Dec
2016 January
All (120) | Courses, Education (11) | Meetings (12) | News, Features (27) | Opinions, Interviews, Reports (35) | Publications (10) | Software (5) | Top Tweets (3) | Tutorials, Overviews (11) | Webcasts (6)
- Top stories for Jan 24-30: 7 Common Data Science Mistakes; Businesses Will Need 1M Data Scientists by 2018 - Jan 31, 2016.
20 Questions to Detect Fake Data Scientists; TensorFlow Disappoints - Google Deep Learning falls shallow; 7 Common Data Science Mistakes and How to Avoid Them; Businesses Will Need One Million Data Scientists by 2018.
- Data ScienceTech Institute, online (off-campus) education, starting March 2016 - Jan 30, 2016.
Data ScienceTech Institute announces the upcoming online education, allowing off-campus education in its programs MSc Data Scientist Designer and MSc Executive Big Data Analyst.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in January 2016 - Jan 30, 2016.
Academic/Research positions Analytics and Data Science at INRIA, U. Texas, U. Mannheim, Eindhoven U. of Technology, IBM Social Good Fellowship, Yale, Xavier, U. Western Switzerland, U. Paris-Est Marne-la-Vallee, and U. Tampere.
- How banks can beat new finance boys with data - Jan 29, 2016.
The rise of Apple/Google smartphone payments and new fintech start ups present challenges to traditional banks. Banks can fight back, but they need to understand how to better use their data to understand its customers.
- Details on First Data Science Job Salary - Jan 29, 2016.
A person new to the Data Science field details their salary and the negotiation process.
- New Poll: Deep Learning – does reality match the hype? - Jan 29, 2016.
New KDnuggets Poll looks at the very hot field of Deep Learning and asks: does reality match the hype? Please vote!
- Python Data Science with Pandas vs Spark DataFrame: Key Differences - Jan 29, 2016.
A post describing the key differences between Pandas and Spark's DataFrame format, including specifics on important regular processing features, with code samples.
- Is Deep Learning Overhyped? - Jan 29, 2016.
With all of the success that deep learning is experiencing, the detractors and cheerleaders can be seen coming out of the woodwork. What is the real validity of deep learning, and is it simply hype?
- Explore Data Science – self-paced online learning - Jan 28, 2016.
Originally created by Booz Allen Hamilton for its team of nearly 600 data science professionals, Explore Data Science is now available exclusively from Metis for $99 for 2 months access.
- U. Delaware Certificate in Analytics: Optimizing Big Data - Jan 28, 2016.
Understand why big data is so important in business decisions, improve your data management skills, and join the rapidly growing analytics field. Classes Feb 18 - May 25, 2016 in Wilmington, DE.
- Deep Learning with Spark and TensorFlow - Jan 28, 2016.
The integration of TensorFlow with Spark leverages the distributed framework for hyperparameter tuning and model deployment at scale. Both time savings and improved error rates are demonstrated.
- Businesses Will Need One Million Data Scientists by 2018 - Jan 28, 2016.
Deepening shortage of Data Science talent and cybersecurity challenges are trends shaping business in 2016.
- How to Check Hypotheses with Bootstrap and Apache Spark - Jan 28, 2016.
Learn how to leverage bootstrap sampling to test hypotheses, and how to implement in Apache Spark and Scala with a complete code example.
- Useful Data Science: Feature Hashing - Jan 28, 2016.
Feature engineering plays major role while solving the data science problems. Here, we will learn Feature Hashing, or the hashing trick which is a method for turning arbitrary features into a sparse binary vector.
- Implementing Your Own k-Nearest Neighbor Algorithm Using Python - Jan 27, 2016.
A detailed explanation of one of the most used machine learning algorithms, k-Nearest Neighbors, and its implementation from scratch in Python. Enhance your algorithmic understanding with this hands-on coding exercise.
- How to Tackle a Lottery with Mathematics - Jan 27, 2016.
With mathematical rigor and narrative flair, Adam Kucharski reveals the tangled history of betting and science. The house can seem unbeatable. In this book, Kucharski shows us just why it isn't. Even better, he shows us how the search for the perfect bet has been crucial for the scientific pursuit of a better world.
- Strategic Business Analytics – n5 Most Coveted Coursera Certificate in 2015 - Jan 27, 2016.
ESSEC Specialization on “Strategic Business Analytics” was ranked #5 most coveted Coursera certificate on LinkedIn in 2015. The course is aimed at students, business analysts and data scientists who want to apply statistical knowledge and techniques to business contexts.
- Jan 27 Webinar: 3 Ways to Improve your Regression, Part 2 - Jan 26, 2016.
How to take data science techniques even further to extract actionable insight and take advantage of advanced modeling features. You will walk away with several different methods to turn your ordinary regression into an extraordinary regression!
- Google Launches Deep Learning with TensorFlow MOOC - Jan 26, 2016.
Google and Udacity have partnered for a new self-paced course on deep learning and TensorFlow, starting immediately.
- Webinar: Data Mining: Failure to Launch [Feb 9] - Jan 26, 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 Feb 9.
- Modern Data Science and Evolution of BI - Jan 26, 2016.
Modern big data discovery tools enable all employees to access the data, streamlining the data prep process, and allowing data scientists to spend more time on advanced analytics. The infographics in this post show the evolution of the data scientist from data drudgery to modern data science for all.
- 7 Common Data Science Mistakes and How to Avoid Them - Jan 26, 2016.
Data scientist in business is as similar as to that of a detective: discovering the unknown. But, while venturing onto this journey they do tend to fall into the pitfalls. Understand, how these mistakes are made and how you can avoid them.
- Early bird ends for 3 analytics events, Deadline: Feb. 5 - Jan 26, 2016.
Lights, camera, and analytics action! Early bird rates end Feb 5th for three incredible analytics conferences in San Francisco. Save with KDnuggets code KDN150.
- Top KDnuggets tweets, Jan 11-24: Why R Users will inevitably become #Bayesians; Is #Quran really more violent that #Bible? - Jan 25, 2016.
TextAnalytics examines: Is #Quran really more violent that #Bible? Why R Users will inevitably become #Bayesians; Next #MachineLearning problem: what to do with 80% accurate algorithm? ;Learning to Code #NeuralNetworks #MachineLearning Tutorial;
- KNIME Spring Summit 2016, Berlin, Feb 22-26, KDnuggets discount - Jan 25, 2016.
Listen to some of the greatest data scientists to speak about their use of KNIME software to solve complex data problems in life sciences, manufacturing, marketing, retail sales, and many other areas. Register and save with code KDNUGGETS_KNIME_SUMMIT2016.
- Keynotes at Predictive Analytics World San Fran, $550 Savings - Jan 25, 2016.
Check out the top experts who are PAW San Francisco 2016 keynote speakers and be use KDnuggets discount code KDN150 to save on registration.
- Deep Feelings On Deep Learning - Jan 25, 2016.
A thoughtful opinion piece on deep learning and its role in Strong AI. A pragmatic view of deep learning and its comparison to competing learning strategies is presented.
- GraphDB Webinars from Ontotext: Data Visualization, Graph Analytics - Jan 25, 2016.
Two upcoming webinars show how to use the powerful GraphDB from Ontotext: Powerful Searches and Data Visualization in Graph Database (Jan 28) and Transforming your Graph Analytics with GraphDB (Feb 4). Check also GraphDB free version.
- Sentiment Analysis & Predictive Analytics for trading. Avoid this systematic mistake - Jan 25, 2016.
The financial market is the ultimate testbed for predictive theories. With this post we want to highlight the common mistakes, observed in the world of predictive analytics, when computer scientists venture into the field of financial trading and quantitative finance.
- Data Analytics Boosting Digital Engagement at Australian Open 2016 - Jan 25, 2016.
Advanced analytics and visualization is enhancing fan experience and operational excellence at Australian Open 2016
- Beyond the Fence, and the Advent of the Creative Machines - Jan 25, 2016.
Creative machines have been making their influence felt for some time, but an upcoming stage production challenges preconceived notions of what art is.
- Top stories for Jan 17-23: 20 Questions to Detect Fake DS; Yahoo Releases the Largest-ever Machine Learning Dataset - Jan 24, 2016.
20 Questions to Detect Fake Data Scientists; Google Deep Learning TensorFlow Disappoints; Yahoo Releases the Largest-ever Machine Learning Dataset for Researchers; Research Leaders on Data Mining, Data Science and Big Data.
- Spark and the Remorseless Recrystallization of the Open Source Analytics Ecosystem - Jan 23, 2016.
Apache Spark had robust machine learning, graph, streaming, and in-memory capability to the Hadoop-centric ecosystem. In 2016, we expect adoption in diverse big data, advanced analytics, data science, Internet of Things, and other application domains.
- Where Analytics, Data Mining, Data Science were applied in 2015 - Jan 23, 2016.
Consumer Analytics is still the leading application area for analytics & data mining (although losing its share), followed by Finance, and Banking. Highest growth is in Games, Entertainment/Music, and Social Good/Non-profit applications. 3-year comparison shows surprising stability.
- Hadoop and Big Data: The Top 6 Questions Answered - Jan 22, 2016.
6 questions surrounding Hadoop and Big Data are posed and answered, including those related to implementation, management, and practical uses. Find out where Hadoop currently sits in the world of Big Data.
- Chief Analytics Officer Forum Europe, 7-9 March, London - Jan 22, 2016.
The CAO Forum is the premier event for CAOs and senior analytics professionals, providing top-level strategic advice and discussion.
- Learning to Code Neural Networks - Jan 22, 2016.
Learn how to code a neural network, by taking advantage of someone else's experiences learning how to code a neural network.
- Top 2015 KDnuggets Stories on Analytics, Big Data, Data Science, Data Mining, Machine Learning, updated - Jan 21, 2016.
R vs Python for Data Science: The Winner is ...; 60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning; Top 20 Python Machine Learning Open Source Projects; 50+ Data Science and Machine Learning Cheat Sheets.
- Favorite 2015 Schmarzo Big Data Blogs - Jan 21, 2016.
A top Big Data influencer lists, outlines, and summarizes his favorite blog posts of 2015. Gain some additional insight into various data science topics with some of these great entries.
- Airbnb: Lessons on Digital, Startups, Big Data and Disrupting Markets - Jan 21, 2016.
AirBnB has brought together unmatched supply and demand and allowed for market-driven evaluation of assets. We are sharing lessons learnt from them for digital startups and big data organisations.
- Public Knowledge Graph – small guys unite - Jan 21, 2016.
Currently, only global corporations like Google or Facebook can maintain a vast knowledge graph about the world. Little companies which rely on knowing world context need to unite to create a Public Knowledge Graph, or they will fall further behind the big guys.
- CEOs Pursue Data and Analytics for Stakeholder Engagement - Jan 21, 2016.
PWC’s Global CEO Survey highlights the strategic importance of Data and Analytics for achieving wider stakeholder engagement.
- Data Scientist – best job in America - Jan 20, 2016.
Data Scientist is the best job in America, according to Glassdoor, with median base salary $117K, and high career prospects. Analytics Manager ranks n. 11. However, the number of available positions is much less than predicted shortage of 140-190,000.
- Three Simple Resolutions to Design Better DataViz - Jan 20, 2016.
Start your New Year off with resolutions to produce better data visualizations: visualize your data, remove chart legends, and try new things.
- 12 Data Analytics Thought Leaders on Twitter - Jan 20, 2016.
Menlo technologies compiled a list of Data Analytics thought leaders and companies they follow on Twitter. Spend some time on their informative and as entertaining Twitter Feeds, web and blogs.
- The Unreasonable Reputation of Neural Networks - Jan 20, 2016.
A discussion of why deep neural networks are captivating imaginations everywhere, specifically their abilities to model many natural functions well and to learn surprisingly useful representations.
- Anthony Goldbloom gives you the Secret to winning Kaggle competitions - Jan 20, 2016.
Kaggle CEO shares insights on best approaches to win Kaggle competitions, along with a brief explanation of how Kaggle competitions work.
- Big Data Bootcamp, Santa Clara, Jan 20-22 - Jan 19, 2016.
This is a fast paced, vendor agnostic, technical overview of the Big Data landscape. No prior knowledge of databases or programming is assumed. Use code KDNUGGETS to save.
- SMU Online MS In Data Science - Jan 19, 2016.
Working professionals from anywhere in the world can apply the skills and strategies they learn in class to their current jobs right away and to graduate in as little as 18 months.
- Introducing Quora’s Machine Learning Sessions Series - Jan 19, 2016.
Quora is launching a new format for interacting with domain experts and sharing knowledge, and its first topic is Machine Learning. Yoshua Bengio is the first expert, and he is accepting questions now.
- FlyElephant scientific computing platform – new features, promotions, webinars - Jan 19, 2016.
FlyElephant wishes you a Happy New Year. We started this year with an expansion of our platform, new webinars, and the formation of a community around the platform.
- Webinar: Text Mining Along the Drug Development Pipeline, Jan 28 - Jan 19, 2016.
Research and development of a single drug can take 10 years and cost billions. Learn about applications and business value of text mining in the life sciences through a series of real world examples.
- 7 Essential Elements in a Real-time Streaming Analytics Platform - Jan 19, 2016.
Download the white paper to learn about what to look for in a Big Data real-time streaming analytics (RTSA) platform.
- 5 ways to master uplift modeling - Jan 18, 2016.
At PAW for Business in San Francisco, April 3-7, uplift modeling will be covered in three sessions and one workshop - plus, check out an on-topic article by PAW founder Eric Siegel.
- Yahoo Releases the Largest-ever Machine Learning Dataset for Researchers - Jan 18, 2016.
Are you interested in massive amounts of data for research? Yahoo has just released the largest-ever machine learning dataset to the research community.
- Research Leaders on Data Mining, Data Science and Big Data key advances, top trends - Jan 18, 2016.
Research Leaders in Data Science and Big Data reflect on the most important research advances in 2015 and the key trends expected to dominate throughout 2016.
- Camelyon16 – Machine Learning Challenge in cancer detection - Jan 18, 2016.
Camelyon16 challenge in conjugation with IEEE International Symposium on Biomedical Imaging is here! You have to design and develop a system which can detect and localize metastatic regions in whole slide microscopic images.
- Scikit-learn and Python Stack Tutorials: Introduction, Implementing Classifiers - Jan 18, 2016.
A small collection of introductory scikit-learn and Python stack tutorials for those with an existing understanding of machine learning looking to jump right into using a new set of tools.
- Kickstart Your Data Initiatives in 2016 – KDnuggets discount - Jan 18, 2016.
The Apache Hadoop, Predictive Analytics and Data Science Innovation Summits will be in San Diego, Feb 18-19. Get 20% off all two-day passes with code KD20.
- Top stories for Jan 10-16: 20 Questions to Detect Fake Data Scientists; Machine Intelligence vs Machine Learning vs Deep Learning vs AI - Jan 17, 2016.
20 Questions to Detect Fake Data Scientists; TensorFlow Disappoints - Google Deep Learning falls shallow; Machine Intelligence vs Machine Learning vs Deep Learning vs AI; What To Expect from Deep Learning in 2016.
- Data Science Humor: Google Analytics, if Applied in Real Life - Jan 16, 2016.
From the lighter side: how Google Analytics would look if applied in real life situations.
- Top 100 Big Data Experts to Follow - Jan 15, 2016.
Maptive gives us another list of top Big Data Influencers to check out, including data-driven reasons as to why individuals are included.
- 20+ hottest research papers on Computer Vision, Machine Learning - Jan 15, 2016.
December's ICCV 2015 conference in Santiago, Chile has come and gone, but that's no reason not to know about its top papers. Get an update on which computer vision papers and researchers won awards.
- Hitchhikers Guide to Azure Machine Learning Studio - Jan 15, 2016.
Learn Azure ML Studio through this brief hands-on tutorial. This step-by-step guide will help you get a quick-start and grasp the basics of this Predictive Modeling tool.
- Spark 2015 Year In Review - Jan 15, 2016.
Apache Spark went through a lot in 2015. Get a solid review from Databricks, the steward organization founded by the creators of Spark and the drivers of its innovation.
- U. Delaware Business Analyst Certificate – Info Session Jan 21 - Jan 15, 2016.
Boost your career by learning how to leverage technology to maximize business value with this certificate - only 3 months to complete. Info Session, Jan 21, Wilmington, DE.
- What Is Machine Intelligence Vs. Machine Learning Vs. Deep Learning Vs. Artificial Intelligence (AI)? - Jan 14, 2016.
A discussion of three major approaches to building smart machines - Classic AI, Simple Neural Networks, and Biological Neural Networks - and examples as to how each approach might address the same problem.
- New KDnuggets Podcasts Page - Jan 14, 2016.
Check out KDnuggets' new podcasts page, and let us know what we're missing.
- Plausibility vs. probability, prior distributions, and the garden of forking paths - Jan 14, 2016.
A discussion on plausibility vs. probability: while many given events may be plausible, but they can’t all be probable.
- IoT Data Science Strategy - Jan 14, 2016.
How solid is your strategy for managing data from the Internet of Things (IoT)? A leading analyst firm reveals the smart, new approach - get the white paper.
- InformationWeek Top Data Science, Analytics, and BI experts on Twitter - Jan 14, 2016.
Twitter is great place to learn about what data scientists, business intelligence practitioners, and analytics experts are thinking. Here are 11 of InformationWeek favorites.
- Creating a methodology for Data Science for IoT (IoT Analytics) - Jan 13, 2016.
While there is no specific methodology to solve Data Science for IoT (IoT Analytics) problems, perhaps it is time to draft one.
- Climate Change, Clearly Visualized - Jan 13, 2016.
Global warming has been argued in depths and breadths and arguments for and against are championed too.Here, with a simple data science we obtained a simple (and increasingly accepted) conclusion: the global warming is real.
- The Data Awakens: Star Wars Sentiment Analysis - Jan 13, 2016.
We have tracked the activity on Twitter around the release date to gain insight into the reactions of people and their feelings about the latest episode of the most famous movie franchise in history.
- Top 10 Deep Learning Projects on Github - Jan 13, 2016.
The top 10 deep learning projects on Github include a number of libraries, frameworks, and education resources. Have a look at the tools others are using, and the resources they are learning from.
- A Look Back on the 1st Three Months of Becoming a Data Scientist - Jan 13, 2016.
A person new to the Data Science field summarizes his surprising findings after a few months on the job.
- 3 Ways to Improve your Regression, Jan 20 & 27 Webinars, Hands-on - Jan 12, 2016.
Instead of proceeding with a mediocre analysis, join us for this 2-part webinar series. We will show you how modern algorithms can take your regression model to the next level and expertly handle your modeling woes
- TMA Predictive Analytics Data Mining Training, [Orlando, Feb 18-26] - Jan 12, 2016.
Successful analytics in the big data era does not start with data and software, but with hands-on, immersive training and goal-driven strategy - get it from The Modeling Agency in Orlando, February 18-26.
- History Meets Innovation: Big Data Hackathon with Yad Vashem - Jan 12, 2016.
For 70 years the dedicated people at Yad Vashem have listened, gathered, protected and curated. Now we make their data come alive.
- Free audiobook – Revised & Updated “Predictive Analytics” - Jan 12, 2016.
Get the Revised and Updated Edition of Eric Siegel’s acclaimed Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. Free audiobook with order,
- Free Online Course: Statistical Learning - Jan 12, 2016.
With a free MOOC from Stanford, dive into statistical learning with the respected professors who literally wrote the book on it.
- Wikipedia Mining reveals hidden Revolution of Human Priorities - Jan 12, 2016.
Wikipedia data mining may reveal changes over time in the human perception of the world, and may also serve as an independent reliable quantitative method of investigation of historical events.
- Attention and Memory in Deep Learning and NLP - Jan 12, 2016.
An overview of attention mechanisms and memory in deep neural networks and why they work, including some specific applications in natural language processing and beyond.
- Predict. Share. Deploy. With Open Data Science – Jan 20 Webinar - Jan 12, 2016.
Building, tuning, sharing, deploying, and scaling predictive models is challenging, and rarely covered in statistics class. Learn how to make data science work in the real world in this webinar.
- Top KDnuggets tweets, Jan 4-10: NIPS 2015 #DeepLearning Tutorial by top researchers; Scientific way to cut pizza into equal slices will make you angry - Jan 11, 2016.
#NIPS2015 #DeepLearning Tutorial by top researchers Bengio, LeCun, Hinton; Understanding Rare Events and streak patterns; Most Influential Data Scientists on Twitter and Quora; 12 Tips for Data-Driven Research.
- MapR CEO 5 Big Data Predictions for 2016 - Jan 11, 2016.
The industry is in the midst of the biggest change in enterprise computing in decades. Schroeder sees an acceleration in big data deployments, and has crystallized his view of market trends into these five major predictions for 2016.
- And the hot topic Eric Siegel’s speaking on this year is… - Jan 11, 2016.
Eric will be delivering his keynote as part of the PAW Business San Francisco agenda on Weird Science: How to Know Your Predictive Discovery Is Not BS. Save with code KDN150.
- What To Expect from Deep Learning in 2016 and Beyond - Jan 11, 2016.
Predictions from some of the top names in deep learning, including Ilya Sutskever and Andrej Karpathy, about what to expect in the field over the next 5 years.
- 7 Steps to Understanding Deep Learning - Jan 11, 2016.
There are many deep learning resources freely available online, but it can be confusing knowing where to begin. Go from vague understanding of deep neural networks to knowledgeable practitioner in 7 steps!
- Top stories for Jan 3-9: 20 Questions to Detect Fake Data Scientists; 5 More arXiv Deep Learning Papers, Explained - Jan 10, 2016.
20 Questions to Detect Fake Data Scientists; 5 More arXiv Deep Learning Papers, Explained; TensorFlow is Terrific - A Sober Take on Deep Learning; 55 upcoming January - September Meetings.
- A Non-comprehensive List of Awesome Things other People Did in 2015 - Jan 9, 2016.
A top statistics professor and statistical researcher reflects on a number of awesome accomplishments by individuals in, and related to, the fields of statistics and data science, with a focus on the world of academia but with resonance far beyond.
- Understanding Rare Events and Anomalies: Why streaks patterns change - Jan 8, 2016.
We often look back at the past year and an overall history of rare events, and try to then extrapolate future odds of the same rare event, based on that. We illustrate here, that rare past events have no usefulness in understanding the rarity of the same events in the future!
- OpenText Data Digest, Jan 5: Life and Expectations - Jan 8, 2016.
New year has just begun and for the year ahead is a good opportunity to consider the passage of time, how much is left to each of us. We’re presenting some of the best visualizations of lifespans and life expectancy.
- Data Science Resume Tips and Guidelines - Jan 8, 2016.
A well-built resume is key to get through the first door – in the process of getting hired as a Data Scientist. Learn more, about how to present yourself as a true DS and which pitfalls to avoid.
- Improve your processes with statistical models - Jan 7, 2016.
Through real-world case studies, this technical primer will help you: find best practices to interactively explore the patterns in your data, build useful statistical models, and visually interact with these models.
- New Year, New Predictive Analytics World Events - Jan 7, 2016.
New, fresh Predictive Analytics World events feature prominent predictive analytics experts to learn from. Join us this spring in San Francisco as part of the leading, world-renowned events in predictive analytics. Use code KDN150 to save on top of early bird rates.
- Summer School on Integrating Vision and Language with Deep Learning, March 21-24, Malta - Jan 7, 2016.
Through lectures and hands-on sessions, the summer school will cover the state of the art in using deep learning methods within our two disciplines, Computer Vision and Natural Language Processing. The school is open to anyone interested, free of charge. Researchers resident in COST countries are eligible to apply for financial support.
- AMA Data Scientist, Jan 13: Jake Porway of DataKind - Jan 7, 2016.
Jake Porway is a machine learning and technology enthusiast, and founder of DataKind nonprofit which helps organizations use the power of data science in the service of humanity. He will do Reddit AMA on Jan 13, 2016.
- Prove Your Point with Data and a Fast Python Library - Jan 7, 2016.
Harness the power of Python and the command line to prove your point using data and a fast data-processing library.
- Top 5 Deep Learning Resources, January - Jan 7, 2016.
There is an increasing volume of deep learning research, articles, blog posts, and news constantly emerging. Our Deep Learning Reading List aims to make this information easier to digest.
- Top December stories: Top 10 Machine Learning Projects on Github; 50 Useful Machine Learning, Prediction APIs - Jan 6, 2016.
Top 10 Machine Learning Projects on Github; 50 Useful Machine Learning, Prediction APIs; Free Data Science Curriculum; The Star Wars social networks - who is the central character?
- AI + ML + NLP = Virtual Assistant Summit, Jan 28-29, San Francisco - Jan 6, 2016.
AI, machine learning, speech recognition and NLP technologies are converging to allow creation of Intelligent Virtual Assistants, explored in the first ever Virtual Assistant Summit, Jan 28-29, in SF. Use code KDNUGGETS to save 20% off.
- How Data Science Predicts and Reduces Adverse Birth Outcomes - Jan 6, 2016.
Here we look at project from Chicago University’s Data Science for Social Good (DSSG) Program on Predicting and Reducing Adverse Birth Outcomes.
- Free Book Download: Statistical Learning with Sparsity: The Lasso and Generalizations - Jan 6, 2016.
We witness an explosion of Big Data in finance, biology, medicine, marketing, and other fields. This book describes the important statistical ideas for learning from large and sparse data in a common conceptual framework.
- The Case Against Quick Wins in Predictive Analytics Projects - Jan 6, 2016.
While “quick wins” are desirable, getting them in a predictive project can be difficult. We review 2 major obstacles to quick wins in predictive analytics projects.
- Nando de Freitas AMA: Bayesian Deep Learning, Justice, and the Future of AI - Jan 6, 2016.
During his recent AMA, machine learning star Nando de Freitas answers a host of questions on a number of topics, including Bayesian methods in deep learning, harnessing AI for the good of humanity, and what the future holds for machine learning.
- Strata + Hadoop World San Jose, Mar 28-31, Best Price ends Jan 15 - Jan 5, 2016.
Strata + Hadoop World is the leading event on how big data and ubiquitous, real-time computing is shaping the course of business and society. Get KDnuggets discount to Strata + Hadoop World San Jose.
- Course: Big Data Processing with Hadoop & Spark, starts Jan 19, NYC - Jan 5, 2016.
Learn Hadoop and Spark, two key Big Data technologies, with an evening course in New York City, starting Jan 19. Special KDnuggets discount with code 09P8W01CUP7B.
- Portable Format for Analytics: moving models to production - Jan 5, 2016.
There are many ways to compute the best solution to a problem, but not all of them can be put into production. The Portable Format for Analytics (PFA) provides a way of formalizing and moving models.
- How Data Science reduces maternal mortality in Mexico - Jan 5, 2016.
Here we look at another project from Chicago University’s Data Science for Social Good (DSSG) Program which helps make the world a better place.
- 5 More arXiv Deep Learning Papers, Explained - Jan 5, 2016.
Top recent deep learning papers on arXiv are presented, summarized, and explained with the help of a leading researcher in the field.
- FICO Chief Analytics Officer 2016 Predictions - Jan 5, 2016.
NASA Juno mission will arrive at Jupiter. The Summer Olympics will take place in Rio de Janeiro. The US will have a presidential election. And prescriptive analytics will take center stage as the ultimate destination on the analytics journey.
- Top KDnuggets tweets, Dec 28 – Jan 03: TensorFlow is Terrific; Data Science in Python 100 Interview Questions - Jan 4, 2016.
TensorFlow is Terrific - A Sober Take on Google Deep Learning; Data Science in Python 100 Interview Questions and Answers; 20 Questions to Detect Fake Data Scientists; There are only 5 questions #MachineLearning can answer.
- Predictive Power of Terror Alerts and Monkeys - Jan 4, 2016.
The terrorism threat advisory system was designed to give the public prior warning to when terrorist plots are about to unfold. However, the analysis shows that this system is not more helpful than monkey throwing a dart.
- How Data Science saves lives and helps combat obesity - Jan 4, 2016.
We look at projects from Chicago University Data Science for Social Good (DSSG) Program which help make the world a better place, and in particular at measure to help predict obesity.
- Top /r/MachineLearning Posts, December: The Secret Sauce, OpenAI, Google vs. Facebook - Jan 4, 2016.
December on /r/MachineLearning: Is TensorFlow Google's "secret sauce?", AI leaders unite, an extensive curated list of machine learning resources grows, Google vs. Facebook, and Deep Q Pong.
- Top stories for Dec 27 – Jan 2: The Art of Data Science: The Skills You Need; 20 Questions to Detect Fake Data Scientists - Jan 3, 2016.
The Art of Data Science: The Skills You Need and How to Get Them; 20 Questions to Detect Fake Data Scientists; TensorFlow is Terrific - A Sober Take on Deep Learning Acceleration; What questions can data science answer?
- 55 upcoming January – September Meetings in Analytics, Big Data, Data Mining, Data Science - Jan 2, 2016.
Coming soon: EGC 2016, RapidMiner Wisdom, #BALasVegas, #PASanDiego, WSDM 2016, KNIME Summit, Strata + Hadoop San Jose, PAW San Francisco, and many more.
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20 Questions to Detect Fake Data Scientists - Jan 1, 2016.
Hiring Data Scientists is no easy job, particularly when there are plenty of fake posers. Here is a handy list of questions to help separate the wheat from the chaff. - What questions can data science answer? - Jan 1, 2016.
There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.
20 Questions to Detect Fake Data Scientists