Invention in Data Cycle
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Taps and Swipes: Intuition vs. Machine Learning in UX Design

Taps and Swipes: Intuition vs. Machine Learning in UX Design | Invention in Data Cycle | Scoop.it
The list of things you can do with a touchscreen reads like a manual for misbehaving in elementary school: tap, swipe, double-tap, pinch, pull, drag, flick, twist. Our natural affinity for manipula...
Dominique Mariko's insight:

"The process of choosing this line is what a machine learning algorithm would have done for us, but our data was simple enough that we didn’t need to bother with writing any code. And of course, we only knew that our data was simple because we bothered to visualize it. Whatever route you take, just don’t forget to take a good look at the data first. Only then should you decide for yourself how aggressively you want to dig into it, and how much you need your algorithms to get involved." 

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Becoming a Data Scientist - Curriculum via Metromap - Pragmatic Perspectives

Becoming a Data Scientist - Curriculum via Metromap - Pragmatic Perspectives | Invention in Data Cycle | Scoop.it
Becoming a data scientist a journey; for sure a challenging one. But how do you go about becoming one? Where to start? When do you start seeing light at the end of the tunnel? What is the learning roadmap? What tools and techniques do I need to know? How will you know when you have achieved your goal?
Dominique Mariko's insight:

The Best. Ever.

 

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icrunchdata news | Use AR (augmented reality) to cleanse, analyse and represent Big Data? [part 2]

icrunchdata news | Use AR (augmented reality) to cleanse, analyse and represent Big Data? [part 2] | Invention in Data Cycle | Scoop.it
Let us look at a scenario to help exemplify a point. In project management, there was a full Project Life Cycle (PLC). The full PLC included all areas such as feasibility study, Proof Of Concept, planning, budgeting, execution, testing, implementation, post-implementation review and roll-out. In this framework, it often was the case that the project would in fact take so long that it would be obsolete before it was implemented. This wasn’t very effective. As a result, the concept of breaking the project down into smaller segments arose. One such methodology is Agile project management. Simply put, Agile is where a project is broken down into short term activities that are responsive to the changing directions of that which the project is attempting to handle. This is so that in the smaller segments, actual progress can be obtained and also include a high degree of flexibility.
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Why big-data is doomed

Why big-data is doomed | Invention in Data Cycle | Scoop.it
Dominique Mariko's insight:

Identifying the data you need before running valuable analysis is the one way to make your data cheaper.

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3 Ways to Test the Accuracy of Your Predictive Models

3 Ways to Test the Accuracy of Your Predictive Models | Invention in Data Cycle | Scoop.it
There are many different tests you can use to determine if the predictive models you create will prove valuable to your organization. We spoke to three top data mining experts to learn the tests they use to measure the accuracy of their own results, and what makes each test so effective.
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Data Analysis: The Hard Parts

Data Analysis: The Hard Parts | Invention in Data Cycle | Scoop.it
I don't know whether this word exists, but _mainstreamification_ is what's happening to data analysis right now. Projects like [Pandas] or [scikit-learn] are open source, free, and allow anyone with some Python skills do lift some serious data analysis. Projects like [MLbase] or [Apache Mahout] work to make data analysis scalable such that you can tackle those terabytes of old log data right away. Events like the [Urban Data Hack], which just took place in London, show how easy it has become to do some pretty impressive stuff with data. The general message is: Data analysis has become super easy....
Dominique Mariko's insight:
The algorithms don’t care, they’ll give you a result anyway.
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Nobel laureate Daniel Kahneman discusses life and work in behavioral economics | Harvard Magazine

Nobel laureate Daniel Kahneman discusses life and work in behavioral economics | Harvard Magazine | Invention in Data Cycle | Scoop.it
At Harvard, Nobel laureate Daniel Kahneman discusses decisionmaking with Walmsley University professor Cass Sunstein of Harvard Law School.
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INFORMATION ACTION: actionable ideas for evidence-based decision-making: The Information Management Tube Map

INFORMATION ACTION: actionable ideas for evidence-based decision-making: The Information Management Tube Map | Invention in Data Cycle | Scoop.it
Dominique Mariko's insight:
Another Tube Map to govern information and data
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Time-Series Databases and InfluxDB | Xaprb

Time-Series Databases and InfluxDB | Xaprb | Invention in Data Cycle | Scoop.it

The data we gather is increasingly timestamped and dealt with in time-series ways. For the last 10 years, I’ve worked with “roll-up” or “summary” tables almost constantly. I built, and saw others build, the same types of solutions over and over. For example, I probably consulted with over a dozen companies who do search-engine marketing and advertising. Cost tables are a given, and there’s usually cost-per-ad-per-day and half a dozen other summary tables. In my case I saw these things in the MySQL context, but you can pick your technology and someone’s trying to do time-series tasks on top of it.

Dominique Mariko's insight:

Very good review of time-series databases

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Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine Mar-Apr 2014

Understanding big data leads to insights, efficiencies, and saved lives | Harvard Magazine Mar-Apr 2014 | Invention in Data Cycle | Scoop.it
Information science promises to change the world.
Dominique Mariko's insight:

"It is not the quantity of data that is revolutionary. “The big data revolution is that now we can do something with the data.”"

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Tweet from @davidandrzej

Dominique Mariko's insight:

Explores data logs reduction through use cases : compliance and complexity in distributed systems. PDF for #strataconf

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Comparing machine learning classifiers based on their hyperplanes or decision boundaries - Data Scientist in Ginza, Tokyo

Comparing machine learning classifiers based on their hyperplanes or decision boundaries - Data Scientist in Ginza, Tokyo | Invention in Data Cycle | Scoop.it
In Japanese version of this blog, I've written a series of posts about how each kind of machine learning classifiers draws various classification hyperplanes or decision boundaries. So in this post I want to show you a summary of the series and how their hyperplanes or decision boundaries vary (tran…
Dominique Mariko's insight:
Comparing dataviz
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Interview with Dr. Roy Marsten, the Man Shaping Big Data

Interview with Dr. Roy Marsten, the Man Shaping Big Data | Invention in Data Cycle | Scoop.it
By Vincent Granville
Dr. Roy Marsten, author of more than 30 papers on computational optimization in academic journals, has been a professor at MIT, Northweste…
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On the scalability of statistical procedures: why the p-value bashers just don’t get it. | Simply Statistics

On the scalability of statistical procedures: why the p-value bashers just don’t get it. | Simply Statistics | Invention in Data Cycle | Scoop.it
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