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Scientists create world's first biologically powered computer chip

Scientists create world's first biologically powered computer chip | Dr.T | Scoop.it

The dream of melding biological and man-made machinery is now a little more real with the announcement that Columbia Engineering researchers have successfully harnessed a chemical energy-producing biological process to power a solid state CMOS...


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Richard Platt's curator insight, December 24, 2015 9:00 PM

According to study lead professor Ken Shepard, this is the world's first successful effort to isolate a biological process and use it to power an integrated circuit, much like the ones we use in phones and computers.  The researchers developed the system by using an artificially created lipid bilayer membrane containing naturally occurring ion pumps, which are powered by the biological world's "energy currency molecule," ATP (adenosine triphosphate). ATP is the coenzyme that transfers chemical energy between living cells. It is an end product of processes such as photosynthesis and cellular respiration, and it powers the mechanical work of living systems such as cell division and muscle contraction.   The scientists connected the lipid membrane to a conventional solid-state complementary metal-oxide-semiconductor (CMOS) integrated circuit, and the ion pumps powered the circuit.  "Ion pumps basically act very similarly to transistors," Shepard tells Gizmag. "The one we used is the same kind of pump that is used to maintain the resting potential in neurons. The pump produces an actual potential across an artificial lipid membrane. We packaged that with the IC and we used the energy across that membrane due to those pumped ions to power the integrated circuit."

Vagabond Lifestyles's curator insight, January 3, 9:06 AM

Bio Chem Machines Explained

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Shortest Path using Dijkstra's Algorithm - Techie Me

Shortest Path using Dijkstra's Algorithm - Techie Me | Dr.T | Scoop.it
Shortest Path using Dijkstra's Algorithm is used to find Single Source shortest Paths to all vertices of graph in case the graph doesn't have negative edges
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How to Create a Powerpoint Presentation that Won't Put People to Sleep

How to Create a Powerpoint Presentation that Won't Put People to Sleep | Dr.T | Scoop.it
Before a presentation your nerves become fired up and your heart starts to pound. While the audience may be sizing you up, they are only hoping for an engaging presentation. They want you to succeed and quite frankly they need you to succeed. The infographic provided by Udemy walks us through the three critical points to creating a great presentation.

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Baiba Svenca's curator insight, March 23, 2015 12:42 PM

Attractive and informative infographic on PowerPoint presentations.

Thanks for the suggestion to Ivo Novy.

Nedko Aldev's curator insight, March 24, 2015 5:31 AM

 

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Fenia's curator insight, March 24, 2015 2:37 PM

Useful guide to good presentations - not only for ppt but also for other presentation tools 

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10 Tricks to Make Yourself a Chromecast Master

10 Tricks to Make Yourself a Chromecast Master | Dr.T | Scoop.it
Got yourself a super-cheap streaming dongle from Google? Or thinking about picking one up? Here are 10 lesser-known tricks and tips that you can use to get more from your Chromecast and unlock some of its hidden potential.

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kamusa's curator insight, December 26, 2014 7:56 AM

ajouter votre aperçu ...

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Implementing a Distributed Deep Learning Network over Spark – Data Science Central

Implementing a Distributed Deep Learning Network over Spark – Data Science Central | Dr.T | Scoop.it

Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. Our intention is to parallelize the training phase of the network and consequently reduce training time. We have built the first prototype of our distributed deep learning network over Spark, which has emerged as a de-facto standard for realizing machine learning at scale.


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How to become a data scientist in 8 easy steps: the infographic

How to become a data scientist in 8 easy steps: the infographic | Dr.T | Scoop.it
This post was written by the team behind DataCamp, the online interactive learning platform for data science.   After being dubbed “sexiest job of the 21st Century” by Harvard Business Review, data scientists have stirred the interest of the general public. Many people are intrigued by this job, namely because the name has an interesting […]

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NoSQL Databases: An Overview

NoSQL Databases: An Overview | Dr.T | Scoop.it
Over the last few years we have seen the rise of a new type of databases, known as NoSQL databases, that are challenging the dominance of relational databases. Relational databases have dominated the software industry for a long time providing mechanisms to store data persistently, concurrency control, transactions, mostly standard interfaces and mechanisms to integrate application data, reporting. The dominance of relational databases, however, is cracking.

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The Next Big Thing in Big Data: People Analytics

The Next Big Thing in Big Data: People Analytics | Dr.T | Scoop.it

By combining data from both real and virtual worlds, we can now understand behavior at a previously unimaginable scale.

When we use data to uncover the workplace behaviors that make people effective, happy, creative, experts, leaders, followers, early adopters, and so on, we are using “people analytics.”


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Jacek Bugajski's curator insight, May 18, 2013 5:31 AM

People Analytics - hmmm... Great idea for companies ;) 

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How to Become a Data Scientist

How to Become a Data Scientist | Dr.T | Scoop.it

Summary:  If you are wondering how to become a Data Scientist or what that title really means, try these insights.

I got started in data science way back.  I’ve been a commercial predictive modeler since 2001 and as naming trends have changed I now identify myself as a Data Scientist.  No one gave me this title.  But by observing the literature, the job listings, and my peers in the field it was clear that Data Scientist communicated most clearly what my knowledge and experience have led me to become.

These days you can get a degree in data science so you can show your diploma that certifies your credentials.  But these are relatively new so, with all due respect, if you only recently got your degree you are still a beginner.  Those of us who use this title today most likely came from combination backgrounds of business, hard science, computer science, operations research, and statistics.

What you call yourself is one thing but what your employer or client is looking for can be quite a different kettle of fish.  A lot has been written about data scientists being as elusive as unicorns.  Not being a unicorn I’d say this sets the bar pretty high.  Additionally, as I’ve perused the job listings it is equally true that the title is used so loosely and with such little understanding that an ad for data scientist may actually describe an entry level analyst and some ads for analysts are looking for polymath data scientists. 

All of this confusion over what we’re called and what we actually do can make you down right schizophrenic.  This makes it all the more complicated to answer the frequent inquiries I get from folks still in school or early in their career about how to become a data scientist.

Imagine my surprise and delight when in the space of a week two publications came across my desk that not only cast new light and understanding on this question but also have helped me understand that there is not just one definition of data scientist, but a reasoned argument (based on statistical analysis) that there are in fact four types.

Four Types of Data Scientists

The information here comes from the O’Reilly paper “Analyzing the Analyzers” by Harris, Murphy, and Vaisman, 2013.  My hat’s off to these folks for their insightful survey and conclusions drawn by statistical analysis of those results.  This is a must read.  I was able to download this at no charge from http://www.oreilly.com/data/free/analyzing-the-analyzers.csp.

There are 40 pages of good analysis here so this will be only the highest level summary.  In short, they conclude there are four types of Data Scientists differentiated not so much by the breadth of knowledge, which is similar, but their depth in specific areas and how each type prefers to interact with data science problems.

Data Businesspeople

Data Creatives

Data Developers

Data Researchers

By evaluating 22 specific skills and multi-part self-identification statements they cluster and generalize according to these descriptions.  I am betting you will recognize yourself in one of these categories.

Data Businesspeople are those that are most focused on the organization and how data projects yield profit. They were most likely to rate themselves highly as leaders and entrepreneurs, and the most likely to have reported managing an employee. They were also quite likely to have done contract or consulting work, and a substantial proportion have started a business. Although they were the least likely to have an advanced degree among respondents, they were the most likely to have an MBA. But Data Businesspeople definitely have technical skills and were particularly likely to have undergraduate Engineering degrees. And they work with real data — about 90% report at least occasionally working on gigabyte-scale problems. 

Data Creatives.  Data scientists can often tackle the entire soup-to-nuts analytics process on their own: from extracting data, to integrating and layering it, to performing statistical or other advanced analyses, to creating compelling visualizations and interpretations, to building tools to make the analysis scalable and broadly applicable. We think of Data Creatives as the broadest of data scientists, those who excel at applying a wide range of tools and technologies to a problem, or creating innovative prototypes at hackathons — the quintessential Jack of All Trades. They have substantial academic experience with about three-quarters having taught classes and presented papers. Common undergraduate degrees were in areas like Economics and Statistics. Relatively few Data Creatives have a PhD. As the group most likely to identify as a Hacker they also had the deepest Open Source experience with about half contributing to OSS projects and about half working on Open Data projects.

Data Developer.  We think of Data Developers as people focused on the technical problem of managing data — how to get it, store it, and learn from it. Our Data Developers tended to rate themselves fairly highly as Scientists, although not as highly as Data Researchers did. This makes sense particularly for those closely integrated with the Machine Learning and related academic communities. Data Developers are clearly writing code in their day-to-day work. About half have Computer Science or Computer Engineering degrees.  More Data Developers land in the Machine Learning/ Big Data skills group than other types of data scientist.

Data Researchers.  One of the interesting career paths that leads to a title like “data scientist” starts with academic research in the physical or social sciences, or in statistics. Many organizations have realized the value of deep academic training in the use of data to understand complex processes, even if their business domains may be quite different from classic scientific fields. The majority of respondents whose top Skills Group was Statistics ended up in this category. Nearly 75% of Data Researchers have published in peer-reviewed journals and over half have a PhD.

What Does this Mean for Someone Seeking to Enter the Field?

So if I am a young person seeking to enter Data Science how are these descriptions useful?  It’s possible that you could train and develop an emphasis that would lead you into the Researcher, Developer, or Creative roles.  It is less likely that education alone will put you on the Businesspeople track which implies experiences in business, not just education.  But here’s what’s interesting.  According to Harris, Murphy, and Vaisman it’s not the skills that are different but the way we choose to emphasize them in our approach to Data Science problems.  Here’s their chart.


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vitonzhang's curator insight, October 25, 12:06 AM
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10 Secret Features Hidden In Your Mac

10 Secret Features Hidden In Your Mac | Dr.T | Scoop.it
Use these simple tips and tricks for your Mac to perform intuitive shortcuts and make your life easier.

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Andy Britnell's curator insight, July 21, 2014 5:23 AM

I knew a few of these but didn't know how to produce 's !!

Thierry Benchetrit's curator insight, July 25, 2014 7:52 AM

http://techneb.com/shop/ boutique du gadget et du cadeau techno 

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Big Data: The Hadoop Business Case | Big Data Journal

Big Data: The Hadoop Business Case | Big Data Journal | Dr.T | Scoop.it
Big Data: The #Hadoop Business Case by @JGlesner ▸ http://t.co/vE3uJtaW2b #BigData

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Charles Gerth's curator insight, July 11, 2014 10:11 AM

This is a great read, digging into data characteristics and a opportunity to not just define Big Data business potential but,  a high level model to factor in cost of solution.  

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Spark Release 1.0.0 | Apache Spark

Spark Release 1.0.0 | Apache Spark | Dr.T | Scoop.it

Spark 1.0.0 is a major release marking the start of the 1.X line. This release brings both a variety of new features and strong API compatibility guarantees throughout the 1.X line. Spark 1.0 adds a new major component, Spark SQL, for loading and manipulating structured data in Spark. It includes major extensions to all of Spark’s existing standard libraries (ML, Streaming, and GraphX) while also enhancing language support in Java and Python. Finally, Spark 1.0 brings operational improvements including full support for the Hadoop/YARN security model and a unified submission process for all supported cluster managers.


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Getting Started With MongoLab And The MongoDB Shell

Getting Started With MongoLab And The MongoDB Shell | Dr.T | Scoop.it

Yesterday, I blogged about MongoDB: The Definitive Guide by Kristina Chodorow. It is an excellent book and has gotten me really interested in learning more about MongoDB. Apparently, MongoDB is really easy to download, install, and run locally; but, for some reason, I wanted to try running it as a remote database. So, I signed up for MongoLab - a hosted MongoDB platform that has a free developer sandbox. And, within minutes, I had created my database, connected to it from the Mongo Shell, and was performing CRUD (Create, Read, Update, and Delete) operations!

To start with, I used the Homebrew package manager to install MongoDB:

brew install mongodb

The installation was very easy and went off without error.

Then, I signed up for a MongoLab account and created a database using the free developer sandbox. This process was also very easy! Once logged-in, I clicked on the Create New button to create a new database:


MongoLab allows you to select the Cloud provider for your MongoDB database; I don't really know one provider from another, so I just went with Amazon Web Services, which was selected by default. Then, I chose to use the free developer sandbox, which has limitations, but is perfect for some experimentation.

I am not exactly sure about this following statement, but I believe that the database name you choose - within the developer sandbox - has to be unique. I say this because when I went to choose the database name, "Ben," MongoLab complained that the given name was already taken. I assume this is a byproduct of the shared sandbox and will not be an issue with a dedicated plan.


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Lambda Architecture for Big Data Systems

Lambda Architecture for Big Data Systems | Dr.T | Scoop.it
Guest blog post by Michael Walker




Big data analytical ecosystem architecture is in early stages of development.
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How to Create a Powerpoint Presentation that Won't Put People to Sleep

How to Create a Powerpoint Presentation that Won't Put People to Sleep | Dr.T | Scoop.it
Before a presentation your nerves become fired up and your heart starts to pound. While the audience may be sizing you up, they are only hoping for an engaging presentation. They want you to succeed and quite frankly they need you to succeed. The infographic provided by Udemy walks us through the three critical points to creating a great presentation.

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Baiba Svenca's curator insight, March 23, 2015 12:42 PM

Attractive and informative infographic on PowerPoint presentations.

Thanks for the suggestion to Ivo Novy.

Nedko Aldev's curator insight, March 24, 2015 5:31 AM

 

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Fenia's curator insight, March 24, 2015 2:37 PM

Useful guide to good presentations - not only for ppt but also for other presentation tools 

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Chicago builds ETL toolkit for open data -- GCN

Chicago builds ETL toolkit for open data -- GCN | Dr.T | Scoop.it

Data officials in Chicago built an automated extract transform load (ETL) framework to more quickly and easily open city data.

 

About a year ago, the city government embedded Pentaho Data Integration (PDI), a graphical extract-transform-load (ETL) tool with pre-built and custom components to process big data, into its OpenData ETL Utility Kit. The kit provides several utilities and a framework to help governments extract data from a database and upload it to an open data portal using automated ETL processes. ...


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10 Must-Read Articles About Big Data

10 Must-Read Articles About Big Data | Dr.T | Scoop.it
Ever heard that once something gets on the internet it stays there forever? And that's true. Everything we do, leaves tremendous amount of data. Big data.

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sandra alvaro's curator insight, November 30, 2014 4:27 AM

the potentail of assesing and analyzing various data for eLearning industry.

This week we gathered top 10 articles about big data to show you the potential of assesing and analyzing various data for eLearning industry. - See more at: http://blog.talentlms.com/10-must-read-articles-about-big-data/#sthash.2m73p4TT.dpuf
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Be a Data Scientist in 8 steps!

Data science is the new thing! How to be a data scientist? See here. This was originally was written by the team behind DataCamp, - the online interactive lea…

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Spark officially sets a new record in large-scale sorting – Databricks

Spark officially sets a new record in large-scale sorting – Databricks | Dr.T | Scoop.it

A month ago, we shared with you our entry to the 2014 Gray Sort competition, a 3rd-party benchmark measuring how fast a system can sort 100 TB of data (1 trillion records). Today, we are happy to announce that our entry has been reviewed by the benchmark committee and we have officially won the Daytona GraySort contest!


In case you missed our earlier blog post, using Spark on 206 EC2 machines, we sorted 100 TB of data on disk in 23 minutes. In comparison, the previous world record set by Hadoop MapReduce used 2100 machines and took 72 minutes. This means that Spark sorted the same data 3X faster using 10X fewer machines. All the sorting took place on disk (HDFS), without using Spark’s in-memory cache. This entry tied with a UCSD research team building high performance systems and we jointly set a new world record.


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A New Smart City Cloud Platform in Boston | StateTech

A New Smart City Cloud Platform in Boston | StateTech | Dr.T | Scoop.it

A new cloud-based smart city system being developed in Boston could be a model for other state and local governments.

The project is called SCOPE, and it stands for Smart-city Cloud-based Open Platform & Eco-system. Boston University’s Rafik B. Hariri Institute for Computing and Computational Science & Engineering is spearheading the project in collaboration with several private-sector firms and multiple state and local agencies, including Massachusetts’ lead agency for technology — MassIT — and the Boston Region Metropolitan Planning Organization.

SCOPE’s primary goal is to “develop and implement smart-city services that aim to improve the quality of urban life”...


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Deep Learning Sentiment Analysis for Movie Reviews using Neo4j - Neo4j Graph Database

Deep Learning Sentiment Analysis for Movie Reviews using Neo4j - Neo4j Graph Database | Dr.T | Scoop.it

Kenny Bastani talks through using Neo4j for Deep Learning Sentiment Analysis for Movie Reviews.

Sentiment analysis uses natural language processing to extract features of a text that relate to subjective information found in source materials.

 


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James J. Goldsmith's curator insight, September 22, 2014 1:30 PM

Very interesting topic.  From the article:  " movie review website allows users to submit reviews describing what they either liked or disliked about a particular movie. Being able to mine these reviews and generate valuable meta data that describes its content provides an opportunity to understand the general sentiment around that movie in a democratized way. That’s a pretty cool thing if you think about it. Using machine learning we can democratize subjectivity about anything in the world. We can make an objective analysis of subjective content, giving us the ability to better understand trends around products and services that we can use to make better decisions as consumers."  Read on... 

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Text Mining & Graph Databases - Two Technologies that Work Well Together

Text Mining & Graph Databases - Two Technologies that Work Well Together | Dr.T | Scoop.it

Graph databases, also known as triplestores, have a very powerful capability – they can store hundreds of billions of semantic facts (triples) from any subject imaginable.  The number of free semantic facts on the market today from sources such as DbPedia, GeoNames and others is high and continues to grow every day.   Some estimates have this total between 150 and 200 billion right now.   As a result, Linked Open Data can be a good source of information with which to load your graph databases.

Linked Open Data is one source of data. When does it become really powerful?  When you create your own semantic triples from your own data and use them in conjunction with linked open data to enrich your database.  This process, commonly referred to as text mining,  extracts the salient facts from free flowing text and typically stores the results in some database.  With this done, you can analyze your enriched data, visualize it, aggregate it and report on it.  In a recent project Ontotext undertook on behalf of FIBO (Finanical Information Business Ontology), we enhanced the FIBO ontologies with Linked Open Data allowing us to query company names and stock prices at the same time to show the lowest trading prices for all public stocks in North America in the last 50 years.   To do this, we needed to combine semantic data sources,  something that’s easy to do with the Ontotext Semantic Platform.

We have found that the optimal way to apply text mining is in conjunction with a graph database.  Many of our customers use our Text Mining to do just that.

Some vendors only sell graph databases and leave it up to you to figure out how to mine the text.  Other vendors only sell the text mining part and leave it up to you to figure out where to store them.  At Ontotext, we support both along with other semantic products and services to build a complete solution.   What do we do to extract text from documents?

I’ll explain this in layman’s terms that everyone can understand.  In reviewing countless diagrams and descriptions about how text mining works, I like to boil it down to a basic 5 step process.   Text mining purists  can surely add to this discussion and we encourage you to.  At the most basic level, here’s what happens…

- See more at: http://www.ontotext.com/text-mining-graph-databases-work-well-together/#sthash.5qfN31n6.dpuf


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5 Cool PowerPoint Slide Design Tools

5 Cool PowerPoint Slide Design Tools | Dr.T | Scoop.it
Tired of the same-old presentations? These five free tools can help you add visual oomph to your PowerPoint slides.

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Cynthia Day's curator insight, July 30, 2014 2:47 PM

pp

Athena Catedral's curator insight, August 6, 2014 3:52 AM

Because a good idea will go to waste if you can't present it right. 

becool's curator insight, August 21, 2014 7:51 AM
Voor wie nog graag ppt presentaties maakt, kan dit een manier zijn om er meer variatie in te integreren. Leve Prezi :)
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4 reasons why Spark could jolt Hadoop into hyperdrive

4 reasons why Spark could jolt Hadoop into hyperdrive | Dr.T | Scoop.it
Apache Spark might push MapReduce to the back burner faster than some people might like, but it will also boost the Hadoop overall ecosystem. The project’s co-creator Matei Zaharia explains why Spark is so popular now and where it fits into the big data ecosystem.

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10 Presentation Tips in under 10 Minutes

http://fbbr.co/preshero Watch the new version of this video here: https://www.youtube.com/watch?v=N8NjKarBCVw You can radically improve your presentation ski...

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Baiba Svenca's curator insight, May 27, 2014 11:49 AM

This is a great video with an attractive speaker whose voice and countenance will appeal to anyone who wants to learn a few basic tips on how to become a successful presenter.

Appropriate to school students.

Mercedes Jahn's curator insight, May 27, 2014 6:45 PM

Great tips !!!