May
2016 Jun Opinions, Interviews, Reports
All (119) | Courses, Education (6) | Meetings (12) | News, Features (21) | Opinions, Interviews, Reports (23) | Software (10) | Tutorials, Overviews (40) | Webcasts & Webinars (7)
- 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.
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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).
- 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.
- 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.
- 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.
- 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!
- 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.
- 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.
- 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).
- 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.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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.
Learn How to Listen: One of the hardest parts of being a data scientist
An Introduction to Scikit Learn: The Gold Standard of Python Machine Learning