Jun
2016 Jul Opinions, Interviews, Reports
All (113) | Courses, Education (13) | Meetings (8) | News, Features (17) | Opinions, Interviews, Reports (31) | Software (8) | Tutorials, Overviews (32) | Webcasts & Webinars (4)
- Data Science of Visiting Famous Movie Locations in San Francisco
- Jul 30, 2016.
Using the Google Places API and IMDb API, we selected movie locations in The Golden City which every movie fan should visit while they are in town, and optimize sightseeing by solving the travelling salesman problem.
- Theoretical Data Discovery: Using Physics to Understand Data Science
- Jul 29, 2016.
Data science may be a relatively recent buzzword, but the collection of tools and techniques to which it refers come from a broad range of disciplines. Physics has a wealth of concepts to learn from, as evidenced in this piece.
- Build vs Buy – Analytics Dashboards
- Jul 29, 2016.
Read this post on choosing between available analytics dashboard options, and designing your own. Get an informed opinion.
- Barley, Hops, and Bayes: Predicting The World Beer Cup
- Jul 26, 2016.
This post covers predicting award counts by the United States in an international beer competition. Exploratory data analysis and Bayes methods are also supported.
- Why Do Deep Learning Networks Scale?
- Jul 25, 2016.
A discussion of what about deep learning architectures allows them to scale, and addresses some assumptions that often inhibit an understanding of this topic.
- What Has Pokemon Got To Do With Big Data?
- Jul 23, 2016.
For me, the millions of people around the world playing Pokémon last weekend (and crashing their servers on a regular basis) showed me a glimpse of the future. There may well be an opportunity for real-time Big Data - I will give you a glimpse.
- Machine Learning: Separating Hype From Reality
- Jul 22, 2016.
When it comes to business value and ROI, does machine learning live up tot he claims? We’ll explore a pure machine learning approach through the lens of a typical enterprise use case.
- Interesting Things I Learned at SciPy 2016
- Jul 21, 2016.
Learn about some interesting projects featured at SciPy 2016, brought to you by an attendee who put in the work to bring you this great list of projects.
- Introducing Cloud Hosted Deep Learning Models
- Jul 21, 2016.
Algorithmia introduces a solution for hosting and distributing locally-trained deep learning models on Algorithmia using GPUs in the cloud, where they become smart API endpoints for other developers to use.
- What the Next Generation of IoT Sensors Have in Store
- Jul 19, 2016.
This post is an overview of some of the next-generation IoT sensors, and what they could mean for our future.
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Why Big Data is in Trouble: They Forgot About Applied Statistics
- Jul 18, 2016.
This "classic" (but very topical and certainly relevant) post discusses issues that Big Data can face when it forgets, or ignores, applied statistics. As great of a discussion today as it was 2 years ago. - KDnuggets Interview: Inderpal Bhandari, IBM Global Chief Data Officer on 4 key ideas of Cognitive Computing
- Jul 17, 2016.
In this wide-ranging interview, we discuss the role of IBM global chief data officer, 4 key ideas of cognitive computing, risks of AI, IBM Data Science Experience, healthcare, basketball, sports analytics, and more.
- Data Mining Most Vexing Problem Solved, or is this drug REALLY working?
- Jul 15, 2016.
This is a summary of the basic principle behind a new paper on multiple test correction for streams and cascades of statistical hypothesis tests, showing how to strictly control the risk of making a mistake over a series of tests and draw appropriate conclusions.
- 4 Major Trends Disrupting the Data Science Market
- Jul 15, 2016.
An interesting excerpt from Burtch Works' recently published Burtch Works Study: Salaries of Data Scientists 2016, focusing on trends disrupting the data science market.
- 2016’s Best Places for Data Scientist Jobs
- Jul 15, 2016.
Get the info on the Best Places in the U.S. for Data Scientist Jobs with GoodCall's new data-driven report.
- 10 Algorithm Categories for AI, Big Data, and Data Science
- Jul 14, 2016.
With a focus on leveraging algorithms and balancing human and AI capital, here are the top 10 algorithm categories used to implement A.I., Big Data, and Data Science.
- What Data Scientists Can Learn From Qualitative Research
- Jul 14, 2016.
Learn what data scientists can learn from qualitative researchers when it comes to analysing text, and how this relates to writing quality code.
- What do Postgres, Kafka, and Bitcoin Have in Common?
- Jul 13, 2016.
These three technologies on the surface couldn't look any more different, but under the hood they have one interesting thing in common.
- A Survey of Available Corpora for Building Data-driven Dialogue Systems
- Jul 12, 2016.
This post is a summary of Serban, et al. "A Survey of Available Corpora for Building Data-Driven Dialogue Systems," which is of increasing relevance given the recent state of conversational AI.
- The Hard Problems AI Can’t (Yet) Touch
- Jul 11, 2016.
It's tempting to consider the progress of AI as though it were a single monolithic entity, advancing towards human intelligence on all fronts. But today's machine learning only addresses problems with simple, easily quantified objectives
- Top Machine Learning MOOCs and Online Lectures: A Comprehensive Survey
- Jul 11, 2016.
This post reviews Machine Learning MOOCs and online lectures for both the novice and expert audience.
- Big Data, Bible Codes, and Bonferroni
- Jul 8, 2016.
This discussion will focus on 2 particular statistical issues to be on the look out for in your own work and in the work of others mining and learning from Big Data, with real world examples emphasizing the importance of statistical processes in practice.
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part Two
- Jul 8, 2016.
Read some impressions from onsite visits to 2 companies during Strata Silicon Valley in March: Novumind and Numenta.
- Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists
- Jul 7, 2016.
Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.
- Glimpses & Impressions: Strata Silicon Valley AI + ML Review – Part One
- Jul 7, 2016.
Read some impressions from a visit to Strata Silicon Valley in March. The focus is on integration of data science and machine learning tools, as well as the simplification of related processes.
- Storytelling: The Power to Influence in Data Science
- Jul 6, 2016.
Data scientists need to share results, which is different than talking shop with other data scientists. Read about influencing people and telling stories as a data scientist.
- Success Criteria for Process Mining
- Jul 6, 2016.
This article provides tips about the pitfalls and advice that will help you to make your first process mining project as successful as it can be.
- 3 Key Ethics Principles for Big Data and Data Science
- Jul 6, 2016.
If ethics in general are important, should ethics training be a crucial element of the data science field?
- Getting Started with Analytics: What’s the Upfront Investment?
- Jul 5, 2016.
Everyone wants to leverage analytics, but should everyone dive into the deep end right away? Heed some sensible advice on getting started with analytics, and assessing the true upfront investment.
- Data Mining History: The Invention of Support Vector Machines
- Jul 4, 2016.
The story starts in Paris in 1989, when I benchmarked neural networks against kernel methods, but the real invention of SVMs happened when Bernhard decided to implement Vladimir Vapnik algorithm.
- Three Impactful Machine Learning Topics at ICML 2016
- Jul 1, 2016.
This post discusses 3 particular tutorial sessions of impact from the recent ICML 2016 conference held in New York. Check out some innovative ideas on Deep Residual Networks, Memory Networks for Language Understanding, and Non-Convex Optimization.
fastcore: An Underrated Python Library
Goodhart’s Law for Data Science and what happens when a measure becomes a target?