Sat.Dec 28, 2019 - Fri.Jan 03, 2020

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Predict Electricity Consumption Using Time Series Analysis

KDnuggets

Time series forecasting is a technique for the prediction of events through a sequence of time. In this post, we will be taking a small forecasting problem and try to solve it till the end learning time series forecasting alongside.

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Building The DataDog Platform For Processing Timeseries Data At Massive Scale

Data Engineering Podcast

Summary DataDog is one of the most successful companies in the space of metrics and monitoring for servers and cloud infrastructure. In order to support their customers, they need to capture, process, and analyze massive amounts of timeseries data with a high degree of uptime and reliability. Vadim Semenov works on their data engineering team and joins the podcast in this episode to discuss the challenges that he works through, the systems that DataDog has built to power their business, and how

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Celebrating 1,000 Employees and Looking Towards the Path Ahead

Confluent

During the holiday season, it’s a particularly relevant time to pause, reflect, and celebrate, both the days past and those ahead. Here at Confluent, it’s a noticeably nostalgic moment, given […].

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Don’t Organize for AI, Organize for Analytics

Teradata

How do you organize your business for analytics? Here are six steps your enterprise should take when creating an analytics team. Read more!

59
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Precision in Motion: Why Process Optimization Is the Future of Manufacturing

Speaker: Jason Chester, Director, Product Management

In today’s manufacturing landscape, staying competitive means moving beyond reactive quality checks and toward real-time, data-driven process control. But what does true manufacturing process optimization look like—and why is it more urgent now than ever? Join Jason Chester in this new, thought-provoking session on how modern manufacturers are rethinking quality operations from the ground up.

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Why Python is One of the Most Preferred Languages for Data Science?

KDnuggets

Why do most data scientists love Python? Learn more about how so many well-developed Python packages can help you accomplish your crucial data science tasks.

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What is the most important question for Data Science (and Digital Transformation)

KDnuggets

With so many buzzwords surrounding AI and machine learning, understanding which can bring business value and which are best left in the lab to mature is difficult. While machine learning offers significant power in driving digital transformations, a business must start with the right questions and leave the math to the development teams.

More Trending

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Automated Machine Learning: How do teams work together on an AutoML project?

KDnuggets

In this use case, available to the public on GitHub, we’ll see how a data scientist, project manager, and business lead at a retail grocer can leverage automated machine learning and Azure Machine Learning service to reduce product overstock.

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How To “Ultralearn” Data Science: summary, for those in a hurry

KDnuggets

For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.

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Accuracy vs Speed – what Data Scientists can learn from Search

KDnuggets

Delivering accurate insights is the core function of any data scientist. Navigating the development road toward this goal can sometimes be tricky, especially when cross-collaboration is required, and these lessons learned from building a search application will help you negotiate the demands between accuracy and speed.

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Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part II

KDnuggets

AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems.

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Airflow Best Practices for ETL/ELT Pipelines

Speaker: Kenten Danas, Senior Manager, Developer Relations

ETL and ELT are some of the most common data engineering use cases, but can come with challenges like scaling, connectivity to other systems, and dynamically adapting to changing data sources. Airflow is specifically designed for moving and transforming data in ETL/ELT pipelines, and new features in Airflow 3.0 like assets, backfills, and event-driven scheduling make orchestrating ETL/ELT pipelines easier than ever!

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Towards a Quantitative Measure of Intelligence: Breaking Down One of the Most Important AI Papers of 2019, Part I

KDnuggets

AI scientist Francois Chollet proposes a better framework for measuring the intelligence of AI systems.

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Top KDnuggets tweets, Dec 18-30: A Gentle Introduction to Math Behind Neural Networks

KDnuggets

A Gentle Introduction to #Math Behind #NeuralNetworks; Learn How to Quickly Create UIs in Python; I wanna be a data scientist, but. how!?; I created my own deepfake in two weeks.

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Don’t Organize for AI, Organize for Analytics

Teradata

How do you organize your business for analytics? Here are six steps your enterprise should take when creating an analytics team. Read more!

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How HR Is Using Data Science and Analytics to Close the Gender Gap

KDnuggets

The gender gap can extend to the lack of equal representation in certain industries or career paths, and there's an extraordinarily long way to go before people will be on equal footing in the labor market. Human resources professionals can rely on data analytics to make progress.

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Whats New in Apache Airflow 3.0 –– And How Will It Reshape Your Data Workflows?

Apache Airflow® 3.0, the most anticipated Airflow release yet, officially launched this April. As the de facto standard for data orchestration, Airflow is trusted by over 77,000 organizations to power everything from advanced analytics to production AI and MLOps. With the 3.0 release, the top-requested features from the community were delivered, including a revamped UI for easier navigation, stronger security, and greater flexibility to run tasks anywhere at any time.

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Top Stories, Dec 16-29: What is a Data Scientist Worth?; Google’s New Explainable AI Service

KDnuggets

Also: Let’s Build an Intelligent Chatbot; 10 Best and Free Machine Learning Courses, Online; Build Pipelines with Pandas Using pdpipe; Alternative Cloud Hosted Data Science Environments.

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Why Kaggle will not Make you a Great Data Scientist

Teradata

Are you a budding data scientist? Learn why Kaggle only offers a limited view of data science and is not the optimal place to learn data science skills.