Apr
2016 May Opinions, Interviews, Reports
All (103) | Courses, Education (3) | Meetings (12) | News, Features (26) | Opinions, Interviews, Reports (34) | Software (5) | Tutorials, Overviews (17) | Webcasts & Webinars (6)
- 5 Reasons Machine Learning Applications Need a Better Lambda Architecture
- Jun 2, 2016.
The Lambda Architecture enables a continuous processing of real-time data. It is a painful process that gets the job done, but at a great cost. Here is a simplified solution called as Lambda-R (ƛ-R) for the Relational Lambda.
- Intel’s Investments in Cognitive Tech: Impact and New Opportunities
- May 31, 2016.
An overview of Intel's recent investments in cognitive technology, the impact of these investments on technology and research, and the new opportunities these investments present.
- Beyond Big Data Skills: Creating the Right DNA for the Managers of the Data-driven Business World
- May 31, 2016.
Advice to schools and universities to help them prepare the future managers of the data-driven business world. One key step is to get managers acquainted with data by touching data, manipulating it, and 'playing' with it.
- Interacting with Machine Learning – Here is Why You Should Care
- May 30, 2016.
The issue of designing new interactive interfaces with machine learning systems that best serve our needs and help us build and maintain trust is a central issue in AI. Read one researcher's take on this topic.
- Automakers Must Partner Around Big Data
- May 26, 2016.
A discussion on the need for auto manufacturers to come together and leverage Big Data.
- Trust and Analytics in the Banking Sector
- May 26, 2016.
This post explores the intricate relationship between customers, trust, and analytics in the banking sector, and offer actions that banks may need to take to assess the way they assure trust across the analytics lifecycle.
- SAP Predictive Analytics Interview with Sven Bauszus
- May 26, 2016.
I talk with Sven Bauszus, SAP Predictive Analytics global leader, about their main products in Business and Predictive Analytics and Big Data space, analytics maturity by industry, the automation of Data Science, and "citizen" Data Scientists.
- 5 Ways in Which Big Data Can Help Leverage Customer Data
- May 25, 2016.
Every business enterprise realizes the importance of big data but rarely puts the customer data that they possess to good use. Here are few ways enterprises can leverage customer data.
- Let Me Hear Your Voice and I’ll Tell You How You Feel
- May 24, 2016.
This post provides an overview of a voice tone analyzer implemented as part of a cohesive emotion detection system, directly from the researcher and architect.
- Harnessing Open Data Science for Predictive Analytics (Whitepaper)
- May 24, 2016.
Discover how Anaconda, the leading Open Data Science platform, enables powerful predictive analytic solutions for enterprises.
- Don’t Just Assume That Data Are Interval Scale
- May 23, 2016.
Is the interval scale assumption of your data justified? Research suggests that it may not be, and that applying scale transformations often improves performance.
- The Data Science Market: 2016 Compensation Insights
- May 23, 2016.
Burtch Works shares the annual update to their highly regarded data science salary report series. This year's report details the compensation and demographic data on 374 data scientists.
- 10 Must Have Data Science Skills, Updated
- May 23, 2016.
An updated look at the state of the data science landscape, and the skills - both technical and non-technical - that are absolutely required to make it as a data scientist.
- How to Explain Machine Learning to a Software Engineer
- May 20, 2016.
How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.
- Tips for Data Scientists: Think Like a Business Executive
- May 18, 2016.
Thinking like a Data Scientist is important; it puts businesses and business leaders in an analytical frame of mind. But it is also important for Data Scientists to be able to think like business executives. Read on to find out why.
- HR/Workforce Analytics leadership conference/London/Innovation Enterprise: Summary
- May 17, 2016.
Two intense days, buzzing with energy, knowledge exchange, panel discussions, in short London Data Festival was a great place to be if you are a data scientist. Here is summary of speakers and major attractions of the event.
- High Performance Python for Open Data Science (Whitepaper)
- May 17, 2016.
In this whitepaper, you'll learn from our seasoned experts about the approaches to scaling your data science models, review the various options, and learn how to easily accomplish them using Anaconda, the leading Open Data Science platform powered by Python.
- Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers
- May 16, 2016.
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
- Resume Tips for Early Career Analytics Professionals
- May 16, 2016.
Trying to put together your first resume or two after graduation can be tricky. Without a lot of relevant work experience to highlight, sometimes none at all, graduates often wonder how they can adequately impress hiring managers with their analytics capabilities.
- Practical skills that practical data scientists need
- May 13, 2016.
The long story short, data scientist needs to be capable of solving business analytics problems. Learn more about the skill-set you need to master to achieve so.
- Are Deep Neural Networks Creative?
- May 12, 2016.
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
- Big Data: Content and Technology
- May 11, 2016.
A discussion of using Big Data to provide insight into the big economic questions, and the big expectations that come along.
- Data Scientists – future-proof yourselves
- May 10, 2016.
Here are 7 suggestions for Data Scientist to make themselves future-proof and get skills for a successful Data Science career in the future.
- Data scientists mostly just do arithmetic and that’s a good thing
- May 10, 2016.
Are you also wondering how you can get started as data scientist, and become a valuable team player. Understand what really matters as data scientist, and things to focus in the initial stages.
- Artificial Intelligence ‘Chatbots’ – When or if?
- May 9, 2016.
Chatbots can have extensive applications, now that Facebook is considering to implement AI in their Messenger and WhatsApp platforms. We examine 3 main factors that will determine the success of chatbots.
- There is more to a successful data scientist than mere knowledge
- May 9, 2016.
Look at Data scientist "definitions" with a wry smile: the "essential" skills very much reflect those that a short time ago were quite novel, and are being used in applications to problems that have recently become solvable.
- Why Implement Machine Learning Algorithms From Scratch?
- May 6, 2016.
Even with machine learning libraries covering almost any algorithm implementation you could imagine, there are often still good reasons to write your own. Read on to find out what these reasons are.
- Will Predictive Hiring Algorithms Replace or Augment your HR Decisions?
- May 6, 2016.
Corporate recruiters spend an average of 6 seconds on every resume. Predictive screening algorithms can help identify good candidates, and help recruiters to do a better job.
- From Insight-as-a-Service to Insightful Applications
- May 5, 2016.
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
- Spark with Tungsten Burns Brighter
- May 4, 2016.
Apache Spark is one of “the hottest technology” for data science and analytics. A project called Tungsten represents a huge leap forward for Spark, particularly in the area of performance. Understand how it works, and why it improves Spark performance so much.
- Free Advice For Building Your Data Science Career
- May 4, 2016.
Got hired as data scientist, where to go now from here? Understand how you can make the most of your career by following the different paths like managerial, consulting, or as a domain expert.
- How Much do Analytics Salaries Increase when Changing Jobs?
- May 4, 2016.
A data-informed analysis of analytics career salaries and their increase when changing jobs.
- A Data Science Approach to Writing a Good GitHub README
- May 4, 2016.
Readme is the first file every user will look for, whenever they are checking out the code repository. Learn, what you should write inside your readme files and analyze your existing files effectiveness.
- Datasets Over Algorithms
- May 3, 2016.
The average elapsed time between key algorithm proposals and corresponding advances is about 18 years; the average elapsed time between key dataset availabilities and corresponding advances is less than 3 years, 6 times faster.
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