5 Reasons Machine Learning Applications Need a Better Lambda Architecture
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.
on Jun 2, 2016 in Applications, Lambda Architecture, Machine Learning, Monte Zweben, Splice Machine
Intel’s Investments in Cognitive Tech: Impact and New Opportunities
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.
on May 31, 2016 in Cognitive Computing, Intel
Beyond Big Data Skills: Creating the Right DNA for the Managers of the Data-driven Business World
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.
on May 31, 2016 in Advice, Big Data, Data Analytics, Data Science Education, Data-Driven Business, Manager
Interacting with Machine Learning – Here is Why You Should Care
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.
on May 30, 2016 in Machine Learning, Siri
Automakers Must Partner Around Big Data
A discussion on the need for auto manufacturers to come together and leverage Big Data.
on May 26, 2016 in Automotive, Big Data, Tesla, Uber
Trust and Analytics in the Banking Sector
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.
on May 26, 2016 in Advanced Analytics, Banking, Big Data, Finance, Trust
SAP Predictive Analytics Interview with Sven Bauszus
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.
on May 26, 2016 in Automated Data Science, In-Memory Computing, Interview, Predictive Analytics, SAP, SAP BusinessObjects, SAP HANA
5 Ways in Which Big Data Can Help Leverage Customer Data
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.
on May 25, 2016 in Analytics, Big Data, Data Management, Data Mining
Let Me Hear Your Voice and I’ll Tell You How You Feel
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.
on May 24, 2016 in Artificial Intelligence, Deep Learning, Emotion
Harnessing Open Data Science for Predictive Analytics (Whitepaper)
Discover how Anaconda, the leading Open Data Science platform, enables powerful predictive analytic solutions for enterprises.
on May 24, 2016 in Continuum Analytics, Data Science, Open Data, Predictive Analytics
Don’t Just Assume That Data Are Interval Scale
Is the interval scale assumption of your data justified? Research suggests that it may not be, and that applying scale transformations often improves performance.
on May 23, 2016 in Geoff Webb
The Data Science Market: 2016 Compensation Insights
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.
on May 23, 2016 in Burtch Works, Data Science, Salary
10 Must Have Data Science Skills, Updated
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.
on May 23, 2016 in Advice, Books, Data Science Skills, Data Scientist, MOOC
How to Explain Machine Learning to a Software Engineer
How do you explain what machine learning is to the uninitiated software engineer? Read on for one perspective on doing so.
on May 20, 2016 in Automating, Machine Learning, Software Engineer
Tips for Data Scientists: Think Like a Business Executive
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.
on May 18, 2016 in Advice, Analytics, Data Scientist
HR/Workforce Analytics leadership conference/London/Innovation Enterprise: Summary
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.
on May 17, 2016 in HR, IE Group, London, UK, Workforce Analytics
High Performance Python for Open Data Science (Whitepaper)
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.
on May 17, 2016 in Continuum Analytics, Data Science, Open Data, Python
Embrace the Random: A Case for Randomizing Acceptance of Borderline Papers
A case for using randomization in the selection of borderline academic papers, a particular use case which has parallels with many other possible scenarios.
on May 16, 2016 in Academics, ICML, NIPS, Random, Randomization
Resume Tips for Early Career Analytics Professionals
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.
on May 16, 2016 in Advice, Analytics, Burtch Works, Career
Practical skills that practical data scientists need
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.
on May 13, 2016 in Business Context, Data Scientist, Mathematics, Skills, SQL
Are Deep Neural Networks Creative?
Deep neural networks routinely generate images and synthesize text. But does this amount to creativity? Can we reasonably claim that deep learning produces art?
on May 12, 2016 in Artificial Intelligence, Deep Learning, Generative Adversarial Network, Generative Models, Recurrent Neural Networks, Reinforcement Learning, Zachary Lipton
Big Data: Content and Technology
A discussion of using Big Data to provide insight into the big economic questions, and the big expectations that come along.
on May 11, 2016 in Big Data, Economics, Gio Wiederhold, Stanford
Data Scientists – future-proof yourselves
Here are 7 suggestions for Data Scientist to make themselves future-proof and get skills for a successful Data Science career in the future.
on May 10, 2016 in Career, Data Scientist, Skills
Data scientists mostly just do arithmetic and that’s a good thing
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.
on May 10, 2016 in Data Scientist, Mathematics, Skills
Artificial Intelligence ‘Chatbots’ – When or if?
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.
on May 9, 2016 in AI, Bots, Chatbot, Cybersecurity, Facebook, Humans vs Machines, Microsoft
There is more to a successful data scientist than mere knowledge
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.
on May 9, 2016 in Data Scientist, Mathematics, Skills
Why Implement Machine Learning Algorithms From Scratch?
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.
on May 6, 2016 in Algorithms, Machine Learning
Will Predictive Hiring Algorithms Replace or Augment your HR Decisions?
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.
on May 6, 2016 in Hiring, HR, Workforce Analytics
From Insight-as-a-Service to Insightful Applications
Applications that combine machine learning, AI, and domain knowledge have strong potential for industry and investors.
on May 5, 2016 in Artificial Intelligence, Domain Knowledge, Evangelos Simoudis, Insights, Machine Learning
Spark with Tungsten Burns Brighter
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.
on May 4, 2016 in Apache Spark, In-Memory Computing, Tungsten
Free Advice For Building Your Data Science Career
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.
on May 4, 2016 in Advice, Career, Data Science
How Much do Analytics Salaries Increase when Changing Jobs?
A data-informed analysis of analytics career salaries and their increase when changing jobs.
on May 4, 2016 in Analytics, Burtch Works, Career, Salary
A Data Science Approach to Writing a Good GitHub README
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.
on May 4, 2016 in Algorithmia, GitHub, Text Mining
Datasets Over Algorithms
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.
on May 3, 2016 in Algorithms, Datasets
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