Analytics

Analytics | News, how-tos, features, reviews, and videos

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Automated machine learning or AutoML explained

AutoML frameworks and services eliminate the need for skilled data scientists to build machine learning and deep learning models

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How to do real-time analytics across historical and live data

5 in-memory computing platform capabilities that support analytical processing of both data lake data and operational streams

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HPE plus MapR: Too much Hadoop, not enough cloud

MapR gives HPE superior big data analytics technology and expertise, but not what HPE needs most

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Stop searching for that data scientist unicorn

Instead, focus on hiring the technical skills needed to build the team, and the soft skills needed to work on the team

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The best NoSQL databases

Highly flexible and hugely scalable, NoSQL databases offer a range of data models and consistency options to suit your application

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Unsupervised learning explained

Unsupervised learning is used mainly to discover patterns and detect outliers in data today, but could lead to general-purpose AI tomorrow

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Get started with AI using ML.Net and Model Builder

Microsoft’s .Net machine learning tooling makes it easy to add AI to your code

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The best graph databases

These stellar databases combine horizontal scalability with highly efficient engines for storing and analyzing connected data

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5 machine learning tools to ease software development

AI-driven development tools that provide code auto-completion, code vulnerability detection, and even cutting-edge code generation

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Applying devops in data science and machine learning

Having data scientists collaborate with devops and engineers leads to better business outcomes, but understanding their different requirements is key

Do More With R [video teaser/video series] - R Programming Guide - Tips & Tricks

How to use .SD in the R data.table package

See how to use data.table's special .SD symbol to perform calculations and other tasks by group

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The best machine learning and deep learning libraries

TensorFlow, Spark MLlib, Scikit-learn, PyTorch, MXNet, and Keras shine for building and training machine learning and deep learning models

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Julia vs. Python: Which is best for data science?

Python has turned into a data science and machine learning mainstay, while Julia was built from the ground up to do the job

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TensorFlow 2 review: Easier machine learning

Now more platform than toolkit, TensorFlow has made strides in everything from ease of use to distributed training and deployment

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The data lake is becoming the new data warehouse

Platforms like AWS Lake Formation and Delta Lake point toward a central hub for decision support and AI-driven decision automation

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Time series analysis with KNIME and Spark

Train and evaluate a simple time series model using a random forest of regression trees and the NYC Yellow taxi data set

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7 MySQL and MariaDB features you don’t want to miss

Look to these powerful MySQL and MariaDB features to boost your modern apps

Do More With R [video teaser/video series] - R Programming Guide - Tips & Tricks
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How to calculate month-over-month changes in R

See how to generate weekly and monthly reports in R including month-over-month, week-over-week, and year-over-year changes

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How dataops improves data, analytics, and machine learning

A dataops team will help you get the most out of your data. Here’s how people, processes, technology, and culture bring it all together

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Supervised learning explained

Supervised learning turns labeled training data into a tuned predictive model

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