data mining


MIT to Launch Online Micromasters in Data Science -- Campus Technology

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The Statistics and Data Science Center (SDSC) at the Massachusetts Institute of Technology is launching a new online micromasters in statistics and data science. Currently under development by MIT faculty, the program will be available through edX in the fall and will feature a curriculum covering foundational knowledge of data science's methods and tools, in-depth coverage of probability and statistics and opportunities to experiment with data analysis techniques and machine learning algorithms. "The demand for data scientists is growing rapidly," said Krishna Rajagopal, dean for digital learning, in a prepared statement. "This new program increases the supply of professionals who are masters of the data science of today, and who have the foundational understanding needed to keep on top of the data science of tomorrow." MIT's micromasters programs are open to anyone who wants to enroll with no application process.


7 Tracks, 5 Events & a Deep Discount-PAW Las Vegas

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PAW Business is the leading cross-vendor conference covering the commercial deployment of machine learning and predictive analytics. PAW Financial covers the deployment of machine learning and predictive analytics for financial services. The PAW Healthcare program will feature sessions and case studies across Healthcare Business Operations and Clinical applications so you can witness how predictive analytics is employed at leading enterprises and resulting in improved outcomes, lower costs, and higher patient satisfaction. PAW Manufacturing focuses on real-world examples of deployed predictive analytics. Attend and hear how some of the world's largest and most forward-thinking manufacturers are tapping the powering predictive modeling to improve business outcomes.



FICO Delivers Mission Critical Artificial Intelligence in the Cloud

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FICO World, analytics software firm FICO showcased the latest enhancements to FICO Decision Management Suite (DMS) which leverages artificial intelligence (AI), machine learning (ML), advanced analytics, optimization and decisioning to deliver better, more predictive business outcomes. "We are excited to unveil new capabilities in FICO DMS," said Jari Koister, vice president of product management at FICO. "DMS supports the full life cycle of decision, analytics and AI driven applications, this includes data ingest and management, collaborative development, scalable elastic execution, and management and performance tracking of models." The new Enterprise Grade Cloud Service is a managed service offered in a FICO datacenter or on AWS. This new service provides everything an enterprise needs to run and operate decision management solutions in the Cloud, including infrastructure, support, architecture best practices, and lifecycle services. Introducing a new Analytics Workbench, FICO now offers a robust analytic development tool that was built from the ground up to be fully integrated with the decision platform.


Introduction to Data Science with Python

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This course introduces Python programming as a way to have hands-on experience with Data Science. It starts with a few basic examples in Python before moving onto doing statistical processing. The course then introduces Machine Learning with techniques such as regression, classification, clustering, and density estimation, in order to solve various data problems.


AI, Machine Learning & Modern Manufacturing Productivity

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After-sales service is in a period of technological disruption like the industry has never seen. And leading this charge are artificial intelligence and machine learning. Set to be worth more than $9 billion by 2020, predictive analytics is poised to open up business intelligence doors for manufacturers, and also have a dramatic impact on productivity. With this in mind, below are a few ways artificial intelligence and machine learning are set to revolutionize productivity for electronics manufacturers and their supply chains. Streamlining operations While today's manufacturing supply chains are becoming more streamlined and informed, there are still instances where additional oversight could be used to identify potential areas for improvement based on delays, production times, results and more.


Self Driven Data Science -- Issue #44 – Conor Dewey – Medium

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This post walks through a complete example illustrating an essential data science building block: the underfitting vs. overfitting problem. The author explores the problem through a beginner's implementation of cross-validation. The wide growth of deep learning has complicated things a bit in the hardware department. This post will walk through the different types of computer chips, where they're available, and which ones are the best to boost your performance. One of the most common problems in data science is that of dealing with missing values.


A Data Scientist's Guide to Happiness: Findings From the Happy Experiences of 10,000 Humans

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Modern life throws a lot at us. We often find ourself struggling to manage anxiety, wrangle responsibilities, adapt to new conditions, and maintain a happy state of mind. But happiness is a noisy space these days. Self help books, articles, blogs, and meditation apps can't help everyone, and often increase the mental burden needed to stay content. So, as mental health becomes increasingly vulnerable and solutions become increasingly complex, it's important to anchor oneself to the fundamentals.


Tobi Bosede on What It Takes to Be a Machine Learning Engineer

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At the Strata Data Conference in New York City in the fall, Paige Roberts of Syncsort had a chance to sit down with Tobi Bosede, Sr Machine Learning Engineer, shortly after her presentation. In the first of this three-part blog series, Bosede explains what goes into being a Machine Learning Engineer as well as some of the projects she is currently involved with. I got my graduate degree from Johns Hopkins and my presentation was on the research I did for a graduate thesis. I am a Machine Learning Engineer so I look at all sorts of data relating to finance, not necessarily relating to trades, but that includes staying up to date with current tools and technologies such as python libraries or using things like Ansible. Some of it is local on my machine but, yeah.


Interpretable Machine Learning with XGBoost – Towards Data Science

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The features are sorted by mean( Tree SHAP) and so we again see the relationship feature as the strongest predictor of making over $50K annually. By plotting the impact of a feature on every sample we can also see important outlier effects. For example, while capital gain is not the most important feature globally, it is by far the most important feature for a subset of customers. The coloring by feature value shows us patterns such as how being younger lowers your chance of making over $50K, while higher education increases your chance of making over $50K. We could stop here and show this plot to our boss, but let's instead dig a bit deeper into some of these features.