2005. As noted in the above quote, Jiao and van Dyk have discovered two mistakes in this latest version, jeopardizing the validity of the output.
Data Science in HR
Last year in a post on interesting R topics presented at the JSM I described how data scientists in Google’s human resources department were using R and predictive analytics to better understand the characteristics of its workforce. Google may very well have done the pioneering work, but predictive analytics for HR applications is going mainstream. In the still below from a Predictive Analytics Times video on Data Science for Work Force Optimization Pasha Roberts, Chief Scientists at Talent Analytics, describes using survival analysis for modeling employee retention.
Bolding plotting characters
The lwd argument is most commonly used to adjust line width in functions like plot(), lines(), abline(), and other plotting functions, but it can also be used to adjust the line width of plotting characters.
What is your plan for cultivating analytics talent?
With the supply of analytics talent in the labor force slowly growing to meet demand, a majority of companies are looking within their own walls for solutions. While there is clearly a need for new graduates and data scientists who know the most cutting-edge techniques of analysis, there is just as much of a need for information workers who know both the business and technical sides of an organization. Current employees who are able to develop an analytics skill set and combine that with their knowledge of the business can be invaluable when moving analytical insights across the ‘last mile’ to decision makers. For companies that find it difficult to lure top talent, their analytics capability can make substantial advances by looking differently at their own existing talent.
Why your Brain Loves Data Visualization
In commercial terms, how we perceive information determines how we process, interpret and action it. Our brains are wired to process visual information and how we do this ensures we are leveraging Data Visualization to full effect.
Parallel Machine Learning with Hogwild!
In this blog post, I will explain what stochastic gradient descent (SGD) is and how thread locking has a very large effect on performance. I will attempt to explain how parallel algorithms for machine learning such as Hogwild! work, why they have transformed big data analytics, and how GraphLab Create not only adopts these techniques but also actively pushes the frontier of parallel machine learning algorithms.