Monday newsletter published by Data Science Central. Previous editions can be found here. The contribution flagged with a + is our selection for the picture of the week. To subscribe, follow this link.
Featured Resources and Technical Contributions
- Getting Started with pandas a powerful Python data analysis toolkit
- An introduction to Cloud Native applications and Kubernetes
- Multi-Dimensional Frequencies with R data.table.
- Finding organic clusters in complex data-networks
- Python Programming Fundamentals: A Beginner’s Guide
- Leveraging NLP for business processes optimization (Updated)
- Improving statistical prediction on financial market with Symbolic Logic
- Data Storage at the Edge: Typologies and Attributes
- Data Science Lessons from The Coronavirus +
- COVID 19 – How AI can lead to more local manufacturing jobs
- What are Data Pipelines?
- Tech Target Acquires Data Science Central
- Google Releases TensorFlow Quantum
- Overcoming Challenges in Implementation of Successful AI products
- How to Solve the Biggest Challenges in Enterprise AI
- How VDRs Help to Improve Data Security?
Picture of the Week
Source: article flagged with a +
From our Sponsors
- EOL for ETL–The Future of Data Wrangling in the Cloud – eBook
- Accelerating AI Adoption with Machine Learning Operations (MLOps)
- Managing Risk in Decentralized Networks with Time Series – March 24
- 500 Petabytes of Data to Understand the Universe Better – March 18
- Build Better ML Models with These 5 QA Methods – On-Demand Webinar
- Analytics and AI: How The Red Sox Knock it Out of the Park
- AIOps: How to Turn AI into a Core Part of Your Business
- Data Architecture & Model Deployment Trends
- Which chart or graph is right for you?
- Standardizing the Machine Learning Lifecycle: eBook
New Books and Resources for DSC Members – [See Full List]
- Getting Started with TensorFlow 2.0
- Online Encyclopedia of Statistical Science
- Statistics — New Foundations, Toolbox, and Machine Learning Recipes
- Classification and Regression In a Weekend
- Applied Stochastic Processes
- Enterprise AI – An Applications Perspective
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