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Here’s what’s new in data science:
For the practitioner: Causal Inference: An Indispensable Set of Techniques for Your Data Science Toolkit by Vinod Bakthavachalam, Guest Contributor
Causal inference techniques open the door to extracting maximum value from historical data and enable the answering of critical business and product questions. Let’s expand on that.
For the decision-makers: Taking Your Machine Learning from 0 to 10 by Elizabeth Wallace, ODSC
Here are a few pivotal ways that you can implement machine learning into your organization and make sure it’s successful.
More for the practitioner: Not Always a Black Box: Machine Learning Approaches For Model Explainability by Violeta Misheva, Guest Contributor
Let’s take a look at a few different approaches for machine learning explainability so you can avoid the dreaded black box problem.
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Upcoming webinar: Upcoming Webinar: When Holt-Winters is better than ML for Time Series Data
Thursday, August 15, 2019 1:00 PM - 2:00 PM EDT
Developer Advocate Anais Dotis-Georgiou will show you how the Holt-Winters forecasting algorithm works. Then we’ll use the HOLT_WINTERS() function with InfluxData to make our own time series forecast.
Video of the week: Digital Discrimination: Cognitive Bias in Machine Learning - Maureen Mc Elaney, Brendan Dwyer
With increasing regularity, we see stories in the news about machine learning algorithms causing real-world harm. Peoples’ lives and livelihood are affected by the decisions made by machines. Learn how bias can take root in machine learning algorithms and ways to overcome it. From the power of open source, to tools built to detect and remove bias in machine learning models, there is a vibrant ecosystem of contributors who are working to build a digital future that is inclusive and fair. Now you can become part of the solution.
Slides of the Week: From Numbers to Narrative: Data Storytelling - Isaac Reyes
Featured Jobs:
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