Data Science 101

OCTOBER 15, 2023

That’s going to use the various existing computing technology in and around the experimentation of this new capability. The timescale of deep tech is always so I’d urge anyone curious about a career in quantum computing to read @drchrisferrie ’s perfectly titled (and hilarious) book. What can you say about the book you are writing?

Risk 107

Risk Marketing Consulting Technology 107

Data Science 101

JANUARY 15, 2020

We all are familiar with experiments , we read about them in books or newspapers. Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Bias can cause a huge error in experimentation results so we need to avoid them.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Insight

MARCH 12, 2020

There was only one problem: literary agents, the gatekeepers of the publishing industry, kept rejecting the book?—?often With breaking this bottleneck in mind, I’ve used my time as an Insight Data Science Fellow to build the AIgent, a web-based neural net to connect writers to representation. often without even looking at it.

Modeling 59

Modeling Metadata Publishing Sales 59

The Unofficial Google Data Science Blog

JANUARY 14, 2016

by AMIR NAJMI Running live experiments on large-scale online services (LSOS) is an important aspect of data science. We must therefore maintain statistical rigor in quantifying experimental uncertainty. In this post we explore how and why we can be “ data-rich but information-poor ”.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

Sisense

JANUARY 21, 2020

In general, it is not possible to give a rule of thumb about when data should be partitioned or combined. As an example, I’ll present a case from The Book of Why by Judea Pearl. To better understand when the data should be grouped, you should be familiar with causal inference. It really depends on the circumstances.

Testing 104

Testing Risk Data-driven Statistics 104

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

Domino Data Lab

AUGUST 26, 2019

This article provides an excerpt of “Tuning Hyperparameters and Pipelines” from the book, Machine Learning with Python for Everyone by Mark E. Data scientists, machine learning (ML) researchers, and business stakeholders have a high-stakes investment in the predictive accuracy of models. in the book. ]. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

O'Reilly on Data

OCTOBER 19, 2021

It has far-reaching implications as to how such applications should be developed and by whom: ML applications are directly exposed to the constantly changing real world through data, whereas traditional software operates in a simplified, static, abstract world which is directly constructed by the developer. Data Science Layers.

IT 347

IT Testing Experimentation Software 347

Domino Data Lab

AUGUST 22, 2019

Many thanks to Addison-Wesley Professional for providing the permissions to excerpt “Natural Language Processing” from the book, Deep Learning Illustrated by Krohn , Beyleveld , and Bassens. Utilizing NLP helps researchers and data scientists complete core tasks faster. and 2.6) [ in the book]. Introduction.

Deep Learning 111

Deep Learning Modeling Metrics Testing 111

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. We’ll use an example from Judea Pearl’s book. A complementary Domino project is available. .

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

The Unofficial Google Data Science Blog

JUNE 30, 2016

By IVAN DIAZ & JOSEPH KELLY Determining the causal effects of an action—which we call treatment—on an outcome of interest is at the heart of many data analysis efforts. In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects.

Statistics 44

Statistics Optimization Modeling Experimentation 44

DataRobot Blog

SEPTEMBER 22, 2022

Leading French organizations are recognizing the power of AI to accelerate the impact of data science. Next week, we’re excited to partner with industry leaders at Big Data & AI Paris, alongside a launch of a dedicated French language microsite. Why and How to Leverage Data Science at CPAM Loire Atlantique ?

Big Data 52

Big Data Insurance Experimentation Data Science 52