internet of things

Weekly Round-Up: Data Analysis Tools, M2M, Machine Learning, and Naming Babies

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from data analysis tools to naming babies. In this week's round-up:

  • Data Analysis Tools Target Non-experts
  • How M2M Data Will Dominate the Big Data Era
  • What Hackers Should Know About Machine Learning
  • Knowledge Engineering Applied to Baby Names

Data Analysis Tools Target Non-experts

Our first piece this week is an O'Reilly Strata article about some of the data analysis tools that are coming to market and are aimed at providing business users with the analytics they need to make decisions. The article highlights several tools from a variety of companies and categorizes them into three different categories according to what they help you do. The article also includes links to all the companies' websites so that, if you're anything like me, you can check out every single one of them.

How M2M Data Will Dominate the Big Data Era

The Internet of Things is getting a lot of attention these days, partly due to the amount of data that gets produced when one connected device communicates with another connected device. This is known as Machine-to-Machine data (M2M), and this Smart Data Collective article describes where a lot of this data may come from and how much data can potentially be generated.

What Hackers Should Know About Machine Learning

Our third piece is a Fast Company interview with Drew Conway, the author of the must-own book Machine Learning for Hackers. In the interview Drew answers questions about why developers should learn machine learning, the biggest knowledge gaps they need to overcome, and the differences between a machine learning project and a development project. (Editor's Note, the image to the left links to Amazon where if you buy the book we get a small cut of the proceeds. Buy enough books through this link, and we retire to an island.)

Knowledge Engineering Applied to Baby Names

Our final piece this week is a blog post about a company called Nameling is in the midst of holding a contest to improve the algorithms behind their baby name recommendation engine. Coming up with a good name for your baby is very important to parents, as the consequences of choosing a bad one almost certainly result in ridicule and tears. It should be interesting to see the results of the contest as well as what kinds of names the recommendation engine spits out.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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Weekly Round-Up: Industrial Internet, Business Culture, Visualization, and Beer Recommendations

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from the Industrial Internet to beer recommendations. In this week's round-up:

  • The Googlization Of GE
  • 10 Qualities a Data-Friendly Business Culture Needs
  • Interview with Miriah Meyer - Microsoft Faculty Fellow and Visualization Expert
  • Recommendation System in R

The Googlization Of GE

This is an interesting Forbes article about GE, the Internet of Things (which it calls the Industrial Internet), and how they are trying to be to that space what Google has become to the consumer data space.

10 Qualities a Data-Friendly Business Culture Needs

Running a data-driven organization requires not only having the right talent, tools, and infrastructure to meet the organization's objectives. It also requires a data-friendly culture, which is the premise for this article. The author identifies 10 qualities that can make for a better environment to foster innovative data-driven processes.

Interview with Miriah Meyer - Microsoft Faculty Fellow and Visualization Expert

This post is part of Jeff Leek's interview series on his Simply Stats blog. This week Jeff interviewed Miriah Meyer, who is an expert on data visualization. The interview includes questions about her work, background, influences, and advice she has for data scientists about visualization.

Recommendation System in R

This is a fun blog post about putting together a beer recommendation system using the R statistical programming language. The author walks us through the processes he followed, includes snippets of the code he used, and even shows off the resulting app where you choose a beer you like and it recommends other beers that are similar to it.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

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Weekly Round-Up: House of Cards, Machine Learning, Lying, and the Internet of Things

Welcome back to the round-up, an overview of the most interesting data science, statistics, and analytics articles of the past week. This week, we have 4 fascinating articles ranging in topics from the new Netflix series House of Cards to the Internet of Things. In this week's round-up:

  • House of Cards and Our Future of Algorithmic Programming
  • Everything You Wanted to Know About Machine Learning
  • The Future of Lying
  • Big Data and the Internet of Things

House of Cards and Our Future of Algorithmic Programming

This MIT Technology review article is about how Netflix used the data it has gathered from its 33 million users to take the guesswork out of creating its latest originally-created series, House of Cards. Some of this data includes how many movies viewers watched containing the different actors & actresses in the series, their opinions about director David Fincher's other works, and how favorably users rated similar political dramas. This could be the start of a new trend in entertainment as companies that have traditionally served as mediums delivering content to consumers delve into creating content of their very own.

Everything You Wanted to Know About Machine Learning

For those looking to get started with machine learning, BigML published a two-part series on its blog simplifying a paper recently published by University of Washington machine learning professor, Pedro Domingos. The post walks you through the basic concepts in machine learning with very intuitive language and plenty of examples to drive home the points. Part 2 of the series can be found here, and Domingos original paper can be found here.

The Future of Lying

This is an interesting Slate article by Intel futurist Brian David Johnson about how professors at Cornell are working on developing programs that can learn when people lie online. The programs use algorithms that include data such as the amount of detail people give when they describe things, the high-level reasoning being that the less detail is provided the more likely someone is lying. In the article, Johnson goes on to differentiate between different types of lies, touch on some of the implications of being able to tell a truth from a lie, and talk about its potential effect on humanity.

Big Data and the Internet of Things

This is an interesting blog post that describes some of the relationships and challenges between Big Data and the Internet of Things. Both are buzz words nowadays, but that doesn't change the fact that both will have a profound impact over the next several years as more devices start becoming smart - having sensors or RFIDs embedded - and generating useful data streams that can be used by companies to operate more efficiently than they've ever been able to operate and make more timely and better-informed decisions. The article also talks about the new systems that will be required to process all this sensor data and analyze it effectively.

That's it for this week. Make sure to come back next week when we’ll have some more interesting articles! If there's something we missed, feel free to let us know in the comments below.

Read Our Other Round-Ups