O'Reilly on Data

NOVEMBER 13, 2018

Below are the top search topics on our training platform: Beyond “search,” note that we’re seeing strong growth in consumption of content related to ML across all formats—books, posts, video, and training. Data Platforms. How to build analytic products in an age when data privacy has become critical”. Model lifecycle management.

Machine Learning 224

Machine Learning Risk Management Statistics 224

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

O'Reilly on Data

JULY 28, 2020

There’s a substantial literature about ethics, data, and AI, so rather than repeat that discussion, we’ll leave you with a few resources. Ethics and Data Science is a short book that helps developers think through data problems, and includes a checklist that team members should revisit throughout the process.

Marketing 363

Marketing Experimentation Metrics Testing 363

Data Science 101

JANUARY 15, 2020

We all are familiar with experiments , we read about them in books or newspapers. Bias ( syatematic unfairness in data collection ) can be a potential problem in experiments and we need to take it into account while designing experiments. Randomization is a very effective way to solve the bias problem.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Domino Data Lab

OCTOBER 2, 2019

This article covers causal relationships and includes a chapter excerpt from the book Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications by Andrew Kelleher and Adam Kelleher. Data collected from this system reflects the way the world works when we just observe it.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

Domino Data Lab

FEBRUARY 4, 2019

In Paco Nathan ‘s latest column, he explores the role of curiosity in data science work as well as Rev 2 , an upcoming summit for data science leaders. Welcome back to our monthly series about data science. and dig into details about where science meets rhetoric in data science.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Occam's Razor

MARCH 30, 2017

The solution was to leverage real-time signals like bad weather, flight delays at 5,145 airports, and other such data, combine that with ML powered algorithms to automate ads and messaging in the proximity of local airports. 60% increase in bookings in targeted areas. We are needed today because data collection is hard.

Marketing Analytics 166

Marketing Analytics Marketing Analytics Deep Learning 166

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. – back to the structure of the dataset. Let’s look through some antidotes. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

IBM Big Data Hub

FEBRUARY 8, 2023

The IBM AI Governance solution automates across the AI lifecycle from data collection, model building, deploying and monitoring. This comprehensive solution comes without the excessive costs of switching from your current data science platform. Read the e-book or visit the website.

Risk Management 52

Risk Management Risk Metadata Modeling 52

The Unofficial Google Data Science Blog

APRIL 17, 2017

Our rationale was in accord with the views expressed in the online forecasting book by Hyndman and Athana­sopou­los [1], who after mentioning the potential utility of an "explanatory model" write: However, there are several reasons a forecaster might select a time series model rather than an explanatory model.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

MARCH 3, 2019

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise. Welcome back to our monthly series about data science! This month, the theme is not specifically about conference summaries; rather, it’s about a set of follow-up surveys from Strata Data attendees.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

Andrew White

JANUARY 9, 2022

This research does not tell you where to do the work; it is meant to provide the questions to ask in order to work out where to target the work, spanning reporting/analytics (classic), advanced analytics and data science (lab), data management and infrastructure, and D&A governance. The last item should be updated in 2022.

Analytics 64

Analytics Measurement Modeling Data-driven 64

Domino Data Lab

OCTOBER 23, 2019

As Domino is committed to supporting data scientists and accelerating research, we reached out to Addison-Wesley Professional (AWP) Pearson for the appropriate permissions to excerpt “Predicting Social-Media Influence in the NBA” from the book, Pragmatic AI: An Introduction to Cloud-Based Machine Learning by Noah Gift. In Figure 6.1,

Statistics 59

Statistics Machine Learning Testing Modeling 59

Domino Data Lab

AUGUST 20, 2019

Many thanks to AWP Pearson for the permission to excerpt “Manual Feature Engineering: Manipulating Data for Fun and Profit” from the book, Machine Learning with Python for Everyone by Mark E. We discussed this as far back as Chapter 1 [in the book]. There is also a complementary Domino project available.

Testing 68

Testing Modeling Interactive Measurement 68

Alation

FEBRUARY 17, 2023

In this blog, I’ll address some of the questions we did not have time to answer live, pulling from both Dr. Reichental’s book as well as my own experience as a data governance leader for 30+ years. Can you have proper data management without establishing a formal data governance program?

Data Governance 52

Data Governance Data Quality Metadata Cost-Benefit 52

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52