Jul
2016 Aug
All (123) | Courses, Education (11) | Meetings (15) | News, Features (23) | Opinions, Interviews (27) | Software (8) | Tutorials, Overviews (32) | Webcasts & Webinars (7)
- Top KDnuggets tweets, Aug 24-30: #DataScientist – sexiest job of the 21st century until …; Activation Function in #NeuralNetworks.
- Aug 31, 2016.
Cartoon: #DataScientist - sexiest job of the 21st century until ...; What is the Role of the Activation Function in Neural Networks?; LinkedIn Machine Learning team tutorial on building #Recommender system; Create a #Chatbot for #Telegram in #Python to Summarize Text.
- Learning from Imbalanced Classes
- Aug 31, 2016.
Imbalanced classes can cause trouble for classification. Not all hope is lost, however. Check out this article for methods in which to deal with such a situation.
- How Convolutional Neural Networks Work
- Aug 31, 2016.
Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.
- What is the Role of the Activation Function in a Neural Network?
- Aug 30, 2016.
Confused as to exactly what the activation function in a neural network does? Read this overview, and check out the handy cheat sheet at the end.
- How to Become a Data Scientist – Part 2
- Aug 30, 2016.
Check out part 2 of this excellent series of articles on becoming a data scientist, written by someone who spends their day recruiting data scientists. This installation focuses on learning.
- Whitepaper: Big Data Visualization With Datashader
- Aug 30, 2016.
Download this free whitepaper on how datashader helps tame the complexity of visualizing large amounts of data, along with examples for accomplishing this.
- PAPIs 16 Conference on Predictive Applications & APIs, Oct 10-12, Boston
- Aug 30, 2016.
PAPIs is the premier forum for the presentation of new machine learning APIs, techniques, architectures and tools to build intelligent applications. It also hosts the world’s 1st startup competition where the jury is an AI.
- New Book: TensorFlow for Machine Intelligence, KDnuggets Offer
- Aug 30, 2016.
TensorFlow for Machine Intelligence is a hands-on introduction to learning algorithms and the "TensorFlow book for humans." KDnuggets readers get a 25% discount, available here.
- Top Stories, Aug 22-28: How to Become a Data Scientist; 10 Need to Know Machine Learning Algorithms
- Aug 29, 2016.
How to Become a Data Scientist; 10 Need to Know Machine Learning Algorithms; How to Become a (Type A) Data Scientist; Cartoon: Data Scientist: The sexiest job of the 21st century until...
- Up to Speed on Deep Learning: July Update
- Aug 29, 2016.
Check out this thorough roundup of deep learning stories that made news in July. See if there are any items of note you missed.
- Data Mining Tip: How to Use High-cardinality Attributes in a Predictive Model
- Aug 29, 2016.
High-cardinality nominal attributes can pose an issue for inclusion in predictive models. There exist a few ways to accomplish this, however, which are put forward here.
- KNIME Summit, San Francisco, Sep 14-16, KDnuggets Offer
- Aug 29, 2016.
KNIME, a leading open Analytics Platform is holding a first North American summit in San Francisco. Use code SUMMIT_KDNUGGETS to get 10% discount. Early bird rates till Sep 4.
- PAW Healthcare: Improve patient care with predictive analytics, Oct 23-27, New York
- Aug 29, 2016.
Predictive analytics can help medical professionals reduce costs, improve outcomes, an increase patient satisfaction. Learn from keynotes, dozens of sessions and workshops how to apply these lessons to your own organization. Use code KDN150 to save.
- Rio Olympics 2016 on Twitter: Positive Sentiment (75%), Water Sports, Simone Biles Win
- Aug 27, 2016.
Who were the most talked about athletes in the 2016 Rio Olympic Games? Which sport was most cited by users? What was the overall sentiment? This analysis by Expert System provides the detailed answers.
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Cartoon: Data Scientist – the sexiest job of the 21st century until …
- Aug 27, 2016.
This Data Scientist thought that he had the sexiest job of the 21st century until the arrival of the competition ... - MDL Clustering: Unsupervised Attribute Ranking, Discretization, and Clustering
- Aug 26, 2016.
MDL Clustering is a free software suite for unsupervised attribute ranking, discretization, and clustering based on the Minimum Description Length principle and built on the Weka Data Mining platform.
- How Can Data Scientists Mitigate Sensitive Data Exposure Vulnerability?
- Aug 26, 2016.
What is sensitive data? How does it affect data science, and what can be done to mitigate data exposure vulnerability? Read on to find out.
- New Poll: Which methods/algorithms you used for a Data Science or Machine Learning application?
- Aug 26, 2016.
Which methods/approaches you used in the past 12 months for an actual Data Science-related application? Please vote and we will analyze and publish the results.
- Is “Artificial Intelligence” Dead? Long Live Deep Learning?!?
- Aug 26, 2016.
Has Deep Learning become synonymous with Artificial Intelligence? Read a discussion on the topic fuelled by the opinions of 7 participating experts, and gain some additional insight into the future of research and technology.
- The top 5 Big Data courses to help you break into the industry
- Aug 25, 2016.
Here is an updated and in-depth review of top 5 providers of Big Data and Data Science courses: Simplilearn, Cloudera, Big Data University, Hortonworks, and Coursera
- Interpretability over Accuracy
- Aug 25, 2016.
If researchers can’t understand a provided answer, it is not viable. They can’t write about techniques they don’t understand beyond “Here are the numbers. Look how pretty my model is.” Good research, that ain’t.
- A Tutorial on the Expectation Maximization (EM) Algorithm
- Aug 25, 2016.
This is a short tutorial on the Expectation Maximization algorithm and how it can be used on estimating parameters for multi-variate data.
- Build Your Analytics Skills with TDWI Online Learning
- Aug 25, 2016.
Skill Up Over the Summer! Save on Business Analytics, Data Modeling, or other online courses* or bundle through Sep 2, 2016 with CODE: SUMMER.
- Introduction to Local Interpretable Model-Agnostic Explanations (LIME)
- Aug 25, 2016.
Learn about LIME, a technique to explain the predictions of any machine learning classifier.
- Top KDnuggets tweets, Aug 17-23: Approaching (Almost) Any #MachineLearning Problem; #Database Nirvana – can one query language rule them all?
- Aug 24, 2016.
In Search of #Database Nirvana - can one query language rule them all? Google Cloud Datalab: #Jupyter meets #TensorFlow, #cloud meets local deployment; Approaching (Almost) Any #MachineLearning Problem; The Gentlest Introduction to Tensorflow Part 1.
- A Gentle Introduction to Bloom Filter
- Aug 24, 2016.
The Bloom Filter is a probabilistic data structure which can make a tradeoff between space and false positive rate. Read more, and see an implementation from scratch, in this post.
- A Primer on Logistic Regression – Part I
- Aug 24, 2016.
Gain an understanding of logistic regression - what it is, and when and how to use it - in this post.
- A simple approach to anomaly detection in periodic big data streams
- Aug 24, 2016.
We describe a simple and scaling algorithm that can detect rare and potentially irregular behavior in a time series with periodic patterns. It performs similarly to Twitter's more complex approach.
- 4 Online Data Science Training Options for Your Team
- Aug 24, 2016.
We highlight and compare 4 great online data science training providers that can help you foster a data-driven organization: DataCamp, Lynda, Pluralsight, and Coursera.
- Predictive Analytics World for Government, Oct 17-20, Washington, DC
- Aug 23, 2016.
PAW Government is dedicated to exploring how agencies at all levels of government can use data science to reduce wait times, anticipate community needs, minimize overhead, and improve operational efficiency. Use code KDN150 to save.
- Predictive Analytics. Max Results. Min Time.
- Aug 23, 2016.
Successful analytics in the big data era does not start with data and software. It starts with immersive hands-on training, and goal-driven strategy. Get this training with TMA courseware, which spans all skill levels and analytic team roles - Wash-DC in October or Live Online in November.
- Data Science of Reviews: ReviewMeta tool Automatically Detects Unnatural Reviews on Amazon
- Aug 23, 2016.
ReviewMeta is a tool that analyzes millions of reviews and helps customers decide which ones to trust. As the dataset grows, so do the insights on unbiased reviews.
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How to Become a (Type A) Data Scientist
- Aug 23, 2016.
This post outlines the difference between a Type A and Type B data scientist, and prescribes a learning path on becoming a Type A. - The Inside Scoop on Apache Sqoop, Aug 25 Webinar
- Aug 23, 2016.
Last chance! Register for Aug 25 webinar to learn about the best practices for using Apache Sqoop and interoperability with JDBC data sources from relational to cloud.
- IAPA National Conference: Advancing Analytics 2016
- Aug 22, 2016.
At IAPA Advancing Analytics event you can meet and hear from the leading global and local thinkers on big data, predictive analytics, machine learning, sentiment analysis, IoT, and more. Early bird ends 25 August, so get your ticket now.
- A Neat Trick to Increase Robustness of Regression Models
- Aug 22, 2016.
Read this take on the validity of choosing a different approach to regression modeling. Why isn't L1 norm used more often?
- Top Stories, Aug 15-21: 10 Need to Know Machine Learning Algorithms; Does Data Scientist Mean What You Think It Means?
- Aug 22, 2016.
The 10 Algorithms Machine Learning Engineers Need to Know; Does Data Scientist Mean What You Think It Means?; The Gentlest Introduction to Tensorflow; Central Limit Theorem for Data Science - Part 2
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How to Become a Data Scientist – Part 1
- Aug 22, 2016.
Check out this excellent (and exhaustive) article on becoming a data scientist, written by someone who spends their day recruiting data scientists. Do yourself a favor and read the whole way through. You won't regret it! - Big Data Innovation Summit – KDnuggers Offer
- Aug 22, 2016.
The Big Data Innovation Summit in Boston, Sep 8-9 brings you top experts who discuss how data can be made actionable, effective and produce tailored insights. Use code KD10 for extra savings.
- Analytics, Security, Deep Learning, IoT, Data Science Online Courses
- Aug 20, 2016.
Upcoming online courses include : Statistical and machine learning methods for detecting anomalies, identifying images, and processing data from sensors; Deep Learning; Internet of Things (IoT): Programming for Analytics; and Meta Analysis in R.
- Misinformation Key Terms, Explained
- Aug 20, 2016.
Misinformation has emerged as a key issue for social media platforms. This post will introduce the concept of misinformation and the 8 Key Terms, which provides insights into mining misinformation in social media.
- 5 Awesome jQuery-based Interactive Data Visualization Tools
- Aug 19, 2016.
Presented in this post are five great interactive data visualization libraries for jQuery.
- Don’t just read about learning data
- Aug 19, 2016.
Master data analytics at Level Bootcamp this fall to get ahead in your career and your life. Apply by Aug 31 for a 15% tuition discount.
- The Gentlest Introduction to Tensorflow – Part 2
- Aug 19, 2016.
Check out the second and final part of this introductory tutorial to TensorFlow.
- Top Machine Learning Projects for Julia
- Aug 19, 2016.
Julia is gaining traction as a legitimate alternative programming language for analytics tasks. Learn more about these 5 machine learning related projects.
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The 10 Algorithms Machine Learning Engineers Need to Know
- Aug 18, 2016.
Read this introductory list of contemporary machine learning algorithms of importance that every engineer should understand. - Approaching (Almost) Any Machine Learning Problem
- Aug 18, 2016.
If you're looking for an overview of how to approach (almost) any machine learning problem, this is a good place to start. Read on as a Kaggle competition veteran shares his pipelines and approach to problem-solving.
- Join Us for the Top Analytics Conference
- Aug 18, 2016.
Sign up now for the industry's leading analytics and data management conference. Get an extra $100 off TDWI San Diego with exclusive KDnuggets discount code.
- Top KDnuggets tweets, Aug 10-16: 5 EBooks to Read Before Getting into a #DataScience or #BigData Career
- Aug 17, 2016.
5 EBooks to Read Before Getting into a #DataScience or #BigData Career; Visualizing 1 Billion Points of #Data Webinar; #Cartoon: Make Data Great Again!; The role of the activation function in a #NeuralNetwork
- Data Science Challenges
- Aug 17, 2016.
This post is thoughts for a talk given at the UN Global Pulse lab in Kampala, and covers the challenges in data science.
- The Gentlest Introduction to Tensorflow – Part 1
- Aug 17, 2016.
In this series of articles, we present the gentlest introduction to Tensorflow that starts off by showing how to do linear regression for a single feature problem, and expand from there.
- Free MOOC: Business Analytics Using Forecasting – enroll now
- Aug 17, 2016.
A new iteration of a MOOC on business analytics using forecasting gets underway in October. Enroll today!
- Does Data Scientist Mean What You Think It Means?
- Aug 16, 2016.
Do we have an accurate idea of what "data scientist" actually means? Read this thought-provoking opinion on the topic.
- Central Limit Theorem for Data Science – Part 2
- Aug 16, 2016.
This post continues an explanation of Central Limit Theorem started in a previous post, with additional details... and beer.
- PAW Financial Keynotes Making History
- Aug 16, 2016.
The first-ever Predictive Analytics World conference dedicated to Financial Services will be held this October 23-27 in New York. Register now for early bird pricing, and save an additional $150 with code KDN150.
- Artificial Intelligence: Useful Technology or the Next Frankenstein?
- Aug 15, 2016.
For a refreshingly insightful and honest answer this question, just ask Facebook's founder Mark Zuckerberg.
- Top Stories, Aug 8-14: Beginner’s Guide to Neural Nets with R; 5 Data Science/Big Data Ebooks
- Aug 15, 2016.
Beginner's Guide to Neural Networks with R; 5 EBooks to Read Before Getting into A Data Science or Big Data Career; Cartoon: Make Data Great Again; Understanding the Bias-Variance Tradeoff: An Overview
- Tales from ICML: Insights and Takeaways
- Aug 15, 2016.
The dust has settled from ICML 2016, having been held in June in NYC. Read some perspective on what was offered at the conference and relevant takeaways from a reflective attendee.
- O’Reilly AI: Last chance to get Early Price
- Aug 15, 2016.
This is your last chance to get early pricing at the O'Reilly AI conference happening in New York September 26-27. Space is limited, so register now!
- Big Data Doesn’t Rule The Olympics
- Aug 13, 2016.
Whenever there is a Big Data conversation, especially in sports, expectations have to be set correctly. Big Data isn’t perfect, but it is a lot better than the more superficial methods of making a judgment.
- Cartoon: Make Data Great Again
- Aug 13, 2016.
This KDnuggets cartoon considers a speech that a certain presidential candidate can give on a topic of Big Data.
- Central Limit Theorem for Data Science
- Aug 12, 2016.
This post is an introductory explanation of the Central Limit Theorem, and why it is (or should be) of importance to data scientists.
- Understanding the Empirical Law of Large Numbers and the Gambler’s Fallacy
- Aug 12, 2016.
Law of large numbers is a important concept for practising data scientists. In this post, The empirical law of large numbers is demonstrated via simple simulation approach using the Bernoulli process.
- Robots Need “Common Sense” AI to Work Out Our Uncertain World
- Aug 12, 2016.
At the Machine Intelligence Summit in Berlin last week, Jeremy Wyatt, Professor of Robotics and Artificial Intelligence at University of Birmingham, was asked a few questions about his work in mobile robot task planning and manipulation.
- 5 EBooks to Read Before Getting into A Data Science or Big Data Career
- Aug 11, 2016.
A short, carefully-curated list of 5 free ebooks to help you better understand what Data Science is all about and how you can best prepare for a career in data science, big data, and data analysis.
- Making Data Science Accessible – Neural Networks
- Aug 11, 2016.
This post attempts to make the underlying concepts of neural networks more accessible to everyone. Gain a high level view of their working here.
- Stop Blaming Terminator for Bad AI Journalism
- Aug 11, 2016.
Too often, we blame The Terminator for the public's misconceptions concerning machine learning. But do James Cameron and the Austrian Oak stand wrongfully accused?
- A Beginner’s Guide to Neural Networks with R!
- Aug 11, 2016.
In this article we will learn how Neural Networks work and how to implement them with the R programming language! We will see how we can easily create Neural Networks with R and even visualize them. Basic understanding of R is necessary to understand this article.
- Visualizing 1 Billion Points of Data: Doing It Right – Aug 18 Webinar
- Aug 11, 2016.
Join Continuum Analytics on August 18 for a webinar on Big Data visualization with the datashader library. Save your spot today!
- Big Data Key Terms, Explained
- Aug 11, 2016.
Just getting started with Big Data, or looking to iron out the wrinkles in your current understanding? Check out these 20 Big Data-related terms and their concise definitions.
- Chief Data Scientist Forum, San Francisco, Nov 16-17
- Aug 10, 2016.
The inaugural Chief Data Scientist Forum will be the premier event for high-level data science practitioners, containing essential content and new ideas to develop the leadership role for data science. Use code KDCDS to save on registration.
- Top KDnuggets tweets, Aug 03-09: Understanding the Bias-Variance Tradeoff: An Overview
- Aug 10, 2016.
Understanding the Bias-Variance Tradeoff: An Overview; Cartoon: Facebook #DataScience experiments and Cats; Bayesian #Machine Learning, Explained; Deep Reinforcement Learning for Keras.
- Is a Chief Data Officer Required for Analytics Success?
- Aug 10, 2016.
In this insightful opinion piece, gain perspective on whether a Chief Data Officer is required for an organization's analytics success.
- Should We Be Rethinking Unsupervised Learning?
- Aug 10, 2016.
Roland Memisevic, Assistant Professor at the University of Montreal and Chief Scientist at Twenty Billion Neurons, explores ideas on rethinking unsupervised learning, which he feels may explain what scientists have been doing wrong.
- Exploring Social Media Diversity with Natural Language Processing
- Aug 10, 2016.
This post uses natural language processing on Twitter data to determine the diversity of Twitter accounts the author is following. An innovative take on social media analytics.
- 3 Thoughts on Why Deep Learning Works So Well
- Aug 10, 2016.
While answering a posed question in his recent Quora Session, Yann LeCun also shared 3 high-level thoughts on why deep learning works so well.
- Real-Time Decisions for Real Results
- Aug 9, 2016.
Enova Decisions real-time predictive analytics services help businesses improve the customer experience while protecting against fraud, optimizing operations and increasing marketing profitability.
- Predictive Analytics World for Healthcare – Oct 23-27 in New York
- Aug 9, 2016.
PAW Healthcare brings together top predictive analytics experts, practitioners, authors, and healthcare thought leaders to discuss concrete examples of deployed predictive analytics in the healthcare industry. Save w. code KDNPAW150.
- Advice for Data Science Interviews
- Aug 9, 2016.
Check out an interview excerpt from Springboard’s Guide to Data Science Interviews. Determine how one can find data science interviews - and ace them!
- Choosing Tools for Data ETLs
- Aug 9, 2016.
Which tool should I use for my data pipelines? Get some advice from a data scientist recently having gone through this pipeline tool selection process.
- Stanford Webinar – Supersize Your Career with Big Data Opportunities
- Aug 9, 2016.
Sign up for this upcoming webinar which will outline the variety of big data programs offered by Stanford to working professionals and answer common questions.
- 7 Steps to Understanding Computer Vision
- Aug 9, 2016.
A starting point for Computer Vision and how to get going deeper. Dive into this post for some overview of the right resources and a little bit of advice.
- MICCAI 2016 Cancer Radiomics Challenge
- Aug 9, 2016.
Details on the ongoing MICCAI 2016 Cancer Radiomics Challenge, organized by University of Texas MD Anderson Cancer Center radiation oncology team, hosted on Kaggle, and being held until September 12th.
- Short course: Statistical Learning and Data Mining IV, Washington, DC, Oct 19-20
- Aug 8, 2016.
This new two-day course gives a detailed and modern overview of statistical models used by data scientists for prediction and inference, including sparse models and deep learning.
- Top Stories, Aug 1-7: Bayesian Machine Learning, Explained; Data Science for Beginners Video Series
- Aug 8, 2016.
Bayesian Machine Learning, Explained; Data Science for Beginners Video Series; What Statistics Topics are Needed for Excelling at Data Science?; The Core of Data Science
- Cartoon: Facebook data science experiments and Cats
- Aug 8, 2016.
In honor of International Cat Day, we revisit KDnuggets cartoon that looks at the Facebook data science experiment on emotion manipulation and the importance of happy kittens.
- Data Science, Data Engineering Bootcamp, Seattle, Oct 10-14
- Aug 8, 2016.
Data Science Dojo will be teaching a comprehensive five-day Data Science & Data Engineering Bootcamp in Seattle on October 10 - 14. Register today!
- The Inside Scoop on Apache Sqoop
- Aug 8, 2016.
Check out this webinar to learn about the best practices for using Sqoop and interoperability with JDBC data sources from relational to cloud. Register today!
- Understanding the Bias-Variance Tradeoff: An Overview
- Aug 8, 2016.
A model's ability to minimize bias and minimize variance are often thought of as 2 opposing ends of a spectrum. Being able to understand these two types of errors are critical to diagnosing model results.
- Connect with IoT Leaders, Learn and Network at Connected+, Toronto, Oct 12-13
- Aug 8, 2016.
This premier event offers a 360-degree view of the connected device ecosystems and all IoT verticals. Early bird till Aug 11. Use code KDNuggetspromo for extra savings.
- Top July stories: Bayesian Machine Learning, Explained; Why Big Data is in Trouble: They Forgot About Applied Statistics
- Aug 6, 2016.
Also - How to Start Learning Deep Learning; What Has Pokemon Got To Do With Big Data?
- Introduction to Data Science and Data Visualization with D3.js: San Francisco and New York City
- Aug 5, 2016.
Visit Metis in San Francisco (Aug 10) and New York City (Aug 9) for an overview of the field of data science and the use of data visualization tool D3.js.
- Brain Monitoring with Kafka, OpenTSDB, and Grafana
- Aug 5, 2016.
Interested in using open source software to monitor brain activity, and control your devices? Sure you are! Read this fantastic post for some insight and direction.
- Contest Winner: Winning the AutoML Challenge with Auto-sklearn
- Aug 5, 2016.
This post is the first place prize recipient in the recent KDnuggets blog contest. Auto-sklearn is an open-source Python tool that automatically determines effective machine learning pipelines for classification and regression datasets. It is built around the successful scikit-learn library and won the recent AutoML challenge.
- Nigeria: Telling Internally Displaced Persons Stories Using Visual Data and Infographics
- Aug 5, 2016.
Read a data-driven discussion on the plight of internally displaced persons (IDPs) in Nigeria, and see the real power of data science and data visualization.
- Reinforcement Learning and the Internet of Things
- Aug 5, 2016.
Gain an understanding of how reinforcement learning can be employed in the Internet of Things world.
- BaseCamp – New Innovative Data Science Bootcamp in Vienna
- Aug 4, 2016.
Big Data startup Knoyd is launching a data science bootcamp disrupting the training and hiring of data scientists. The first cohort will start in Vienna in January 2017. Apply by August 31.
- Insurers, the new kids on the blockchain: Everledger, Guardtime and CGSC discuss why
- Aug 4, 2016.
Where are insurers in adopting blockchain technology and what are the benefits? Insurance Nexus conducted exclusive interviews with Everledger, Guardtime and CGSC and created an exclusive white paper which you can freely download.
- Making Data Science Accessible – HDFS
- Aug 4, 2016.
This post explains some basic Big Data concepts and offers some insight into when HDFS can be useful, employing basic analogies to do so.
- Contest 2nd Place: Automated Data Science and Machine Learning in Digital Advertising
- Aug 4, 2016.
This post is an overview of an automated machine learning system in the digital advertising realm. It is an entrant and second-place recipient in the recent KDnuggets blog contest.
- Upcoming Meetings in Analytics, Big Data, Data Mining, Data Science: August and Beyond
- Aug 4, 2016.
Coming soon: KDD 2016, HPE Big Data Boston, Global Big Data Santa Clara, Big Data Innovation Boston, Adversarial ML San Francisco, Cypher 2016 Bangalore, and many more.
- Common Sense in Artificial Intelligence… by 2026?
- Aug 4, 2016.
An insightful opinion piece on the future of common sense in AI. A recommended read by an authority in the field.
- Understand customer needs with choice modeling
- Aug 3, 2016.
Which product features are most important to your customers? This case study of American vs Belgian chocolate choice analysis can help you understand which factors drive your customer.
- Top KDnuggets tweets, Jul 27 – Aug 2: Understanding neural networks with Google TensorFlow Playground; Getting Started with Data Science in Python
- Aug 3, 2016.
Understanding neural networks with Google TensorFlow Playground; The 100 Best-Funded #Analytics #DataScience #Startups; Great tutorial: Getting Started with #DataScience - #Python; #MachineLearning over 1M hotel reviews: interesting insights.
- Getting Started with Data Science – R
- Aug 3, 2016.
A great introductory post from DataRobot on getting started with data science in R, including cleaning data and performing predictive modeling.
- Contest 2nd Place: Automating Data Science
- Aug 3, 2016.
This post discusses some considerations, options, and opportunities for automating aspects of data science and machine learning. It is the second place recipient (tied) in the recent KDnuggets blog contest.
- Data Science for Beginners: Fantastic Introductory Video Series from Microsoft
- Aug 3, 2016.
The remaining videos in Microsoft's Data Science for Beginners video series are available now. Have a look at what they offer.
- Looker: Exploring the Census, Aug 11 Webinar
- Aug 3, 2016.
We will dig into 20 years of Census voting data that we have loaded into Google BigQuery and modeled in Looker. You can ask anything you're interested and we will look it up, live.
- Introducing Predictive Analytics World Financial, Oct 24-27, New York City
- Aug 2, 2016.
PAW Financial focuses on analytics needs of banks, insurance companies, credit card companies, investment firms, and other financial institutions. Book now for the early bird rates, and save extra with code KDN150.
- Webinar: Predictive Analytics: Failure to Launch [Aug 16]
- Aug 2, 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 August 16.
- Make Your Data Greater: Big Data Innovation, IoT, Data Visualization Summits, Boston, Sep 8-9
- Aug 2, 2016.
Check Big Data Innovation, Internet of Things, and Data Visualization Summits in Boston, Sep 8-9, 2016. The program is filling out with new sessions being added every week - the depth and breadth of content covered is unrivaled. Use code KD10 for 10% off All Access path.
- What Statistics Topics are Needed for Excelling at Data Science?
- Aug 2, 2016.
Here is a list of skills and statistical concepts suggested for excelling at data science, roughly in order of increasing complexity.
- Data Science Automation: Debunking Misconceptions
- Aug 2, 2016.
This opinion piece aims to clear up some proposed misconceptions surrounding data science automation.
- Doing Statistics with SQL
- Aug 2, 2016.
This post covers how to perform some basic in-database statistical analysis using SQL.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in July 2016
- Aug 2, 2016.
Director for the Institute for CyberScience at Penn State, Research Fellow - Data Science at Monash U; Postdocs at UTSW; PhD positions at TU/E Netherlands, Leiden U Netherlands, Ningbo China.
- The Data Mining Group releases PMML v4.3
- Aug 2, 2016.
PMML is an application and system independent format for statistical and data mining models. Key PMML 4.3 features include Improved support for post-processing, model types, and model elements, and new models for Gaussian Process and Bayesian Networks. Check PMML session at KDD-16.
- Top /r/MachineLearning Posts, July: Google Brain AMA, Geoff Hinton Awarded IEEE Medal, Hinton ANN Course Lives!
- Aug 2, 2016.
Google Brain AMA; Geoff Hinton Awarded IEEE Medal; Geoff Hinton's ANN Course Lives; Google’s DeepMind Reduces Data Center Cooling Bill; Training an artificial neural network to play Diablo 2
- And the Winner is… Stepwise Regression
- Aug 1, 2016.
This post evaluates several methods for automating the feature selection process in large-scale linear regression models and show that for marketing applications the winner is Stepwise regression.
- Top Stories, July 25-31: What Has Pokemon Got To Do With Big Data?; 35 Open Source tools for Internet of Things
- Aug 1, 2016.
What Has Pokemon Got To Do With Big Data?; 35 Open Source tools for Internet of Things; 7 Steps to Understanding NoSQL Databases; SAS vs R vs Python: Which Tool Do Analytics Pros Prefer?
- The Core of Data Science
- Aug 1, 2016.
This post provides a simplifying framework, an ontology for Machine Learning and some important developments in dynamical machine learning. From first hand Data Science product experience, the author suggests how best to execute Data Science projects.
- Dataiku DSS 3.1 – Now with 5 ML Backends & Scala!
- Aug 1, 2016.
Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface.
- Yann LeCun Quora Session Overview
- Aug 1, 2016.
Here is a quick oversight, with excerpts, of the Yann LeCun Quora Session which took place on Thursday July 28, 2016.
- FlyElephant 2.0, Big Data High-Performance Computing Platform
- Aug 1, 2016.
FlyElephant is a platform for data scientists, engineers and scientists, which provides a ready-computing infrastructure for high-performance computing and rendering.