This post introduces a data economic valuation process that uses an organization’s key business initiatives as this basis for establishing prudent value.
Join Northwestern Executive Education Course "Big Data to Big Profits: Strategies for Monetizing Social, Mobile, and Digital Data with Data Science", Aug 25-26, in San Francisco.
#Bayesian #Statistics explained to Beginners in Simple English; Amazing analysis of #Brexit with #MachineLearning - it is sad; 18 Useful Mobile Apps for #DataScientist; Sharp divisions between England, #Scotland in #Brexit vote suggest future UK split.
The Big Data ecosystem is just too damn big! It's complex, redundant, and confusing. There are too many layers in the technology stack, too many standards, and too many engines. Vendors? Too many. What is the user to do?
This article is an interview with computational linguist Jason Baldridge. It's a good read for data scientists, researchers, software developers, and professionals working in media, consumer insights, and market intelligence. It's for anyone who's interested in, or needs to know about, natural language processing (NLP).
On June 30th, Continuum Analytics Product Manager Lance Ransom will showcase how Anaconda Mosaic can empower your organization to light up your dark data. Save your spot now!
There are a lot of popular machine learning projects out there, but many more that are not. Which of these are actively developed and worth checking out? Here is an offering of 5 such projects.
New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters; Machine Learning Trends and the Future of Artificial Intelligence; Top Machine Learning Libraries for Javascript; 7 Steps to Mastering Machine Learning With Python
An overview of a recent paper outlining BigDebug, which provides real-time interactive debugging support for Data-Intensive Scalable Computing (DISC) systems, or more particularly, Apache Spark.
In the final part of this 6 part series on the process of data science, and applying it to a Kaggle competition, building the predictive models is covered, and multiple algorithms are discussed.
Javascript may not be the conventional choice for machine learning, but there is no reason it cannot be used for such tasks. Here are the top libraries to facilitate machine learning in Javascript.
Plan ahead and save on Predictive Analytics World conferences this October. Early bird savings are still available, and save extra with with KDnuggets code KDN150.
With a background in bioinformatics, Christian discusses his recent transition to the world of data science and the learning curve associated with this dynamic field.
The Databricks just-in-time data platform takes a holistic approach to solving the enterprise security challenge by building all the facets of security — encryption, identity management, role-based access control, data governance, and compliance standards — natively into the data platform with DBES.
The retail industry has been data centric for a while. With the rise of loyalty programs and digital touch points, retailers have been able to collect more and more data about their customers over time, opening up the ability to create better personalized marketing offers and promotions.
Building statistical model to predict UEFA #Euro2016; A Visual Explanation of Back Propagation Algorithm for #NeuralNetworks; Scala is the new golden child for coding and #DataScience.
Strata + Hadoop World is where cutting-edge science and new business fundamentals intersect-and merge. It's a deep dive into emerging techniques and technologies. Get 20% off with code PCKDNG.
Learn how Cisco, its partners, and customers are developing and using new solutions for data and analytics, IoT, cloud, edge analytics, data preparation and data virtualization. Register before Aug 19 to get early bird rates.
The confluence of data flywheels, the algorithm economy, and cloud-hosted intelligence means every company can now be a data company, every company can now access algorithmic intelligence, and every app can now be an intelligent app.
Data mining is a subfield of computer science which blends many techniques from statistics, data science, database theory and machine learning. Here are the major milestones and “firsts” in the history of data mining plus how it’s evolved and blended with data science and big data.
In 12 week intensive program, Metis data science students build five projects using machine learning and statistical modeling techniques in Python, industry-level visualizations in D3, and real-world data in cloud-based SQL, no-SQL, and Hadoop databases.
This post is a concise overview of a few of the more interesting popular deep learning models to have appeared over the past year. Get up to speed and try a few of the models out for yourself.
HPE Haven OnDemand provides a native API based on cURL calls, as well as numerous language-specific APIs, providing maximum flexibility for developers. This cheat sheet will cover the native and Python text extraction APIs.
Now it is 4th run, the "Data Science for Internet of Things" course is designed to prepare you for the role of a Data Scientist for the Internet of Things(IoT) domain. The course starts in Aug – Sep 2016 , online or in London.
In the previous article, we looked at some of the ways to compare different numerical variables. In this article, we shall look at techniques to compare categorical variables with the help of an example.
A Visual Explanation of the Back Propagation Algorithm for Neural Networks; Apache Spark Key Terms, Explained; What Big Data, Data Science, Deep Learning software goes together?; 10 Data Acquisition Strategies for Startups; 7 Steps to Mastering Machine Learning With Python
The CDO Insurance Forum will establish a focal point of discussion for CDOs, CAOs and senior data professionals to evaluate the evolving demands of big data and analytics in Insurance space. Use code CDOINSUR to save when registering.
Two days of networking, high level insight and discussion on hottest topics and challenges faced by CAOs and Senior Analytics professionals. Attend also pre-conference focus day on Machine Learning, Deep Learning and AI for Strategic Innovation.
Check out the details on Andrew Ng's new book on building machine learning systems, and find out how to get your free copy of draft chapters as they are written.
Jun 23 webinar shows how pouring more data to your system can actually make it smarter. July webinar shows how to quickly prototype with Ontotext Dynamic Semantic Publishing platform on AWS, using your own content.
Read exclusive interviews with PAW Chicago speakers on advanced data and analytics techniques and get early bird tickets for PAW New York in September. Use KDN150 for extra savings.
The recent announcement of Microsoft’s acquisition of LinkedIn has raised many questions about how Microsoft will monetize this data. We examine LinkedIn value per user and compare to Google, Facebook, Yahoo, and Twitter.
This post shares some results of political text analytics performed on Twitter data. How negative are the US Presidential candidate tweets? How does the media mention the candidates in tweets? Read on to find out!
We are always told that apples and oranges can’t be compared, they are completely different things. Learn as an analyst, how you deal with such difference and make sense of it on a daily basis.
This post will explain why anyone transforming their company into a data-driven organization should care about software development best practices, even if they don’t consider themselves a software company.
An open API is available on the internet for free. We review the growth of API economy and how organizations have been realizing the potential of open APIs in transforming their business.
This article touches upon an important but under-discussed topic of analytics readiness, including whether and when organizations should engage in analytics.
Learn about a novel use case of applying text mining tools during and after patient rounds in a hospital, including use of text mining on a tablet computer to extract information to aid physicians on their daily visits to patients.
On June 24th, find out how to use Anaconda Fusion, part of the Anaconda Platform, to bring the power of Open Data Science into Excel. Reserve your spot now!
Good Book list for #Data lovers; OpenAI - a living collection of important and fun problems; All-in-one #Docker image for #DeepLearning; 10 Useful #Python #DataVisualization Libraries for Any Discipline;
Why think about what neural networks (and AI in general) can do that we can already do, when he real question that we should be asking is this: What will A.I. be able to do that we can’t even dream of?
Marvin Minsky, the father of AI, passed away this year. One of his inventions was the confocal microscope, which we used to take this high-resolution picture of a live brain circuit. Something in these cells allows them to automatically identify useful connections and establish useful networks out of information.
Part 1 of a 7 part series focusing on mining Twitter data for a variety of use cases. This first post lays the groundwork, and focuses on data collection.
We analyze the associations between top Data Science tools, Commercial vs Free/Open Source, rank tools on R vs Python bias, find tools more associated with Big Data, those more associated with Deep Learning, and uncover strong regional differences.
A great overview of 10 useful Python data visualization tools. It covers some of the big ones, like matplotlib and Seaborn, but also explores some more obscure libraries, like Gleam, Leather, and missingno.
The Data Science Summit is packed with industry experts, authors, researchers and business leaders delivering concrete examples of data science and machine learning in action. Use kdnuggets15 to save.
An interesting discussion of the myriad methods in which startups may choose to acquire data, often the most overlooked and important aspect of a startup's success (or failure).
Learn why subject-matter experts are better off when they understand their data; how traditional statistics has missed an opportunity; why it takes a long time for some methods to gain popularity and more.
Data Science of Variable Selection; R, Python Duel As Top Analytics, Data Science Software; Big Data Business Model Maturity Index and the Internet of Things (IoT); Where are the Opportunities for Machine Learning Startups?
Support Vector Machine kernel selection can be tricky, and is dataset dependent. Here is some advice on how to proceed in the kernel selection process.
Help answer 2 key questions about Parkinson's disease and gather new insights into PD diagnosis and progression. MJFF and GE Healthcare are offering $50,000 in total prizes.
This second part of an introduction to linear regression moves past the topics covered in the first to discuss linearity, normality, outliers, and other topics of interest.
Visit Metis in New York City on June 13 at 6:30pm to see an Intro to Data Science presentation by Sergey Fogelson, creator and instructor of 6-week intro to Data Science course which starts in July.
Listen to experienced speakers - Heads of Data Science, Analytics and BI - sharing their first-hand experience on the do's and don'ts to successfully fast-track the right data experiments into actionable intelligence! Use code KDNUGGETS10 to save.
Where and how can machine learning be practically applied by insurers? And is it worth it? Read the white paper from insurance experts at AIG and Zurich.
With Microsoft AI-based Bot Framework you can add the bot on Skype, Messenger, Telegram, ... and ask it questions like: "What if Charlie Chaplin was a baby?" or "What if Beethoven was a rockstar!" The results are always fun.
Part 4 of this fantastic 6 part series covering the process of data science, and its application to a Kaggle competition, focuses on feature extraction and data transformation.
In the conclusion to this two part tutorial, learn how to leverage HPE Haven OnDemand's Machine Learning APIs to build an audio/video analytics app with minimal time and effort.
An introductory overview of Matplotlib, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
Learn why Open Data Science is the foundation to modernizing data analytics, and ways availability, interoperability, transparency and innovation are some of the most important benefits of the ODS approach.
In this first part of a two part tutorial, learn how to leverage HPE Haven OnDemand's Machine Learning APIs to build an audio/video analytics app with minimal time and effort.
Lack of data security can not only result in financial losses, but may also damage the reputation of organizations. Take a look at some of the most important data security best practices that can reduce the risks associated with analyzing a massive amount of data.
The Chief Data and Analytics Officer Forum Hong Kong will bring to the forefront, the core issues needed to be discussed, debated and challenged to facilitate this momentum toward greater data adoption. Use CDAOHKDN to save 15%.
How to Build Your Own #DeepLearning Box; What is the Difference Between #DeepLearning and "Regular" #MachineLearning? Data Science of #Variable Selection: A Review; Why choose #Python for #MachineLearning?
Machine learning has permeated data-driven businesses, which means almost all businesses. Here are a few areas where it’s possible that big corporations haven’t already eaten everybody’s lunch.
Have you been trying to answer the question of what type of a data scientist would be the best fit for your team? Is there a single all-encompassing answer or does it vary based on the client objectives? Read on for some insight.
A reasoned discussion of why the next generation of data efficient learning approaches rely on us developing new algorithms that can propagate stochasticity or uncertainty right through the model, and which are mathematically more involved than the standard approaches.
Infinite Data Overlap Detection(IDOD) is a new, Spark-based technology that empowers non-technical business users to automatically discover data patterns and blendany data type for any set of values from multiple sources – both inside and outside the enterprise.
There are as many approaches to selecting features as there are statisticians since every statistician and their sibling has a POV or a paper on the subject. This is an overview of some of these approaches.
Attend Big Data Innovation Summit on Sep 8-9 in Boston and learn how to organize your data science team, increase productivity, construct an effective data strategy, and use the most advanced data tools and technologies.
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 June 8.
R remains the leading tool, with 49% share, but Python grows faster and almost catches up to R. RapidMiner remains the most popular general Data Science platform. Big Data tools used by almost 40%, and Deep Learning usage doubles.
An honest look at deep learning, what it is not, its advantages over "shallow" neural networks, and some of the common assumptions and conflations that surround it.
Difference Between Deep Learning and “Regular” Machine Learning; An Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy; How to Build Your Own Deep Learning Box; Interacting with Machine Learning - Here is Why You Should Care
A set of free resources for learning machine learning, inspired by similar open source degree resources. Find links to books and book-length lecture notes for study.
This introduction to linear regression discusses a simple linear regression model with one predictor variable, and then extends it to the multiple linear regression model with at least two predictors.
The Master in Business Analytics & Big Data is an innovative gateway degree that is designed to train the new generation of business-oriented, analytical professionals who are in high demand by recruiters. Choose from full-time in Madrid or part-time in Madrid/Dubai + online.
Poll: What software you used for Analytics, Data Mining, Data Science? How to Explain Machine Learning to a Software Engineer; Meet 11 Big Data & Data Science Leaders on LinkedIn.
Over the next several years data will be served in a variety of ways, greater innovation will come from companies that look to share raw data. Here we talk about, democratizing the data which requires a different philosophy to allow all business functions to participate in analytics.
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.
Uplift modeling predicts what will influence a consumer to take the action you want. This free webinar from the Predictive Analytics World conference series gives an introduction into this rapidly growing area of data modeling.
This post shares some insight gained through years of building data-powered products, and discusses the capabilities you need to have in place in order to successfully build and maintain data systems and data infrastructure.
Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.
This is part three in a fantastic 6 part series covering the process of data science, and the application of the process to a Kaggle competition. In this episode, data cleaning and preparation is covered.
The Wharton Customer Analytics Initiative is offering two research opportunities: “Understanding Past, Present, and Future Economic Behaviors for Financial Products” and “Identifying and Maintaining Great Financial Advisors". The deadline for submissions is June 12.
A look at the four characteristics that differentiate data infrastructure development from traditional development, and the key issues to look out for.
Coming soon: Spark Summit - SF, Marketing Analytics and Data Science - SF, PAW Business Chicago, Social Computing, Behavioral-Cultural Modeling and Prediction - DC, Sentiment Analysis Symposium NYC, and more.
It can be easy to get carried away with the deluge of big data and to rely on its abundance to deliver better models. However, use of data without context and objective could prove counterproductive; contextual and objective driven samples from the large volume and variety of data can be effective tools.
Introducing Hybrid lda2vec Algorithm via Stitch Fix; #DeepLearning and Deep #Gaussian Processes - explainer; Awesome collection of public #datasets on Github; #DataScience foundations: 19 Free eBooks to learn #programming with #Python.
An introductory overview of NumPy, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
Analyzing Big Data without paying attention to its characteristics and objective can be detrimental, the fix for which can be correct and effective sampling. Read on to transform your Big Data to Smart Data.
The Big Data Ecosystem is Too Damn Big
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