DataRobot Blog

MARCH 10, 2022

The “Fourth Industrial Revolution” was coined by Klaus Schwab of the World Economic Forum in 2016. Python is unarguably the most broadly used programming language throughout the data science community. Train, Compare, Rank, Validate, and Select Models for Production.

Manufacturing 52

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CIO Business Intelligence

MAY 17, 2022

Microsoft’s Tay chatbot, released on Twitter in 2016, quickly started spewing racist, misogynist, and anti-Semitic messages. This particular hospital’s data science team had built a system to identify such comorbidities. The hospital did this by investing more in data collection, Taglieti says. Can we automate?”.

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Smarten

JANUARY 3, 2019

These business users have adopted business intelligence and advanced analytical tools to gather and analyze data from varied data sources and use that analysis to identify the root cause of problems, identify opportunities, solve problems and share crucial data to support business decisions.

Business Intelligence 40

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Insight

AUGUST 8, 2019

But for those who haven’t been through it, the program’s unique model can be hard to grasp. If you’re considering transitioning to career in data, here are the top five things you should know about Insight. Insight’s model is very different from this. In 2014, Insight started its first Data Engineering program.

Data Science 66

Data Science Cost-Benefit Big Data Management 66

Cloudera

SEPTEMBER 28, 2020

ACID transactions, ANSI 2016 SQL SupportMajor Performance improvements. The data lifecycle model ingests data using Kafka, enriches that data with Spark-based batch process, performs deep data analytics using Hive and Impala, and finally uses that data for data science using Cloudera Data Science Workbench to get deep insights.

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DataRobot Blog

AUGUST 23, 2019

Using this data, we built a historical dataset containing past results, current Elo scores (both overall and surface-specific) and tournament information, then used DataRobot to determine the best model and predict the probability that a player would win a set. The US Open has begun, and the world is watching. Andrew received his Ph.D.

Predictive Modeling 52

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Domino Data Lab

JANUARY 6, 2019

In Paco Nathan ‘s latest column, he explores the theme of “learning data science” by diving into education programs, learning materials, educational approaches, as well as perceptions about education. He is also the Co-Chair of the upcoming Data Science Leaders Summit, Rev. Learning Data Science.

Data Science 56

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Occam's Razor

MARCH 30, 2017

While the phrase Artificial Intelligence has been around since the first human wondered if she could go further if she had access to entities with inorganic intelligence, it truly jumped the shark in 2016. trillion pictures in 2016. Most Deep Learning methods involve artificial neural networks, modeling how our bran works.

Marketing Analytics 166

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Rita Sallam

JULY 22, 2016

Gartner revamped the BI and Analytics Magic Quadrant in 2016 to reflect the mainstreaming of this market disruption. Q2: Would you consider Sisense better than others in handling big and unstructured data? Sisense supports a wide range of relational, NoSQL and big data sources. Research VP, Business Analytics and Data Science.

Analytics 40

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Domino Data Lab

AUGUST 8, 2019

Paco Nathan ‘s latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams. In this episode I’ll cover themes from Sci Foo and important takeaways that data science teams should be tracking. Introduction.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

Domino Data Lab

JULY 1, 2021

In this blog post we present the Named Entity Recognition problem and show how a BiLSTM-CRF model can be fitted using a freely available annotated corpus and Keras. The model achieves relatively high accuracy and all data and code is freely available in the article. The CRF model. What is Named Entity Recognition?

Modeling 111

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Domino Data Lab

AUGUST 22, 2019

The excerpt covers how to create word vectors and utilize them as an input into a deep learning model. While the field of computational linguistics, or Natural Language Processing (NLP), has been around for decades, the increased interest in and use of deep learning models has also propelled applications of NLP forward within industry.

Deep Learning 111

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Andrew White

JANUARY 9, 2022

Data mesh seems to mean a network of systems and hubs connected to help cope with silos. Perhaps a data mesh is what a pure architecture might design. There are many tactics to employ that might help – including data literacy; storytelling; use of our value pyramid/business value model; use of our decision intelligence model.

Analytics 64

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The Unofficial Google Data Science Blog

NOVEMBER 17, 2020

by TAMAN NARAYAN & SEN ZHAO A data scientist is often in possession of domain knowledge which she cannot easily apply to the structure of the model. On the one hand, basic statistical models (e.g. On the other hand, sophisticated machine learning models are flexible in their form but not easy to control.

Machine Learning 108

Machine Learning Deep Learning Modeling Interactive 108

datapine

AUGUST 29, 2022

Best for : the new intern who has no idea what data science even means. An excerpt from a rave review : “I would definitely recommend this book to everyone interested in learning about data from scratch and would say it is the finest resource available among all other Big Data Analytics books.”.

Big Data 263

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The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

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O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. The term “ML” is No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

The Unofficial Google Data Science Blog

APRIL 23, 2024

Experiments, Parameters and Models At Youtube, the relationships between system parameters and metrics often seem simple — straight-line models sometimes fit our data well. That is true generally, not just in these experiments — spreading measurements out is generally better, if the straight-line model is a priori correct.

Experimentation 52

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CIO Business Intelligence

APRIL 15, 2022

But according to the UK’s Turing Institute, a national center for data science and AI, the predictive tools made little to no difference. In March 2016, Microsoft learned that using Twitter interactions as training data for machine learning algorithms can have dismaying results.

Analytics 145

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Insight

JUNE 10, 2019

In 2016, Uber published its Uber Elevate White Paper , setting its aspirations on providing on-demand air taxis from San Francisco to San Jose for about $130. Alexander | June 1, 2016 Training a c onvolution neural network (CNN) to spot helipads The solution I developed rests on retraining a CNN to recognize helipads in aerial images.

Breakeven 69

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Rita Sallam

JULY 31, 2017

And yet organizations’ processes for preparing data for analysis, analyzing data, building advanced analytics models, interpreting results and telling stories with data remain largely manual and prone to bias. It will transform how users interact with data, and how they consume and act on insights.

Marketing 72

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Data Science 101

OCTOBER 12, 2023

The first project we did used NLP for finance contracts (this was 2016). The semantic kernel is an API designed to help devs use AI services, especially large language models like GPT-3.5 By random chance, we were looking for technical help in Finance and a research lab was looking for meaningful projects.

Finance 64

Finance Machine Learning Interactive Manufacturing 64

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

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Domino Data Lab

OCTOBER 23, 2019

This chapter will explore the numbers behind the numbers using ML and then creating an API to serve out the ML model. This means covering details like setting up your environment, deployment, and monitoring, in addition to creating models on clean data. Gathering the Data. Phrasing the Problem. questions to ask.

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IBM Big Data Hub

SEPTEMBER 11, 2023

On June 7, 1983, a product was born that would revolutionize how organizations would store, manage, process , and query their data: IBM Db2. Codd published his famous paper “ A Relational Model of Data for Large Shared Data Banks.” In 1969, retired IBM Fellow Edgar F. Chamberlin and Raymond F.

Machine Learning 85

Machine Learning Cost-Benefit IoT Data Warehouse 85

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. We are all familiar with linear and logistic regression models. The top few features with the largest weight (i.e.,

IT 68

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Insight

MARCH 18, 2019

Bias in Machine Learning Algorithms (Bottom Photos Source: ProPublica ; Top Photos Source: Pexels.com) Biases in predictive modeling are a widespread issue Machine learning and AI applications are used across industries, from recommendation engines to self-driving cars and more.

Machine Learning 77

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Domino Data Lab

AUGUST 1, 2021

In this article we cover explainability for black-box models and show how to use different methods from the Skater framework to provide insights into the inner workings of a simple credit scoring neural network model. 2016) for an example of this technique (LIME). See Ribeiro et al.

Modeling 139

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Cloudera

JANUARY 22, 2019

With annual revenues exceeding $100 million, Cloudera is positioned as a “large player” in Big Data Fabric* which is conceptualized by Forrester in Figure 1 below. Source: Big Data Fabric Drives Innovation And Growth, Noel Yuhanna, March 8 2016. Shared Data Catalog to preserve context and enforce Governance.

Big Data 45

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Smarten

SEPTEMBER 9, 2024

The Definition and Evolution of the Citizen Data Scientist Role The world-renowned technology research firm, Gartner, first introduced the concept of the Citizen Data Scientist in 2016. Who is a Citizen Data Scientist ? Citizen Data Scientist candidates may also be IT team members who are interested in data science.

Prescriptive Analytics 52

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CIO Business Intelligence

OCTOBER 11, 2022

As one of the world’s largest biopharmaceutical companies, AstraZeneca pushes the boundaries of science to deliver life-changing medicines that create enduring value for patients and society. Before AI Bench, every data science project was like a separate IT project. Modernize your approach to data and technology.

Machine Learning 145

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Domino Data Lab

SEPTEMBER 25, 2019

Conducting exploratory analysis and extracting meaningful insights from data are core components of research and data science work. Time series data is commonly encountered. We see it when working with log data, financial data, transactional data, and when measuring anything in a real engineering system.

Forecasting 87

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Smarten

SEPTEMBER 9, 2024

What is a Citizen Data Scientist, What is Their Role, What are the Benefits of Citizen Data Scientists…and More! The term, ‘Citizen Data Scientist’ has been around for a number of years. In fact, the world-renowned technology research firm, Gartner, first introduced the concept in 2016. So, let’s get started.

Consulting 52

Consulting Cost-Benefit Data Science Business Intelligence 52

Domino Data Lab

MARCH 27, 2019

Special thanks to Addison-Wesley Professional for permission to excerpt the following “Manipulating data with dplyr” chapter from the book, Programming Skills for Data Science: Start Writing Code to Wrangle, Analyze, and Visualize Data with R. Data scientists spend countless hours wrangling data.

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