- Last chance to register to attend DataScience: Elevate in San Francisco - Feb 12, 2018.
DataScience: Elevate will be held Feb 22 in San Francisco. Register to be a part of a full day of panels and presentations from people and companies at the forefront of data science.
- Register for DataScience: Elevate Livestream, Feb 22 - Feb 6, 2018.
DataScience: Elevate will be held Feb 22 in San Francisco. Register now for the livestream to tune into a full day of panels and presentations from people and companies at the forefront of data science.
- DataScience.com Adds Former U.S. Chief Data Scientist DJ Patil to Advisory Board - Nov 20, 2017.
Former U.S. Chief Data Scientist DJ Patil will be lending his expertise to DataScience.com’s product, engineering, and R&D teams as they expand the features of the company’s enterprise data science platform.
- DataScience: Elevate Live Stream, July 27 - Jul 13, 2017.
Register now for the live stream of DataScience: Elevate, a half-day event featuring data science leaders from Google, Netflix, eHarmony, and other global companies.
- DataScience.com, H2O.ai Partner to Bring AI Capabilities to Enterprise Data Science Teams - Jun 23, 2017.
DataScience.com Platform customers can now easily deploy artificial intelligence and deep learning models built with H2O.ai’s open source AI platform.
- Webinar: Forecast Demand in R With DataScience.com + RStudio, June 15 - Jun 13, 2017.
DataScience.com and RStudio are co-hosting a free webinar on June 15 to showcase how RStudio’s suite of tools for R seamlessly integrate with the DataScience.com Platform.
- DataScience.com and RStudio Have Partnered to Seamlessly Integrate RStudio Suite into the DataScience.com Platform - Jun 9, 2017.
With the RStudio integration, DataScience.com customers are able to write and run code in RStudio while benefitting from additional features of the platform: on-demand infrastructure, pre-configured environments, secret management, and more.
- DataScience.com Releases Python Package for Interpreting the Decision-Making Processes of Predictive Models - May 24, 2017.
DataScience.com new Python library, Skater, uses a combination of model interpretation algorithms to identify how models leverage data to make predictions.
- DataScience.com New Update Aims to Be Industry-Leading Enterprise Data Science Platform - May 4, 2017.
DataScience.com’s enterprise data science platform can now be deployed on-premises or in the cloud. New features include scalable infrastructure, intuitive project organization, and task automation.
- DataScience Launches Interactive Tool For Exploring Data Science Trends - Apr 14, 2017.
DataScience Trends, a new interactive tool from DataScience Inc., gives users the ability to explore and visualize data across 2.8 million open source repositories without writing code.
- Introduction to Anomaly Detection - Apr 3, 2017.
This overview will cover several methods of detecting anomalies, as well as how to build a detector in Python using simple moving average (SMA) or low-pass filter.
- Grunion, Query Optimization Tool for Data Science and Big Data - Mar 14, 2017.
Grunion is a patent-pending query optimization, translation, and federation framework built to help bridge the gap between data science and data engineering teams. Read more to request access.
- The Challenges of Building a Predictive Churn Model - Mar 8, 2017.
Unlike other data science problems, there is no one method for predicting which customers are likely to churn in the next month. Here we review the most popular approaches.
- What is Customer Churn Modeling? Why is it valuable? - Mar 1, 2017.
Getting new customers is much more more expensive than retaining existing ones, so reducing churn is a top priority for many firms. Understanding why customers churn and estimating the risks are powerful components of a data-driven retention strategy.
- Introduction to Correlation - Feb 22, 2017.
Correlation is one of the most widely used (and widely misunderstood) statistical concepts. We provide the definitions and intuition behind several types of correlation and illustrate how to calculate correlation using the Python pandas library.
- Introduction to Natural Language Processing, Part 1: Lexical Units - Feb 16, 2017.
This series explores core concepts of natural language processing, starting with an introduction to the field and explaining how to identify lexical units as a part of data preprocessing.
- Forrester Study: Companies Using Data Science Platforms Are Surpassing The Competition - Feb 8, 2017.
Companies that regularly exceed shareholder expectations have something in common: 88% of them use a fully functional platform to do data science work. Get the white paper from Forrester to learn more.
- Introduction to Forecasting with ARIMA in R - Jan 16, 2017.
ARIMA models are a popular and flexible class of forecasting model that utilize historical information to make predictions. In this tutorial, we walk through an example of examining time series for demand at a bike-sharing service, fitting an ARIMA model, and creating a basic forecast.
- Creating Data Visualization in Matplotlib - Jan 5, 2017.
Matplotlib is the most widely used data visualization library for Python; it's very powerful, but with a steep learning curve. This overview covers a selection of plots useful for a wide range of data analysis problems and discusses how to best deploy each one so you can tell your data story.
- Introduction to Bayesian Inference - Dec 16, 2016.
Bayesian inference is a powerful toolbox for modeling uncertainty, combining researcher understanding of a problem with data, and providing a quantitative measure of how plausible various facts are. This overview from Datascience.com introduces Bayesian probability and inference in an intuitive way, and provides examples in Python to help get you started.
- Introduction to K-means Clustering: A Tutorial - Dec 9, 2016.
A beginner introduction to the widely-used K-means clustering algorithm, using a delivery fleet data example in Python.