Smarten

MAY 11, 2017

The Smarten Advanced Data Discovery gives users the freedom to leverage data beyond simple visual data analysis and dashboards. Advanced Data Discovery allows business users to perform early prototyping and to test hypothesis without the skills of a data scientist, ETL or developer.

Experimentation 49

Experimentation Visualization Business Intelligence Predictive Analytics 49

FineReport

DECEMBER 19, 2019

At the same time, it also advocates visual exploratory analysis. The visualization component library of FineReport is very rich. It can be used as a portal for data reporting, or as a platform for business analysis. Data Analysis Libraries. Pandas is a Python data science library that is constantly improving.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

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. ML model interpretability and data visualization. back to the structure of the dataset.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

DECEMBER 4, 2020

This tutorial will show how easy it is to integrate and use Pumas in the Domino Data Science Platform , and we will carry out a simple non-compartmental analysis using a freely available dataset. The Domino data science platform empowers data scientists to develop and deliver models with open access to the tools they love.

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

IBM Big Data Hub

APRIL 18, 2024

Achieving these feats is accomplished through a combination of sophisticated algorithms, natural language processing (NLP) and computer science principles. LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

The Unofficial Google Data Science Blog

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. Naturally, this issue is of particular concern to us in the Play Data Science team.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. Figure 4 is a visual representation of these steps.

Testing 58

Testing Experimentation Measurement Modeling 58

Alation

SEPTEMBER 28, 2022

Moreover, as the highly fragmented technologies of the data industry are now ever-expanding, having a DataOps mindset and infrastructure helps data teams to build a sustainable way of adopting new technologies as a company grows. This requires that data engineers embrace learning and integrating new technologies, such as AI tools.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. They have the foundations of data infrastructure. Yet, this challenge is not insurmountable.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

OCTOBER 21, 2015

Our post describes how we arrived at recent changes to design principles for the Google search page, and thus highlights aspects of a data scientist’s role which involve practicing the scientific method. There has been debate as to whether the term “data science” is necessary. Some don’t see the point.

Slice and Dice 45

Slice and Dice Experimentation Data-driven Data Science 45

Insight

MARCH 12, 2020

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. The most powerful approach for the first task is to use a ‘language model’ (LM), i.e. a statistical model of natural language. features) and metadata (i.e.

Modeling 59

Modeling Metadata Publishing Sales 59

DataRobot Blog

OCTOBER 27, 2022

As highlighted in the DataRobot interface above, the Data Drift tab is enhanced with a Drill Down section for users to visualize drift details. This is important as data drift can look different at different time granularities; drift can happen at any time and at any rate. That insight requires looking at specific time slices.

Data Science 52

Data Science Experimentation Visualization Modeling 52

Domino Data Lab

AUGUST 22, 2019

Data scientists and researchers require an extensive array of techniques, packages, and tools to accelerate core work flow tasks including prepping, processing, and analyzing data. Utilizing NLP helps researchers and data scientists complete core tasks faster. Visualizing data using t-SNE. Example 11.6 Example 11.9

Deep Learning 111

Deep Learning Modeling Metrics Testing 111