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Operationalizing Data Science

In the video presentation below, Joel Horwitz, Vice President, Partnerships, Digital Business Group for IBM Analytics, discusses what it means to “operationalize data science” – basically what it means to harden the ops behind running data science platforms.

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? 365 DataScience gathered data from 1,001 publicly listed LinkedIn profiles of data scientists and prepared a compelling report “Studying 1,001 Data Scientist LinkedIn Profiles.”

Research of 1,001 Data Scientist LinkedIn Profiles

Data science is a super-hot topic and the data scientist job is the sexiest job of the 21st century according to the Harvard Business Review. But how does one actually become a data scientist? For you it doesn’t really matter how others became data scientists. What you are interested in is whether you can become […]

Python: Unlocking the Power of Data Science & Machine Learning

Python stands out as the language best suited for all areas of the data science and machine learning framework. Designed as a flexible general purpose language, Python is widely used by programmers and easily learnt by statisticians. Download the new guide from ActiveState that provides a summary of Python’s attributes, as well as considerations for implementing the programming language to drive new insights and innovation from big data.

Sentenai Delivers Automated Data Engineering for Data Science and Machine Learning Applications

Sentenai, an emerging sensor data technology company, announced the availability of its flagship product, the Sentenai Sensor Data Cloud. By going beyond the initial harnessing of machine-based data and understanding the information that data provides, organizations can streamline their operational processes and develop predictive maintenance solutions that decrease unplanned downtime.

The Practical Guide to Managing Data Science at Scale

Our friends over at Domino Data Lab, Inc. have written a new whitepaper “The Practical Guide to Managing Data Science at Scale” that aims to demystify and elevate the current state of data science management. They identify consistent struggles around stakeholder alignment, the pace of model delivery, and the measurement of impact. The root cause of these challenges can be traced to a set of particular cultural issues, gaps in process and organizational structure, and inadequate technology.

The Practical Guide to Managing Data Science at Scale

Lessons from the field on managing data science projects and portfolios The ability to manage, scale, and accelerate an entire data science discipline increasingly separates successful organizations from those falling victim to hype and disillusionment. Data science managers have the most important and least understood job of the 21st century. This paper from Domino Data […]

Intel’s New Processors: A Machine-learning Perspective

Machine learning and its younger sibling deep learning are continuing their acceleration in terms of increasing the value of enterprise data assets across a variety of problem domains. A recent talk by Dr. Amitai Armon, Chief Data-Scientist of Intel’s Advanced Analytics department, at the O’reilly Artificial Intelligence conference, New-York, September 27 2016, focused on the usage of Intel’s new server processors for various machine learning tasks as well as considerations in choosing and matching processors for specific machine learning tasks.

MapR Delivers Self-Service Data Science for Leveraging Machine Learning and Artificial Intelligence

MapR Technologies, Inc., a pioneer in delivering one platform for all data, across every cloud, announced the MapR Data Science Refinery, a new solution that provides data scientists an easy way to access and analyze all data in-place, to collaborate, build and deploy machine learning models on the MapR Converged Data Platform.

Data 2020: State of Big Data Study

Our friends over at SAP recently published a study that highlights some really compelling findings around data scientists. There’s a wide gap and serious discrepancy in the level of importance organizations place on data scientists and the number of data scientists they employ. Below are some key statistics from the study, along with a summary infographic.