- Leaders, Changes, and Trends in Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 24, 2020.
The Gartner 2020 Magic Quadrant for Data Science and Machine Learning Platforms has the largest number of leaders ever. We examine the leaders and changes and trends vs previous years.
- [Upcoming Webinar] 5 Steps to Building Responsible AI Systems - Apr 10, 2019.
What does responsible AI mean? This webinar, Apr 18 @ 11 AM ET, will cover the essential steps to building AI systems that are responsible.
- Get the guidebook for tackling data privacy & compliance - Mar 12, 2019.
This guidebook walks through the myths & realities of pseudonymization and working with personal data, and suggests data team processes for compliance.
- How to Cope with the Rise of the Citizen Data Scientist - Feb 19, 2019.
Gartner predicts that citizen data scientists will surpass data scientists in the amount of advanced analytics produced. Does that mean that Enterprise AI and augmented analytics render the job of a data scientist obsolete? Download this white paper to found out more.
- Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms - Feb 11, 2019.
We compare Gartner 2019 MQ for Data Science, Machine Learning Platforms to its previous versions and identify notable changes for leaders and challengers, including RapidMiner, KNIME, TIBCO, Alteryx, Dataiku, SAS, and MathWorks.
- Get the latest analyst research on data science platforms - Jan 30, 2019.
Access a complimentary copy of the Gartner 2019 Magic Quadrant for Data Science and Machine-Learning Platforms to discover the latest trends and see why Dataiku was named a "Challenger" in the industry.
- Webinar: 2019 AI Trends: Filtering the Noise - Jan 18, 2019.
Check out Dataiku's exclusive webinar on Feb 7, 11am EST, "2019 AI Trends: Filtering the Noise," featuring insights from Léo Drefus-Schmidt, Lead Data Scientist at Dataiku.
- Improve ML transparency without sacrificing accuracy - Dec 19, 2018.
O'Reilly begins to shed some light on the accuracy/complexity tradeoff in machine learning, with An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI. Get the ebook now!
- Open Source Data Science Adoption: The How & Why - Dec 4, 2018.
Get the report on Enterprise Open Source Data Science Adoption which outlines the most popular open source tools for a host of jobs. Free download.
- Self-Service Analytics and Operationalization – Why You Need Both - Nov 12, 2018.
Get the guidebook / whitepaper for a look at how today's top data-driven companies scale their advanced analytics & machine learning efforts.
- 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).
- A Deep (But Jargon Free) Dive Into Deep Learning - Sep 20, 2018.
Learn what exactly deep learning is, how it works, and about its growing and innovative applications in healthcare, finance, retail, and more with this illustrated guide.
- Build an Anomaly Detection Project [Free Guidebook] - Jun 14, 2018.
Learn how to find value and insight in outliers in the latest anomaly detection guidebook by Dataiku, which includes use cases, and step-by-step guidance (including code samples) to starting an anomaly detection project.
- ML Powering Marketing Automation: New Guidebook - Apr 24, 2018.
Understanding and quantifying a customer's journey - otherwise known as marketing attribution - is essential for marketers to analyze the ROI from campaigns. Get the latest guidebook to understand how its done!
- 7 Steps of a Data Science PoC – Get The Guidebook - Feb 12, 2018.
Download a free copy of the white paper The 7 Steps to Driving a Successful Data Science POC for a detailed walk-through of the seven steps to running a successful POC.
- Industry Predictions: Main AI, Big Data, Data Science Developments in 2017 and Trends for 2018 - Dec 19, 2017.
Here is a treasure trove of analysis and predictions from 17 leading companies in AI, Big Data, Data Science, and Machine Learning: What happened in 2017 and what will 2018 bring?
- Your guide to predictive analytics in media and entertainment - Nov 13, 2017.
Download your free guide to predictive analytics in media and entertainment for a look at the landscape and use cases, from Dataiku.
- Recommendation Engines and Real-time personalization – download guidebook - Oct 26, 2017.
Recommendation engines are effective because they expose users to content they may not have otherwise found. For a step-by-step guide on building an effective recommendation engine from the ground up, check out our latest guidebook.
- EGG2017: Innovate. Get Ahead. Disrupt. And Embrace Non-Conformity. - Oct 20, 2017.
On November 30th 2017, there’s a new kind of data science & analytics conference: EGG2017, Dataiku’s first large-scale data science and analytics conference in New York, NY.
- The Role of the Data Analyst in a Predictive Era - Aug 17, 2017.
Read "Analyst of the Future" guidebook to discover 3 emerging analyst roles and what they encompass, 4 trends transforming the world of data, and more.
- Making Predictive Models Robust: Holdout vs Cross-Validation - Aug 11, 2017.
The validation step helps you find the best parameters for your predictive model and prevent overfitting. We examine pros and cons of two popular validation strategies: the hold-out strategy and k-fold.
- Get Out of the Sandbox – Put Your Models in Production, Aug 10 Webinar - Aug 2, 2017.
Learn how to deploy your Data Science work in production, both in batch and real-time environments, where people and programs can use them simply and confidently.
- Free Guidebook: Build a Complete Predictive Maintenance Strategy - Jul 18, 2017.
Learn how predictive maintenance differs from and better than traditional one; Use cases and potential data sources; and next steps for getting started.
- Your Checklist to Get Data Science Implemented in Production - Jun 7, 2017.
For over a year we surveyed thousands of companies from all types of industries and data science advancement on how they managed to overcome these difficulties and analyzed the results. Here are the key things to keep in mind when you're working on your design-to-production pipeline.
- Dataiku: The Complete Data Sheet - Apr 20, 2017.
Whether your every day tool is Scala, Python, R, or Excel, you can now use one tool - Dataiku - to transform raw data to predictions without the hassle. Discover the platform!
- The Evolution of a Productive Data Team - Apr 11, 2017.
Successful data teams at companies of any size are able to produce results because they develop gradually through a series of stages and acquire skills along the way that help them stay efficient and effective.
- Getting Up Close and Personal with Algorithms - Mar 21, 2017.
We've put together a brief summary of the top algorithms used in predictive analysis, which you can see just below. Read to learn more about Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting, and more.
- Deploying Production-grade Data Products – Special Report - Jan 24, 2017.
Dataiku launched a survey a few months back to find out how companies handled going from designing to deploying a data product. Read the report and learn four ways that companies approach production.
- Dataiku DSS 3.1 – Now with 5 ML Backends & Scala! - Aug 1, 2016.
Introducing Dataiku DSS 3.1, with new visual machine learning engines that allow users to create incredibly powerful predictive applications within a code-free interface.
- Interview: Florian Douetteau, Dataiku Founder, on Empowering Data Scientists - Jul 7, 2016.
Here is an interview with Florian Douetteau, founder of Dataiku, on how their tools empower data scientists, and how data science itself is evolving.
- Predicting purchases at retail stores using HPE Vertica and Dataiku DSS - Jun 23, 2016.
The retail industry has been data centric for a while. With the rise of loyalty programs and digital touch points, retailers have been able to collect more and more data about their customers over time, opening up the ability to create better personalized marketing offers and promotions.
- Survey: Why Companies Still Fail to Get Full Value From Big Data - Apr 29, 2016.
Any company that has decided to put efforts in data has to face bringing these projects from the design and development phase to the production phase at some point. So tell us how you do it. And we’ll tell you what we learned from you.
- R or Python? Consider learning both - Mar 8, 2016.
The key to become a data science professional is in understanding the underlying data science concepts and work towards expanding your programming toolbox as much as you can. Hence, one should understand when to use Python and when to pick R, rather mastering just one language.
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- Will Balkanization of Data Science lead to one Empire or many Republics? - Nov 30, 2015.
We examine the “Technoslavia” of the Big Data and Data Science market and consider whether it is likely to lead to a unified empire or a federation of independent republics.
- Dataiku Data Science Studio, now also runs on Apache Spark - Sep 29, 2015.
Dataiku Data Science Studio version 2.1 has many useful features for Data Scientists, including integration with Apache Spark.
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- Top KDnuggets tweets, Jul 7-13: Deep Learning and the Triumph of Empiricism - Jul 14, 2015.
Deep Learning and the Triumph of Empiricism; What can Hadoop do that my data warehouse cant?; Emacs for Data Science; Dataiku DataScience Studio - intuitive solution.
- Dataiku Data Science Studio – intuitive solution for data professionals - Jul 8, 2015.
Data Science Studio (DSS) from Dataiku is an intuitive software solution that let data professionals harness the power of big data. The latest version DSS 2.0 brings predictive analytics to a whole new level in terms of collaboration and usability.
- Top stories for Aug 24-30: 4 main languages for Analytics, Data Mining; Dataiku Data Science Studio - Aug 31, 2014.
Four main languages for Analytics, Data Mining, Data Science; Dataiku Data Science Studio; KDD-2014 - The Biggest, Best, and Booming Data Science Meeting; On the Secret Sauce of Impressive Content Curation.
- Top KDnuggets tweets, Aug 25-26: KDD-2014 organizers (including Gregory) Ice Bucket challenge - Aug 27, 2014.
KDD-2014 organizers, including Gregory, take Ice Bucket challenge; Most important APIs every Data Scientist should know; Best Text Analytics Summit Presentations; Artificial Dataset Generation for Machine Learning.
- Dataiku Data Science Studio - Aug 26, 2014.
Data Science Studio (DSS) from Dataiku is a complete Data Science software tool for developers and analysts,
which significantly shortens the time-consuming load-clean-train-test-deploy cycles of building predictive applications.
A community edition and a free trial available.