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Interview: Vinod Bakthavachalam, Data Scientist at Coursera

I recently caught up with Vinod Bakthavachalam, Data Scientist at Coursera, to discuss how to build in-demand skills in data science such as machine learning, statistics, and data management across your organization to drive competitive advantage. Coursera for Business provides companies with the world’s best learning experience and best content to transform their talent.

Gain In-demand Cloud, Data, and Machine Learning Skills with the Full Google Cloud Suite of Learning Programs on Coursera

Online learning leader Coursera now offers the full Google Cloud suite of programs on their learning platform. Learners can enroll in Google Cloud courses and Specializations to access top-quality cloud training. All courses include free hands-on labs to provide practical experience.

Infographic: The Data Scientist Shortage

Statistics point to a promising career in data science for anyone with the skills and interest to pursue this field in the 21st Century, whether one wishes to start from the first year of university or redirect his or her track midway. Given the current employment crisis featured in the infographic below, developed by our friends over at the University of California, Riverside, even individuals who have pursued programming and technical programs at high school could be thrust into more demanding positions in the work place.

Coursera Expands Coursera for Business Features, Launches AI-powered Skills Benchmarking Tool

Coursera, an online learning leader, announced the launch of Skills Benchmarking, a powerful new Coursera for Business tool that enables organizations to measure how their talent stacks up against others in their industry as well as identify top performing individuals in a given competency area.

insideBIGDATA “Ask a Data Scientist” Series

Welcome to the series of articles sponsored by Intel – “Ask a Data Scientist” from insideBIGDATA’s popular Data Science 101 channel. These articles constitute many of our site’s most popular resources for newbie data scientists. The 12 articles listed below were from reader submitted questions of varying levels of technical detail and answered by a practicing data scientist – sometimes by me and other times by an Intel data scientist.

Be on Top of Key Data Analytic Trends

Emily Washington: ‘Businesses are increasingly evaluating ways to streamline their overall technology stack… to successfully leverage big data and analytics’. Tech trends in data analytics are seeing the industry soar. Discover more here.

Data Science 101: Handling Missing Data (Revisited)

I recently received the following question on data science methods from an avid reader of insideBIGDATA who hails from Taiwan. I think the topics are very relevant to many folks in our audience so I decided to run it here in our Data Science 101 channel. The issue of missing data is one most data scientists see quite frequently.

Interview: John Hart, Professor of Computer Science at University of Illinois

I recently caught up with John Hart, Professor of Computer Science at University of Illinois, to discuss his university’s new Master of Computer Science in Data Science (MCS-DS) degree program. The completely online degree allows students to learn about new statistical and computational tools that are transforming business and society from the Illinois faculty who are pioneering them. Students discover Data Science: the art of extracting new knowledge and finding meaningful information in a massive sea of data.

Reinforcement Learning: Hidden Theory and New Super-Fast Algorithms

Stochastic Approximation algorithms are used to approximate solutions to fixed point equations that involve expectations of functions with respect to possibly unknown distributions. The most famous examples today are TD- and Q-learning algorithms. This three hour tutorial lecture series, courtesy of the Simon Institute for the Theory of Computing at UC Berkeley, consists of two video segments.

Importance of Using Data and Analysis in Higher Education

Using the right data, in the right way, can help educational institutions and leaders keep up with ongoing challenges. Data analysis consolidates information to provide the big picture of trends and patterns for higher education leadership teams that can be used to evaluate and streamline processes, create efficiencies, and improve the overall student experience. The infographic below from our friends over at Maryville University highlights the importance of data and analysis in higher education.