- Bayesian Basics, Explained - Dec 9, 2016.
This interview between Professor Andrew Gelman of Columbia University and marketing scientist Kevin Gray covers the basics of Bayesian statistics.
- 4 Cognitive Bias Key Points Data Scientists Need to Know - Dec 9, 2016.
Cognitive biases are inherently problematic in a variety of fields, including data science. Is this something that can be mitigated? A solid understanding of cognitive biases is the best weapon, which this overview hopes to help provide.
- Introduction to K-means Clustering: A Tutorial - Dec 9, 2016.
A beginner introduction to the widely-used K-means clustering algorithm, using a delivery fleet data example in Python.
- Chief Data & Analytics Officer Winter, Miami, January 25-26 - Dec 9, 2016.
A one and a half day exclusive event for the who’s who in the world of Analytics, we will be welcoming 100+ senior analytics leaders. Save 20% with code KDW20.
- Artificial Neural Networks (ANN) Introduction, Part 2 - Dec 9, 2016.
Matching the performance of a human brain is a difficult feat, but techniques have been developed to improve the performance of neural network algorithms, 3 of which are discussed in this post: Distortion, mini-batch gradient descent, and dropout.
- KDnuggets Top Blogs and Bloggers in November 2016 - Dec 8, 2016.
We recognize the best KDnuggets Bloggers who had the most popular blogs by views or social media shares in November 2016.
- Predictive Analytics for the Enterprise - Dec 8, 2016.
Data science success in the big data era does not start with data and software. Organizations intent upon being data-driven leaders start with immersive live training and transformative strategy.
- Partners / MGH CCDS: Data Scientist - Dec 8, 2016.
Seeking highly skilled data scientists with significant experience in machine learning and advanced statistics to apply the newest mathematical techniques to help generate value from our immense repository of health records.
- The big data ecosystem for science: Physics, LHC, and Cosmology - Dec 8, 2016.
Big Data management is essential for experimental science and technologies used in various science communities often predate those in Big Data industry and in many cases continue to develop independently. This post highlights some of these technologies, focusing on those used by several projects supported by the National Energy Research Scientific Computing Centre (NERSC).
- Artificial Neural Networks (ANN) Introduction, Part 1 - Dec 8, 2016.
This intro to ANNs will look at how we can train an algorithm to recognize images of handwritten digits. We will be using the images from the famous MNIST (Mixed National Institute of Standards and Technology) database.
- Data Science Trends To Look Out For In 2017 - Dec 8, 2016.
Machine Learning is here to stay, with more firms following Google and Facebook in the race to attract the best machine learning experts and Data Scientists. We also see a merger of IoT and Data Science. Read on for more trends.
- Navigating the World of Big Data Analytics - Dec 8, 2016.
Fulcrum Agile Analytics Lab- helps our partners test new technologies, new methodologies and new data sets quickly in an environment that can scale up and down and that meets all of their security and compliance requirements. Read to learn more and schedule a consultation.
- Top KDnuggets tweets, Nov 30 – Dec 06: A great and useful collection of minimal and clean implementations of #MachineLearning algorithms - Dec 7, 2016.
Also: #MachineLearning Yearning book draft, Free Download, by Andrew Ng; A short guide to learn #NeuralNets, and maybe get famous and rich with #DeepLearning; Free Book: Foundations of Computer Science, Aho & Ullman.
- Washington & Jefferson College: Assistant Professor of Computing and Information Studies - Dec 7, 2016.
Seeking a Data Scientist for a tenure-track Assistant Professor position. We are searching for a colleague with interdisciplinary interests in the field of computing and with a particular focus in topics of data analysis and modeling as applied across all liberal arts fields.
- The Best Metric to Measure Accuracy of Classification Models - Dec 7, 2016.
Measuring accuracy of model for a classification problem (categorical output) is complex and time consuming compared to regression problems (continuous output). Let’s understand key testing metrics with example, for a classification problem.
- What You Are Too Afraid to Ask About Artificial Intelligence (Part I): Machine Learning - Dec 7, 2016.
In the first of a 2 part series, this post will briefly discuss major advancements in pure machine learning techniques - while a followup post will similarly treat neuroscience - both with an agnostic lens.
- arXiv Paper Spotlight: Automated Inference on Criminality Using Face Images - Dec 7, 2016.
This recent paper addresses the use of still facial images in an attempt to differentiate criminals from non-criminals, doing so with the help of 4 different classifiers. Results are as promising as they are unsettling.
- R-Brain Platform for Data Science: R, Python, sharing, security, and marketplace - Dec 7, 2016.
R-Brain IDE enables data scientists to use both R and Python with full language support. It enables sharing and has a marketplace for models. Try it free.
- KDnuggets™ News 16:n43, Dec 7: Where did you use Data Science? The hard thing about Deep Learning; Big Data Main Events in 2016, Key Trends for 2017 - Dec 7, 2016.
Where did you apply Analytics, Data Science in 2016? Big Data Main Developments in 2016 and Key Trends in 2017; The Data Science Delusion; The hard thing about deep learning.
- Top November Stories: Trump, Failure of Prediction, and Lessons for Data Scientists - Dec 6, 2016.
Also: How Bayesian Inference Works; Top 20 Python Machine Learning Open Source Projects, updated; Machine Learning vs Statistics
- Internet of Things Tutorial: Introduction - Dec 6, 2016.
In the first of a series of posts, a set of Internet of Things technologies and applications are presented. Following posts will expand on these topics in tutorial form. Get an introduction to IoT here.
- Indian Institute of Science: Amazon IISc Young Scientist Big Data Fellowship - Dec 6, 2016.
The fellow should be able to lead an independent research program and will be expected to publish high-quality papers during the fellowship period. Apply by Dec 30, 2016.
- Big Data: Main Developments in 2016 and Key Trends in 2017 - Dec 6, 2016.
As 2016 comes to a close and we prepare for a new year, KDnuggets has solicited opinions from numerous Big Data experts as to the most important developments of 2016 and their 2017 key trend predictions.
- Webinar: Predictive Analytics: Failure to Launch – Dec 14 - Dec 6, 2016.
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 Dec 14.
- Join Data Leaders in London this February – KDnuggets Offer - Dec 6, 2016.
Join Europe's Data Leaders in London at Chief Data Officer Europe, 20-23 February or at Chief Analytics Officer Europe, 25-27 April. Special KDnuggets discount until 31 December 2016.
- Analytica: Informatica PowerCenter Systems Administrator - Dec 5, 2016.
Seeking an Informatica PowerCenter Systems Administrator with proven expertise and experience in managing enterprise scale Informatica architectures and environments.
- TDWI Analytics, Big Data, Data Science Training: Las Vegas, February - Dec 5, 2016.
TDWI Las Vegas, Feb 12-17 is the leading event for Analytics, Big Data, Data Management and Data Science training, bringing together the brightest minds in data to share their expertise and insights. Choose from 5 core learning tracks, TDWI Leadership Summit, or Data Science Bootcamp.
- Free ebooks: Machine Learning with Python and Practical Data Analysis - Dec 5, 2016.
Two free ebooks: "Building Machine Learning Systems with Python" and "Practical Data Analysis" will give your skills a boost and make a great start in the New Year.
- Kobielus Predictions for Data Science in 2017 - Dec 5, 2016.
IBM Data Evangelist James Kobielus predictions for 2017, including key role of data scientists in survival of their companies. Join industry experts for a live #MakeDataSimple Crowdchat on Thursday December 8 at 1:00pm EST.
- CDO: to stay or to go? - Dec 5, 2016.
The Chief Digital Officer role has grown 1000-fold in the last 9 years, but will it remain popular in 2025? We examine the parallels between the electric and digital revolutions.
- Top Stories, Nov 28-Dec 4: Machine Learning vs Statistics; Hard Thing About Deep Learning; Developers’ Machine Learning Intro - Dec 5, 2016.
Machine Learning vs Statistics; The hard thing about deep learning; Top 20 Python Machine Learning Open Source Projects, updated; Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed; Introduction to Machine Learning for Developers
- Why Deep Learning is Radically Different From Machine Learning - Dec 5, 2016.
There is a lot of confusion these days about Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), yet the distinction is very clear to practitioners in these fields. Are you able to articulate the difference?
- Top /r/MachineLearning Posts, November: StarCraft II for AI Research; Google AI Experiments Website; Google in Montreal - Dec 5, 2016.
DeepMind and Blizzard to release StarCraft II as an AI research environment; Google AI Experiments Website; Google opens new Montreal-based AI research lab; Lip Reading Sentences in the Wild; Clean implementations of machine learning algorithms
- How To Make Your Mark As A Woman In Big Data - Dec 3, 2016.
Despite the shift in big data technology innovation that is driving tremendous growth and opportunities, women still play a small role in this arena. Here are 5 thoughts for women considering a career in big data.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in November - Dec 2, 2016.
Faculty/Postdoc positions in Data Science/Machine Learning at DePaul, UCSB, Virginia Tech, Barcelona U, Aarhus U, Georgia State U, U. of Innsbruck, Drexel, CMU, Oregon State U, Iowa State, and more.
- Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science: December 2016 and Beyond - Dec 2, 2016.
Coming soon: IEEE Big Data, Big Data/Business Analytics Innovation Summits Las Vegas, AnacondaCON Austin, WSDM 2017 Cambridge, TDWI Las Vegas, and more.
- Interviews with Data Scientists: Claudia Perlich - Dec 2, 2016.
In this wide-ranging interview, Roberto Zicari talks to a leading Data Scientist Claudia Perlich about what they must know about Machine Learning and evaluation, domain knowledge, data blending, and more.
- Smart Data Platform – The Future of Big Data Technology - Dec 2, 2016.
Data processing and analytical modelling are major bottlenecks in today’s big data world, due to need of human intelligence to decide relationships between data, required data engineering tasks, analytical models and it’s parameters. This article talks about Smart Data Platform to help to solve such problems.
- Random Forests in Python - Dec 2, 2016.
Random forest is a highly versatile machine learning method with numerous applications ranging from marketing to healthcare and insurance. This is a post about random forests using Python.
- Why the Pros Go To Predictive Analytics World - Dec 2, 2016.
Predictive Analytics World for Business is May 14-18, 2017. Save big on full conference passes when you register today with super early bird rates, plus save an extra $150 when you use code KDN150 at checkout!
- KSF Media: Data Scientists, Full Stack Engineers - Dec 1, 2016.
KSF Media, a publisher of newspapers with titles that have been around for more than 150 years, is seeking Data Scientists and Full Stack Engineers in Helsinki Finland.
- The hard thing about deep learning - Dec 1, 2016.
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks.
- Data Science Deployments With Docker - Dec 1, 2016.
With the recent release of NVIDIA’s nvidia-docker tool, accessing GPUs from within Docker is a breeze. In this tutorial we’ll walk you through setting up nvidia-docker so you too can deploy machine learning models with ease.
- Online Master of Science in Predictive Analytics. - Dec 1, 2016.
Build hot skills for the growing analytics field, learn key statistical concepts and practical applications from distinguished Northwestern faculty and industry experts and prepare for leadership level career. Spring application deadline Jan 15.
- Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.
- IDentrix: Data Scientist - Nov 30, 2016.
Seeking a passionate Data Scientist with a proven record of building data driven solutions, who is interested in data mining and modeling specialized large and connected datasets.
- Top KDnuggets tweets, Nov 23-29: The Entire #Python Language in a Single Image ; Great list of Data Science, Machine Learning, AI Resources - Nov 30, 2016.
The Entire #Python Language in a Single Image; Cartoon: Thanksgiving, #BigData, and Turkey #DataScience; 50% of Data Scientists have under 10 GB databases, not #BigData; Machine Learning Algorithms: A Concise Technical Overview
- Measuring Topic Interpretability with Crowdsourcing - Nov 30, 2016.
Topic modelling is an important statistical modelling technique to discover abstract topics in collection of documents. This article talks about a new measure for assessing the semantic properties of statistical topics and how to use it.
- The Data Science Delusion - Nov 30, 2016.
Gleanings from observed technical misunderstandings between business leaders and data scientists (and among data scientists themselves) so dramatic that one could start wondering whether there is something wrong with data science as it is being practiced.
- Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition - Nov 30, 2016.
Given the ongoing explosion in interest for all things Data Science, Artificial Intelligence, Machine Learning, etc., we have updated our Amazon top books lists from last year. Here are the 10 most popular titles in the AI & Machine Learning category.
- KDnuggets™ News 16:n42, Nov 30: Python Machine Learning Open Source Projects; Facebook Groups for Big Data & Data Science - Nov 30, 2016.
Python Machine Learning Open Source Projects; Facebook Groups for Big Data & Data Science; Combining Different Methods to Create Advanced Time Series Prediction; Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed; Continuous improvement for IoT through AI / Continuous learning
- Industries / Fields where you applied Analytics, Data Mining, Data Science in 2016? - Nov 29, 2016.
New KDnuggets Poll is asking: What are the Industries/Fields where you applied Analytics, Data Science, Data Mining in 2016? Please vote and we will publish the analysis and trends.
- Predictive Analytics.
Max Results. Min Time. - Nov 29, 2016.
Successful analytics in the big data era does not start with data and software, but with immersive hands-on training and goal-driven strategy. Get this training with TMA courseware, which spans all skill levels and analytic team roles. Live Online in January or in Wash-DC in April.
- 10 Tips to Improve your Data Science Interview - Nov 29, 2016.
Interviewing is a skill. Here are 10 tips and resources to improve your Data Science interviews.
- Ethical Implications Of Industrialized Analytics - Nov 29, 2016.
Analytics & Big Data will be involved in every aspect of our lives and we should handle the ethical dilemmas wisely to let innovation contribute more to our lives.
- Machine Learning vs Statistics - Nov 29, 2016.
Machine learning is all about predictions, supervised learning, and unsupervised learning, while statistics is about sample, population, and hypotheses. But are they actually that different?
- Top Stories, Nov 21-27: Top 20 Python Machine Learning Open Source Projects; Continuous Improvement for IoT - Nov 28, 2016.
Top 20 Python Machine Learning Open Source Projects, updated; Continuous improvement for IoT through AI; Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning; Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview
- RCloud – DevOps for Data Science - Nov 28, 2016.
After almost two decades of software development, term – DevOps was coined and officially given importance to collaboration between development and deployment of software systems. In this early stage of Data Science field, use of standardized and empirical practises like DevOps will definitely speed up its evolution.
- Introduction to Machine Learning for Developers - Nov 28, 2016.
Whether you are integrating a recommendation system into your app or building a chat bot, this guide will help you get started in understanding the basics of machine learning.
- Webinar: Predictive Analytics: Failure to Launch [Dec 14] - Nov 28, 2016.
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 Dec 14.
- arXiv Paper Spotlight: Stealing Machine Learning Models via Prediction APIs - Nov 28, 2016.
Despite their confidentiality, machine learning models which have public-facing APIs are vulnerable to model extraction attacks, which attempt to "steal the ingredients" and duplicate functionality. The paper at hand investigates.
- Cyber Monday Sale: Save on TDWI Austin, Las Vegas, and more - Nov 28, 2016.
TDWI Conferences are world leading training events for analytics and Big Data, with industry experts sharing their knowledge and experiences in half/full-day sessions on skills you need today. Here are 2 ways to save this Cyber Monday.
- Why We Need Data Science - Nov 26, 2016.
A gentle reminder as to why we need Data Science, reasons for which even you may have been guilty of offending at some point. A basic topic, to be sure, making it all the more important.
- Tips for Beginner Machine Learning/Data Scientists Feeling Overwhelmed - Nov 25, 2016.
Sebastian Raschka weighs in on how to battle stress as a beginner in the data science world. His insight is to-the-point, so reading it should be a stress-free endeavour.
- Continuous improvement for IoT through AI / Continuous learning - Nov 25, 2016.
In reality, especially for IoT, it is not like once an analytics model is built, it will give the results with same accuracy till the end of time. Data pattern changes over the time which makes it absolutely important to learn from new data and improve/recalibrate the models to get correct result. Below article explain this phenomenon of continuous improvement in analytics for IoT.
- Deep Learning Research Review: Reinforcement Learning - Nov 25, 2016.
This edition of Deep Learning Research Review explains recent research papers in Reinforcement Learning (RL). If you don't have the time to read the top papers yourself, or need an overview of RL in general, this post has you covered.
- Neighbors Know Best: (Re) Classifying an Underappreciated Beer - Nov 24, 2016.
A look at beer features to determine whether a specific brew might be better served (pun intended) by being classified under a different style. kNN analysis supported with in-post plots and linked iPython notebook.
- Linear Regression, Least Squares & Matrix Multiplication: A Concise Technical Overview - Nov 24, 2016.
Linear regression is a simple algebraic tool which attempts to find the “best” line fitting 2 or more attributes. Read here to discover the relationship between linear regression, the least squares method, and matrix multiplication.
- Top KDnuggets tweets, Nov 16-22: Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning - Nov 23, 2016.
Top 20 #Python #MachineLearning #OpenSource Projects; Shortcomings of #DeepLearning; What is the Difference Between #DeepLearning and Regular #MachineLearning?; Questions To Ask When Moving #MachineLearning From Practice to Production; How to Choose the Right #Database System
- Vencore: Software Engineer, Machine Learning - Nov 23, 2016.
Seeking a Software Developer/Engineer professional with an experienced background in software/algorithm development and integration. This opportunity requires the ability to develop scalable and maintainable software solutions for our healthcare predictive analytics platform.
- Analytics help app developer conceive of new possibilities - Nov 23, 2016.
By now, we all have realised the power of IoT, Mobile Apps, Big Data and Analytics. Now it’s time to use this power in every possible way for complete well being of everyone in the world. Let’s read this interesting article on Women Health Care Mobile Apps and Data Analytics.
- Top 10 Facebook Groups for Big Data, Data Science, and Machine Learning - Nov 23, 2016.
Social media now not only shares friendship connections or photos of “selfies” but also spreads from political media to science information. Social network members are tending to more eagerly learn about big data, data science and machine learning through groups. We review the ten largest Facebook groups in this area.
- Cartoon: Thanksgiving, Big Data, and Turkey Data Science. - Nov 23, 2016.
We revisit KDnuggets Thanksgiving cartoon, which examines the predicament of one group of fowl Data Scientists.
- Top Data Scientist Daniel Tunkelang on Data Recycling - Nov 22, 2016.
Respected Data Scientist Daniel Tunkelang shares some insight into data recycling, using data from other contexts to bootstrap your initial statistical models until you can collect live data.
- DePaul University: Assistant Professor in Data Science - Nov 22, 2016.
Seeking an Assistant Professor in Data Science to be part of one of the fastest growing and most highly recognized data science programs in the country.
- Predictive Science vs Data Science - Nov 22, 2016.
Is Predictive Science accurately represented by the term Data Science? As a matter of fact, are any of Data Science's constituent sciences well-represented by the umbrella term? This post discusses a few of these points at a high level.
- How to Make Your Database 200x Faster Without Having to Pay More - Nov 22, 2016.
Waiting long for a BI query to execute? I know it’s annoyingly frustrating… It’s a major bottle neck in day-to-day life of a Data Analyst or BI expert. Let’s learn some of the easy to use solutions and a very good explanation of why to use them, along with other advanced technological solutions.
- iSight Cloud – Lightning fast visualizations on large data sets - Nov 22, 2016.
SnappyData is launching a FREE cloud service called iSight-Cloud so anyone can try our engine and provide us some feedback. You can try our simple demos in a visual environment or even bring your own data sets to try.
- Beyond the Dashboard, MS in Applied Data Science - Nov 22, 2016.
Bay Path University’s MS in Applied Data Science will teach the fundamental principles, platforms, and tool-sets of the data science profession.
- The Experience of Being a High-Performing Data Scientist - Nov 21, 2016.
Now in open beta, IBM Data Science Experience (DSX) delivers Machine Learning, Collaboration, and Creative capabilities in an open and integrated environment for team data science, including many productivity features for next-generation data science,
- Top Stories, Nov 14-20: How Bayesian Inference Works; Data Science and Big Data, Explained; Advanced Time Series Prediction - Nov 21, 2016.
How Bayesian Inference Works; Data Science and Big Data, Explained; Trump, Failure of Prediction, and Lessons for Data Scientist; Combining Different Methods to Create Advanced Time Series Prediction; Questions To Ask When Moving Machine Learning From Practice to Production
- Top 20 Python Machine Learning Open Source Projects, updated - Nov 21, 2016.
Open Source is the heart of innovation and rapid evolution of technologies, these days. This article presents you Top 20 Python Machine Learning Open Source Projects of 2016 along with very interesting insights and trends found during the analysis.
- Implementing a CNN for Human Activity Recognition in Tensorflow - Nov 21, 2016.
In this post, we will see how to employ Convolutional Neural Network (CNN) for HAR, that will learn complex features automatically from the raw accelerometer signal to differentiate between different activities of daily life.
- Data Avengers… Assemble! - Nov 19, 2016.
The Avengers are perfectly capable of defending the Earth from our worst enemies. But are they up to the task of taking care of our data? Read this terribly punny "opinion" piece to find out.
- NEW: 6 Hot Career Prospects in Data Science Industry Today - Nov 18, 2016.
You read that Data Scientist is “The Sexiest Job of The 21st Century”, but there are other jobs profiles and opportunities in Data Science – read about these roles, responsibilities, skills, salary prospects and market demand (also pretty sexy!).
- Questions To Ask When Moving Machine Learning From Practice to Production - Nov 18, 2016.
An overview of applying machine learning techniques to solve problems in production. This articles covers some of the varied questions to ponder when incorporating machine learning into teams and processes.
- Process Mining: Where Data Science and Process Science Meet - Nov 17, 2016.
A data scientist without Process Mining training is ill-equipped to uncover the organization’s real processes, analyze compliance, diagnose bottlenecks and improve processes, so improve your skills with a new version of the free Coursera course "Process Mining: Data Science in Action" will start on November 28, 2016.
- KDD 2016: Watch Talks by Top Data Science Researchers - Nov 17, 2016.
Watch the innovative talks and researches from top researchers in Data Science, presented at KDD 2016, San Francisco conference.
- Deep Learning Reading Group: Skip-Thought Vectors - Nov 17, 2016.
Skip-thought vectors take inspiration from Word2Vec skip-gram and attempt to extend it to sentences, and are created using an encoder-decoder model. Read on for an overview of the paper.
- Why Fluid Intelligence Makes You a Better Data Scientist - Nov 17, 2016.
How do you harness the power of insight on a regular basis? Check out these tips for increasing your fluid intelligence to do so, courtesy of Saint Mary's College.