The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.
Learn how to set up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. This on-demand webinar discusses popular collection mechanisms, does a hands-on log-parsing example in Spark, and shows how to use Looker to get insights from event data.
An overview of Intel's recent investments in cognitive technology, the impact of these investments on technology and research, and the new opportunities these investments present.
Advice to schools and universities to help them prepare the future managers of the data-driven business world. One key step is to get managers acquainted with data by touching data, manipulating it, and 'playing' with it.
The issue of designing new interactive interfaces with machine learning systems that best serve our needs and help us build and maintain trust is a central issue in AI. Read one researcher's take on this topic.
Machine Learning Key Terms, Explained; 10 Must Have Data Science Skills, Updated; A Concise Overview of Standard Model-fitting Methods; Free eBook: Healthcare Social Media Analytics and Marketing; 7 Steps to Mastering Machine Learning With Python
A very concise overview of 4 standard model-fitting methods, focusing on their differences: closed-form equations, gradient descent, stochastic gradient descent, and mini-batch learning.
The CRN editorial team has released its annual Big Data 100 report for 2016, which includes the 55 Big Data Startups to Watch in 2016. Get the info here.
This is the second post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Read on for a great overview of practicing data science.
Data scientists are in high demand to help companies gain better market information so they can make more informed decisions, and ultimately improve ROI. St. Mary's College Master of Science in Data Science can help!
This post will provide a quick overview the current state of Data Scientist salaries in the US, and performs some data analysis in concert with some additional data.
This post explores the intricate relationship between customers, trust, and analytics in the banking sector, and offer actions that banks may need to take to assess the way they assure trust across the analytics lifecycle.
I talk with Sven Bauszus, SAP Predictive Analytics global leader, about their main products in Business and Predictive Analytics and Big Data space, analytics maturity by industry, the automation of Data Science, and "citizen" Data Scientists.
Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.
Stanford Crowd Course Initiative: #MachineLearning with #Python course; Practical Guide to Matrix Calculus for #DeepLearning; Build your own #DeepLearning Box < $1.5K
Whether you’re an Apache Spark newbie or a hardcore enthusiast, Spark Summit, June 6-8 in San Francisco, is the place to be to gain new insights and make valuable connections. Use promo code KDNuggets to save 15%
This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.
Is the interval scale assumption of your data justified? Research suggests that it may not be, and that applying scale transformations often improves performance.
Annual KDnuggets Analytics Software Poll; How to Explain Machine Learning to a Software Engineer; 5 Machine Learning Projects You Can No Longer Overlook; Doing Data Science: A Kaggle Walkthrough Part 1 – Introduction
Burtch Works shares the annual update to their highly regarded data science salary report series. This year's report details the compensation and demographic data on 374 data scientists.
An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.
On June 6, IBM will share important announcements for making R, Spark, and open data science a sustainable business reality at the Apache Spark Maker Community Event in San Francisco, Attend in person or watch live.
without the AoT, it is difficult to realize the full potential of the IoT. We review the promise and challenges of Analytics of Things, including data, security, analytics implementation, standartization, and more.
This is the first post in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. Very thorough, and very insightful.
At Chicago's Predictive Analytics World for Business conference, June 20-23, 2016, explore case studies from a range of industries and discuss best practices for infusing organizations with the power of analytics in new and innovative ways. KDnuggets subscribers enjoy $150 off!
We all know the big machine learning projects out there: Scikit-learn, TensorFlow, Theano, etc. But what about the smaller niche projects that are actively developed, providing useful services to users? Here are 5 such projects.
Vote: What software you used for Analytics, Data Mining, Data Science projects? Useful #Cheatsheet: #Python, R #rstats code for #MachineLearning Algorithms; TPOT: A #Python Tool for Automating Data Science; Randomize Acceptance of Borderline Research Papers, save 25 reviewer person-years.
Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.
A fantastic overview of several now-classic papers on word2vec, the work of Mikolov et al. at Google on efficient vector representations of words, and what you can do with them.
Two intense days, buzzing with energy, knowledge exchange, panel discussions, in short London Data Festival was a great place to be if you are a data scientist. Here is summary of speakers and major attractions of the event.
Check hot topics (including uplift modeling) covered at Predictive Analytics World in Chicago, June 20-23, and register today with code KDN150 for $150 off.
In this whitepaper, you'll learn from our seasoned experts about the approaches to scaling your data science models, review the various options, and learn how to easily accomplish them using Anaconda, the leading Open Data Science platform powered by Python.
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
Data scientists mostly just do arithmetic and that’s a good thing; Data Scientists – future-proof yourselves; Are Deep Neural Networks Creative?; Implementing Neural Networks in Javascript; 7 Steps to Mastering Machine Learning With Python
Ontotext offers a pair of free live webinars: Diving in Panama Papers and Open Data to Discover Emerging News, and GraphDB Fundamentals: Transforming your Graph Analytics with GraphDB. Reserve your spot today.
Trying to put together your first resume or two after graduation can be tricky. Without a lot of relevant work experience to highlight, sometimes none at all, graduates often wonder how they can adequately impress hiring managers with their analytics capabilities.
Vote in KDnuggets 17th Annual Poll: What software you used for Analytics, Data Mining, Data Science Machine Learning projects in the past 12 months? We will clean and analyze the results and publish our analysis afterwards.
MLconf in Seattle is a week away and we are getting a glimpse. Ethics in machine learning is the hottest conversation right now. Hear how a quantum molecular dynamic model made Uber service more reliable, get practical advice on next revolution in text search, and learn about multi-classification evaluation and ensemble learning.
The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.
TPOT is an open-source Python data science automation tool, which operates by optimizing a series of feature preprocessors and models, in order to maximize cross-validation accuracy on data sets.
Bing Predicts is an innovative feature which now regularly makes headlines for its ability to analyze massive amounts of Web activity to forecast the outcomes of elections, voting-based reality TV shows, sports matchups and more.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
The 3 main ingredients to creating artificial intelligence are hardware, software, and data, and while we have focused historically on improving software and data, what if, instead, the hardware was drastically changed?
Javascript is one of the most prevalent and fastest growing languages in existence today. Get a quick introduction to implementing neural networks in the language, and direction on where to go from here.
Read a first-hand perspective on Big Data playing field in Singapore, strong support for Machine Learning and Data Science research, excellent local conditions, and how all these contribute to a bigger aspiration this city state is striving towards.
Understanding the Bias-Variance Tradeoff; Python, MachineLearning, & Dueling Languages; Why AI development is going to get even faster; Why Implement #MachineLearning Algorithms From Scratch?
Check the keynote presentations on Weird Science, Persuasion Modeling in Presidential Campaigns, and Rethinking Analytics in Service-oriented world. Register now with code KDN150 to get double discount.
In our Marketing Analytics & Data Science interview series, we are catching up with thought leaders across industries to hear their take on trends and challenges in Big Data ahead of MARKETING ANALYTICS & DATA SCIENCE CONFERENCE, June 8-10, San Francisco. Save 20% with Code MADS16KDN
Are you also wondering how you can get started as data scientist, and become a valuable team player. Understand what really matters as data scientist, and things to focus in the initial stages.
HPE Haven OnDemand is a diverse collection of APIs for interacting with data designed with flexibility in mind, allowing developers to quickly perform data tasks in the cloud. See why it is a simple path to reason and insight for data science and cognitive computing.
The CDAO Forum in Melbourne, 5-7 Sep 2016 will bring over 150 leading data and analytics executives from the region to share their insights on how to develop the infrastructure, ecosystem, buy-in, and culture to use data to drive business advantage. Get KDnuggets discount with code CDAOMKDN.
Chatbots can have extensive applications, now that Facebook is considering to implement AI in their Messenger and WhatsApp platforms. We examine 3 main factors that will determine the success of chatbots.
UC Analytics Summit is a day-long immersion into the practice of applying analytic methods to solve real-world problems. Hear seasoned practitioners and thought leaders from 17 of the top companies and organizations.
Look at Data scientist "definitions" with a wry smile: the "essential" skills very much reflect those that a short time ago were quite novel, and are being used in applications to problems that have recently become solvable.
How to Use Cohort Analysis to Improve Customer Retention; Why Implement Machine Learning Algorithms From Scratch?; 7 Steps to Mastering Machine Learning With Python; R vs Python for Data Science: The Winner is ...
Logit has reduced the tuition for its first immersive Data Science program in Southern California by over 66%, thanks to the grant it received. Learn more and apply for June cohort at www.logitdatascience.com.
In this post, we present a list of popular data science leaders on LinkedIn. Follow these leaders who will keep you in touch with the latest Data Science happenings!
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
Learn how to transform your products at Data-Driven Product Innovation Summit, and about Big Data risks and solutions at Data security summit. Use code KD10 to get an extra 10% off.
Corporate recruiters spend an average of 6 seconds on every resume. Predictive screening algorithms can help identify good candidates, and help recruiters to do a better job.
What makes “googling” the best way to search for something on the Internet? Join Rajan Patel, Stanford Instructor and Google Engineer, as he shares insights into the manipulation of large complex data sets and how you can turn them into valuable, actionable information for your company.
Learn how to solve more scientific, engineering and business problems correctly and faster by extracting powerful insights from existing data using proven, simple statistical modeling methods. Watch the webcast.
Top 10 Essential Books for the Data Enthusiast; 10 Signs Of A Bad Data Scientist; When Does Deep Learning Work Better Than SVMs or Random Forests?; Comprehensive Guide to Learning Python for Data Science and more.
Apache Spark is one of “the hottest technology” for data science and analytics. A project called Tungsten represents a huge leap forward for Spark, particularly in the area of performance. Understand how it works, and why it improves Spark performance so much.
Got hired as data scientist, where to go now from here? Understand how you can make the most of your career by following the different paths like managerial, consulting, or as a domain expert.
The simplest motivation for quantization is to shrink neural network representation by storing the min and max for each layer. Learn more how to perform quantization for deep neural networks.
Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.
Trifecta: #Python, #MachineLearning, + Dueling Languages; Cartoon: When #Automation Goes Too Far; #AI Speed: 2-year old #xkcd cartoon: cannot check if a photo has a bird; Removing Duplicates in #BigData.
Learn how to get started with predictive modeling and overcome strategic and tactical limitations that cause data mining projects to fall short of their potential. Next webinar is May 10.
Coming soon: TDWI Chicago, Apache Big Data Vancouver, Deep Learning Summit Boston, Data by the Bay Oakland, Biz Analytics Summit Chicago, KxCon Montauk, Open Data Science Boston, In-Memory Computing San Francisco, and many more.
The Penn State World Campus online MS in Data Analytics/Business Analytics focuses on exploring and analyzing large data sets to support data-driven business decisions. Complete and submit your application by July 1 to start taking classes in August.
Join 100+ CAOs, CDOs, and other leading data and analytics professionals who share their insights on developing the infrastructure, ecosystem, culture, and strategies to turn data into a strategic asset.
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
This conference is the only industry-wide event of its kind, tailored to in-memory computing related technologies and solutions. The IMC Summit brings together in-memory computing visionaries, decision makers, experts and developers for the purpose of education, discussion and networking. Get 20% off with code KDN20.
Check out the most popular topics on Reddit's Machine Learning subreddit from April, including TensorFlow, deep learning, tutorials, self-reflection, and free books.
Cohort analysis is a subset of behavioral analytics that takes the user data and breaks them into related groups for analysis. Let’s understand using cohort analysis with an example of daily cohort of app users.
Academic/Research positions Analytics and Data Science in Zurich, Hatfield-UK, Paris, Ningbo-China, Tianjin-China, Melbourne, Buffalo-NY, Birmingham-UK, and Tampere-Finland.
7 Steps to Mastering Machine Learning With Python; When Does Deep Learning Work Better Than SVMs or Random Forests; How to Remove Duplicates in Large Datasets; The "Thinking" Part of "Thinking Like A Data Scientist".
Poll: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months?
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