2018 Oct News, Features
All (116) | Courses, Education (10) | Meetings (9) | News, Features (16) | Opinions (30) | Top Stories, Tweets (11) | Tutorials, Overviews (32) | Webcasts & Webinars (8)
- How to Mitigate Open Source License Risks - Oct 30, 2018.
This whitepaper from ActiveState investigates the various types of OSS licenses, common myths and risks, DIY risk management, the importance of enterprise legal indemnification, and more.
- [ebook] Manipulating Data in Apache Spark - Oct 29, 2018.
In this ebook from Databricks, learn how DataFrames leverage the power of distributed processing through Spark, how to make big data processing easier for a wider audience, and more.
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SQL, Python, & R in One Platform - Oct 26, 2018.
No more jumping between applications. Mode Studio combines a SQL editor, Python and R notebooks, and a visualization builder in one platform. - New Book: Linear Algebra – what you need for Machine Learning and Data Science now - Oct 24, 2018.
From machine learning and data science to engineering and finance, linear algebra is an important prerequisite for the careers of today and of the future. Learn the math you need with this book.
- A Deep Look at Deep Learning: Understanding The Basics of How (and Why) it Works - Oct 23, 2018.
In this illustrated guide by Dataiku you'll learn what exactly deep learning is and why its growing and why it can be more powerful than classical machine learning (ML).
- Dr. Data Show Video: How Can You Trust AI? - Oct 20, 2018.
This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
- McKinsey Datathon: The City Cup
17 November, Amsterdam, Stockholm and Zurich. Apply Now - Oct 19, 2018.While solving the challenge, you will gain insights into the types of problems that McKinsey Data Scientists solve daily to help their clients. Top prize is 5K Euro + conference attendance of your choice. - The Definitive Guide to AI’s “Black Box” Problem - Oct 17, 2018.
The Amazing, Anti-Jargon, Insight Filled, and Totally Free Handbook to Integrating AI in Highly Regulated Industries - get it now.
- DATAx Guide to Data Visualization in 2019 - Oct 15, 2018.
Get DATAx Guide to Data Visualization in 2019, the definitive foundation to help you prepare for the future of data visualization, AI and machine learning. Also use KD200 to get extra $200 off DATAx New York early bird pricing until Oct 19.
- New Poll: What was the largest dataset you analyzed / data mined? - Oct 12, 2018.
New KDnuggets Poll is asking: What was the largest dataset you analyzed / data mined? Please vote and we will analyze the trends and publish the results.
- SQL, Python, & R: All in One Platform - Oct 11, 2018.
Mode Studio connects a SQL editor, Python and R notebooks, and a visualization builder in one platform. Sign up now for access.
- Data Mining Book: Chapter Download. - Oct 10, 2018.
Download this immediately useful book chapter, and learn how to create derived variables, which allow the statistical and Data Science modeling to incorporate human insights.
- Unleash a Faster Python on Your Data - Oct 2, 2018.
Intel provides optimized Scikit-learn, the most used Python package for classical machine learning. Get faster scikit-learn through Intel® Distribution for Python*
- DevOps 2.0: Applying Machine Learning in the CI/CD Chain - Oct 2, 2018.
Explore how ML can be implemented in your organization, so you can (for example) enable the automated assessment of test results for far more complex criteria, such as defining thresholds based on statistical significance rather than just presence/absence of specific criteria.
- Dr. Data Show Video: Why Machine Learning Is the Coolest Science - Oct 1, 2018.
This new web series breaks the mold for data science infotainment, captivating the planet with short webisodes that cover the very best of machine learning and predictive analytics.
- Robust Quality – Powerful Integration of Data Science and Process Engineering - Oct 1, 2018.
The book by Rajesh Jugulum provides a strong connection between the concepts in data science and process engineering that is necessary to ensure better quality levels and takes you through a systematic approach to measure holistic quality with several case studies.
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