May
2016 Jun
All (119) | Courses, Education (6) | Meetings (12) | News, Features (21) | Opinions, Interviews, Reports (23) | Software (10) | Tutorials, Overviews (40) | Webcasts & Webinars (7)
- Recursive (not Recurrent!) Neural Networks in TensorFlow - Jun 30, 2016.
Learn how to implement recursive neural networks in TensorFlow, which can be used to learn tree-like structures, or directed acyclic graphs.
- Determining the Economic Value of Data - Jun 30, 2016.
This post introduces a data economic valuation process that uses an organization’s key business initiatives as this basis for establishing prudent value.
- Big Data into Big Profits: Northwestern Executive Education Course, Aug 25-26, San Francisco - Jun 30, 2016.
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.
- Top KDnuggets tweets, Jun 22-28: #Bayesian #Statistics explained in Simple English; Brexit - Jun 29, 2016.
#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.
- Peeking Inside Convolutional Neural Networks - Jun 29, 2016.
This post discusses using some tricks to peek inside of the neural network, and to visualize what the individual units in a layer detect.
- Mining Twitter Data with Python Part 5: Data Visualisation Basics - Jun 29, 2016.
Part 5 of this series takes on data visualization, as we look to make sense of our data and highlight interesting insights.
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The Big Data Ecosystem is Too Damn Big - Jun 28, 2016.
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? - An Inside Update on Natural Language Processing - Jun 28, 2016.
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).
- Webinar, Jun 30: Introducing Anaconda Mosaic: Visualize. Explore. Transform. Once and Done - Jun 28, 2016.
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!
- 5 More Machine Learning Projects You Can No Longer Overlook - Jun 28, 2016.
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.
- Top Stories, June 20-26: New Machine Learning Book, Free Draft Chapters; Machine Learning Trends & Future of A.I. - Jun 27, 2016.
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
- BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark - Jun 27, 2016.
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.
- Mining Twitter Data with Python Part 4: Rugby and Term Co-occurrences - Jun 27, 2016.
Part 4 of this series employs some of the lessons learned thus far to analyze tweets related to rugby matches and term co-occurrences.
- Improving Nudity Detection and NSFW Image Recognition - Jun 25, 2016.
This post discussed improvements made in a tricky machine learning classification problem: nude and/or NSFW, or not?
- Regularization in Logistic Regression: Better Fit and Better Generalization? - Jun 24, 2016.
A discussion on regularization in logistic regression, and how its usage plays into better model fit and generalization.
- Doing Data Science: A Kaggle Walkthrough Part 6 – Creating a Model - Jun 24, 2016.
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.
- Top Machine Learning Libraries for Javascript - Jun 24, 2016.
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.
- Predictive Analytics World in October: Government, Business, Financial, Healthcare - Jun 24, 2016.
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.
- Ten Simple Rules for Effective Statistical Practice: An Overview - Jun 23, 2016.
An overview of 10 simple rules to follow to ensure proper effective statistical data analysis.
- From Research to Riches: Data Wrangling Lessons from Physical and Life Science - Jun 23, 2016.
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.
- Achieving End-to-end Security for Apache Spark with Databricks - Jun 23, 2016.
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.
- Predicting purchases at retail stores using HPE Vertica and Dataiku DSS - Jun 23, 2016.
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.
- Top KDnuggets tweets, Jun 15-21: Predicting UEFA Euro2016; Visual Explanation of Backprop for Neural Nets - Jun 22, 2016.
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, New York City, Sep 26-29 – KDnuggets discount - Jun 22, 2016.
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.
- Cisco 2016 Data and Analytics Conference, Sep 19-21, Chicago - Jun 22, 2016.
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.
- Machine Learning Trends and the Future of Artificial Intelligence - Jun 22, 2016.
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.
- Mining Twitter Data with Python Part 3: Term Frequencies - Jun 22, 2016.
Part 3 of this 7 part series focusing on mining Twitter data discusses the analysis of term frequencies for meaningful term extraction.
- History of Data Mining - Jun 22, 2016.
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.
- KDnuggets Blog Contest: Automated Data Science and Machine Learning - Jun 21, 2016.
KDnuggets Contest for interesting blogs about Automated Data Science and Machine Learning is over - read the 3rd, 2nd, and 1st place winners.
- Data Science Career Days at Metis, NYC – June 23, SF – June 30 - Jun 21, 2016.
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.
- A Review of Popular Deep Learning Models - Jun 21, 2016.
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 Text Extraction API Cheat Sheet for Developers - Jun 21, 2016.
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.
- Standards-based Deployment of Predictive Analytics - Jun 21, 2016.
Using a standards-based approach to deploy predictive analytics on operational systems from mainframes to Hadoop.
- Data Science for Internet of Things course, Online or London - Jun 21, 2016.
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.
- How to Compare Apples and Oranges, Part 2 – Categorical Variables - Jun 21, 2016.
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.
- Top Stories, June 13-19: A Visual Explanation of the Back Propagation Algorithm; Apache Spark Key Terms, Explained - Jun 20, 2016.
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
- Chief Data Officer Forum Insurance 2016, Sep 15, Chicago - Jun 20, 2016.
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.
- Chief Analytics Officer Forum, Oct 4-6, New York, NY - Jun 20, 2016.
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.
- New Andrew Ng Machine Learning Book Under Construction, Free Draft Chapters - Jun 20, 2016.
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.
- Mining Twitter Data with Python Part 2: Text Pre-processing - Jun 20, 2016.
Part 2 of this 7 part series on mining Twitter data for a variety of use cases focuses on the pre-processing of tweet text.
- Does More Data Make Your System Smarter? Ontotext Webinars, June 23, July 7 - Jun 20, 2016.
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.
- Learn From PAW Chicago Speakers & Save on PAW New York - Jun 20, 2016.
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.
- What is Your Data Worth? On LinkedIn, Microsoft, and the Value of User Data - Jun 20, 2016.
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.
- Political Data Science: Analyzing Trump, Clinton, and Sanders Tweets and Sentiment - Jun 18, 2016.
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!
- A Visual Explanation of the Back Propagation Algorithm for Neural Networks - Jun 17, 2016.
A concise explanation of backpropagation for neural networks is presented in elementary terms, along with explanatory visualization.
- Doing Data Science: A Kaggle Walkthrough Part 5 – Adding New Data - Jun 17, 2016.
Here is part 5 of the weekly 6 part series on doing data science in the context of a Kaggle competition, which concentrates on adding in new data.
- How to Compare Apples and Oranges – Part 1 - Jun 17, 2016.
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.
- Connecting Data Systems and DevOps - Jun 17, 2016.
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.
- How open API economy accelerates the growth of big data and analytics - Jun 17, 2016.
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.
- Thinking About Analytics Readiness - Jun 16, 2016.
This article touches upon an important but under-discussed topic of analytics readiness, including whether and when organizations should engage in analytics.
- Nutrition & Principal Component Analysis: A Tutorial - Jun 16, 2016.
A great overview of Principal Component Analysis (PCA), with an example application in the field of nutrition.
- Webinar, Jun 24: Using Text Mining to Improve Patient Care - Jun 16, 2016.
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.
- Webinar, Jun 24: Bring Open Data Science Into Excel with Anaconda Fusion - Jun 16, 2016.
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!
- 7 Steps to Mastering SQL for Data Science - Jun 16, 2016.
Follow these 7 steps to go from SQL data science newbie to seasoned practitioner quickly. No nonsense, just the necessities.
- Top KDnuggets tweets, Jun 8-14: All-in-one Docker image for Deep Learning; Good Book list for Data lovers - Jun 15, 2016.
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;
- How Much Will A.I. Surprise Us? - Jun 15, 2016.
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?
- Figuring Out the Algorithms of Intelligence - Jun 15, 2016.
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.
- Mining Twitter Data with Python Part 1: Collecting Data - Jun 15, 2016.
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.
- What Big Data, Data Science, Deep Learning software goes together? - Jun 14, 2016.
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.
- 10 Useful Python Data Visualization Libraries for Any Discipline - Jun 14, 2016.
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.
- Data Science Summit, July 12-13, San Francisco – KDnuggets offer - Jun 14, 2016.
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.
- 10 Data Acquisition Strategies for Startups - Jun 14, 2016.
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).
- Machine Learning Classic: Parsimonious Binary Classification Trees - Jun 14, 2016.
Get your hands on a classic technical report outlining a three-step method of construction binary decision trees for multiple classification problems.
- Crowdfunding Analytics = New Revelations Ahead - Jun 14, 2016.
CrowdSurfer helps to analyze investments financed through crowdfunding and marketplace lending, but there is more than meets the eye.
- Webcast: Learn how statisticians can work across disciplines. - Jun 13, 2016.
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.
- Top Stories, June 6-12: Data Science of Variable Selection; R, Python Duel As Top Analytics, Data Science Software - Jun 13, 2016.
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?
- How to Select Support Vector Machine Kernels - Jun 13, 2016.
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.
- Apache Spark Key Terms, Explained - Jun 13, 2016.
An overview of 13 core Apache Spark concepts, presented with focus and clarity in mind. A great beginner's overview of essential Spark terminology.
- PPMI Data Challenge 2016 – Help Solve Parkinsons Disease - Jun 13, 2016.
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.
- A Brief Primer on Linear Regression – Part 2 - Jun 13, 2016.
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.
- Metis Data Science Open House, Jun 13, New York City - Jun 10, 2016.
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.
- Data Insight Leaders Summit, Barcelona, 12-13 Oct - Jun 10, 2016.
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.
- AIG & Zurich on Machine Learning in Insurance - Jun 10, 2016.
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.
- Project Murphy Microsoft Bot Framework AI - Jun 10, 2016.
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.
- Doing Data Science: A Kaggle Walkthrough Part 4 – Data Transformation and Feature Extraction - Jun 10, 2016.
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.
- Build Your Own Audio/Video Analytics App With HPE Haven OnDemand – Part 2 - Jun 10, 2016.
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.
- Top NoSQL Database Engines - Jun 10, 2016.
An overview of the top 5 NoSQL database engines in use today, including examples of key-value, column-oriented, graph, and document paradigms.
- An Introduction to Scientific Python (and a Bit of the Maths Behind It) – Matplotlib - Jun 9, 2016.
An introductory overview of Matplotlib, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
- Cloud Computing Key Terms, Explained - Jun 9, 2016.
A concise overview of 20 core cloud computing ecosystem concepts. The focus here is on the terminology, not The Big Picture.
- Whitepaper: The Journey to Open Data Science - Jun 9, 2016.
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.
- Build Your Own Audio/Video Analytics App With HPE Haven OnDemand – Part 1 - Jun 9, 2016.
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.
- 5 Best Practices for Big Data Security - Jun 9, 2016.
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.
- Chief Data & Analytics Officer Forum, Hong Kong, 4-5 October - Jun 8, 2016.
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%.
- Top KDnuggets tweets, Jun 1-7: “Deep” vs “Regular” Machine Learning; Introduction to Scientific Python – NumPy - Jun 8, 2016.
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?
- Where are the Opportunities for Machine Learning Startups? - Jun 8, 2016.
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.
- How Do You Identify the Right Data Scientist for Your Team? - Jun 8, 2016.
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.
- Deep Learning, Pachinko, and James Watt: Efficiency is the Driver of Uncertainty - Jun 8, 2016.
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 Arrives to Speed Business Insights - Jun 8, 2016.
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.
- Algorithms for Modern Massive Data Sets (MMDS), UC Berkeley, June 21-24 – Register until June 12 - Jun 7, 2016.
MMDS 2016 will address algorithmic, mathematical, and statistical challenges in modern statistical data analysis. Register until June 12.
- Data Science of Variable Selection: A Review - Jun 7, 2016.
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.
- Big Data Business Model Maturity Index and the Internet of Things (IoT) - Jun 7, 2016.
This post explores how organizations could use the Big Data Business Model Maturity Index (BDBMMI) to exploit the Internet of Things (IoT).
- Strategize Your Data Capabilities to Maximize Business Performance - Jun 7, 2016.
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.
- Webinar: Predictive Analytics: Failure to Launch [June 8] - Jun 7, 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 June 8.
- Academic/Research positions in Business Analytics, Data Science, Machine Learning in May 2016 - Jun 6, 2016.
Academic/Research positions Analytics and Data Science in Los Angeles-CA, Cardiff-Wales, and Oslo-Norway.
- R, Python Duel As Top Analytics, Data Science software – KDnuggets 2016 Software Poll Results - Jun 6, 2016.
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.
- The Truth About Deep Learning - Jun 6, 2016.
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.
- Top Stories, May 30 – June 6: Difference Between Deep Learning and “Regular” Machine Learning; Introduction to Numpy - Jun 6, 2016.
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
- Open Source Machine Learning Degree - Jun 6, 2016.
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.
- A Brief Primer on Linear Regression – Part 1 - Jun 6, 2016.
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.
- Ethics in Machine Learning – Summary - Jun 6, 2016.
Still worried about the AI apocalypse? Here we are discussion about the constraints and ethics for the machine learning algorithms to prevent it.
- Master in Business Analytics & Big Data: In Madrid or online - Jun 6, 2016.
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.
- Top May stories: What software you used for Analytics, Data Mining, Data Science? - Jun 5, 2016.
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.
- The Benefits of Decentralizing Analytics Talent - Jun 4, 2016.
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.
- Open Data Science in Collaborative Workflows – IBM June 6 event - Jun 3, 2016.
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.
- Webinar: Using Uplift Modeling to Influence and Persuade, June 29 - Jun 3, 2016.
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.
- Building Data Systems: What Do You Need? - Jun 3, 2016.
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.
- What is the Difference Between Deep Learning and “Regular” Machine Learning? - Jun 3, 2016.
Another concise explanation of a machine learning concept by Sebastian Raschka. This time, Sebastian explains the difference between Deep Learning and "regular" machine learning.
- Doing Data Science: A Kaggle Walkthrough Part 3 – Cleaning Data - Jun 3, 2016.
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.
- WCAI Research Opportunity: Understanding Economic Behaviors for Financial Products – deadline June 12 - Jun 2, 2016.
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.
- Understanding Modern Data Systems - Jun 2, 2016.
A look at the four characteristics that differentiate data infrastructure development from traditional development, and the key issues to look out for.
- Upcoming June – December Meetings in Analytics, Big Data, Data Mining, Data Science - Jun 2, 2016.
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.
- Do You Need Big Data or Smart Data? Part 2 - Jun 2, 2016.
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.
- How to Build Your Own Deep Learning Box - Jun 2, 2016.
Want to build an affordable deep learning box and get all the required software installed? Read on for a proper overview.
- Top KDnuggets tweets, May 25-31: 19 Free eBooks to learn #programming with #Python; Awesome collection of public datasets on Github - Jun 1, 2016.
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 Introduction to Scientific Python (and a Bit of the Maths Behind It) – NumPy - Jun 1, 2016.
An introductory overview of NumPy, one of the foundational aspects of Scientific Computing in Python, along with some explanation of the maths involved.
- Do You Need Big Data or Smart Data? Part 1 - Jun 1, 2016.
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.
- Engineering Intelligence Through Data Visualization at Uber - Jun 1, 2016.
An overview of how Uber is using data visualization to help drive intelligence, directly from the Uber data visualization team.
- Udacity Nanodegree Programs: Machine Learning, Data Analyst, and more - Jun 1, 2016.
Develop new skills. Be in demand. Accelerate your career with the credential that fast-tracks you to career success.
- Top /r/MachineLearning Posts, May: TensorFlow Tricks; Machine Learning Tutorials; Google TPUs - Jun 1, 2016.
May on /r/MachineLearning was all about tutorials, TensorFlow, Google hardware, Deep Learning machine installations, and some laughs.
