KDnuggets™ News 17:n02, Jan 18: Most Popular Language For Machine Learning; Analytics & Data Science Make Business Smarter

[unable to retrieve full-text content]The Most Popular Language For Machine Learning and Data Science; Analytics & Data Science Make Business Smarter; Exclusive: Interview with Jeremy Howard on Deep Learning, Kaggle, Data Science; 90 Active Blogs on Analytics, Big Data, Data Mining, and Data Science
Original Post: KDnuggets™ News 17:n02, Jan 18: Most Popular Language For Machine Learning; Analytics & Data Science Make Business Smarter

Data Scientist New Year Resolutions for 2017

[unable to retrieve full-text content]Do you make any new year resolutions? Hit the gym more often? Lose that last 10 pounds? While personal resolutions often get a bad rap, setting professional goals at the start of the new year is not necessarily a bad idea. Check out one data scientist’s new year resolutions for 2017.
Original Post: Data Scientist New Year Resolutions for 2017

90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated)

[unable to retrieve full-text content]Stay up-to-date in the data science with active blogs. This is a list of 90 recently active blogs on Big Data, Data Science, Data Mining, Machine Learning, and Artificial intelligence (newly checked at 01-2017).
Original Post: 90 Active Blogs on Analytics, Big Data, Data Mining, Data Science, Machine Learning (updated)

The fivethirtyeight R package

Andrew Flowers, quantitiative editor of FiveThirtyEight.com, announced at last weeks’ RStudio conference the availability of a new R package containing data and analyses from some of their data journalism features: the fivethirtyeight package. (Andrew’s talk isn’t yet online, but you can see him discuss several of these stories in his UseR!2016 presentation.) While not an official product of the FiveThirtyEight editorial team, it was developed by Albert Y. Kim, Chester Ismay and Jennifer Chunn under their guidance. Their motivation for producing the package was to provide a resource for teaching data science: We are involved in statistics and data science education, in particular at the introductory undergraduate level. As such, we are always looking for data sets that balance being Rich enough to answer meaningful questions with, real enough to ensure that there is context, and realistic enough to convey to…
Original Post: The fivethirtyeight R package

Big Data and the Internet of Things don’t make business smarter, Analytics and Data Science do

[unable to retrieve full-text content]Big Data does not convert data into actionable information. Big Data does not create value. But Data Science does, and it does not have to be complex or expensive, or even big.
Original Post: Big Data and the Internet of Things don’t make business smarter, Analytics and Data Science do

The Most Popular Language For Machine Learning and Data Science Is …

By Jean-Francois Puget, IBM. What programming language should one learn to get a machine learning or data science job?  That’s the silver bullet question.  It is debated in many forums.  I could provide here my own answer to it and explain why, but I’d rather look at some data first.  After all, this is what machine learners and data scientists should do: look at data, not opinions. So, let’s look at some data.  I will use the trend search available on indeed.com.  It looks for occurrences over time of selected terms in job offers.  It gives an indication of what skills employers are seeking.  Note however that it is not a poll on which skills are effectively in use.  It is rather an advanced indicator of how skill popularity evolve (more formally, it is probably close to the first order…
Original Post: The Most Popular Language For Machine Learning and Data Science Is …

A Non-comprehensive List of Awesome Things Other People Did in 2016

Editor’s note: For the last few years I have made a list of awesome things that other people did (2015, 2014, 2013). Like in previous years I’m making a list, again right off the top of my head. If you know of some, you should make your own list or add it to the comments! I have also avoided talking about stuff I worked on or that people here at Hopkins are doing because this post is supposed to be about other people’s awesome stuff. I write this post because a blog often feels like a place to complain, but we started Simply Stats as a place to be pumped up about the stuff people were doing with data. Thomas Lin Pedersen created the tweenr package for interpolating graphs in animations. Check out this awesome logo he made with it.…
Original Post: A Non-comprehensive List of Awesome Things Other People Did in 2016

AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017

At KDnuggets, we try to keep our finger on the pulse of main events and developments in industry, academia, and technology. We also do our best to look forward to key trends on the horizon. Over the past few weeks, we published a series of posts outlining expert opinions in data science, machine learning, artificial intelligence, and related fields. See these previous iterations below: In an encore post of this series, we bring you the collected responses to an amalgam question — including experts from all of the previous posts’ fields — while adding a second dimension this time around. 1. “What were the main developments in AI, Data Science, Machine Learning in 2016 and what Key Trends do you expect in 2017?” 2. “How can we get more women involved in this field?” Without further delay, here is what…
Original Post: AI, Data Science, Machine Learning: Main Developments in 2016, Key Trends in 2017

What can we learn from StackOverflow data?

StackOverflow, the popular Q&A site for programmers, provides useful information to nearly 5 million programmers worldwide with its database of questions and answers — not to mention the additional comments that other programmers provide. (You might be interested in the architecture, based SQL Server 2016, required to deliver the 8.5 billion pages Stack Overflow served last year.) Since its inception, StackOverflow has has a policy of sharing all of this content under a Creative Commons license. This represents a rich trove of unstructured data for analysis, especially given that the database of 13 million questions, 21 million answers and 54 million comments (and growing) is easily accessible via StackExchange Data Explore, Kaggle and Google BigQuery. Various data scientists have investigated this database, and learned some interesting things about programmers in the process. Here are a few examples, with links to the complete reports. Sara…
Original Post: What can we learn from StackOverflow data?

A Tasty approach to data science

By Natalia Hernandez, Foodpairing. When brands are ready to create a new flavor, there is always an ideation phase where developers ask themselves “what can we create next?” At Foodpairing, we help brands answer that question through a hybrid method using consumer behavior data and scientific analysis. Of course, when companies try to answer this question themselves, they begin spending too much time in the fuzzy front end (this is the time between idea conception of a new product flavor and the beginning of its development). At this stage in flavor line development surveys are ineffective and expensive and trend reports are too general to provide a business with actionable insights. Companies are left to rely on intuition – a costly risk. We named our approach to building new flavor lines Consumer Flavor Intelligence (CFI), which shortens the fuzzy front…
Original Post: A Tasty approach to data science