Domino Data Lab

JANUARY 24, 2019

In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Over the last couple years, it would be hard to blame anyone for being overwhelmed looking at the data science platform market landscape. Proprietary (often GUI-driven) data science platforms.

Data Science 65

Data Science Marketing Data-driven Statistics 65

CIO Business Intelligence

MARCH 21, 2022

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals. Learn from data scientists about their responsibilities and find out how to launch a data science career. |

Unstructured Data 137

Unstructured Data Data Analytics Analytics Structured Data 137

O'Reilly on Data

JULY 28, 2020

It’s often difficult for businesses without a mature data or machine learning practice to define and agree on metrics. Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business.

Marketing 362

Marketing Experimentation Metrics Testing 362

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Most data scientists are strong in one or two of these areas, but not all three. This frees up data scientists to focus on more complex analytical tasks.

ROI 52

ROI Machine Learning Experimentation Data Science 52

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. And we can keep repeating this approach, relying on intuition and luck. Why experiment with several parameters concurrently?

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

FineReport

DECEMBER 19, 2019

When it comes to data analysis, from database operations, data cleaning, data visualization , to machine learning, batch processing, script writing, model optimization, and deep learning, all these functions can be implemented with Python, and different libraries are provided for you to choose. From Google.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

by AMIR NAJMI & MUKUND SUNDARARAJAN Data science is about decision making under uncertainty. Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

Insight

APRIL 28, 2020

Originally posted on Open Data Science (ODSC). In this article, we share some data-driven advice on how to get started on the right foot with an effective and appropriate screening process. Designing a Data Science Interview Onsite interviews are indispensable, but they are time-consuming. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

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. You saw in the previous chapter that conditioning can break statistical dependence.

Machine Learning 68

Machine Learning Measurement Modeling Testing 68

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 96

Cost-Benefit Modeling Manufacturing Interactive 96

Data Science and Beyond

JULY 22, 2018

I got my first data science job in 2012, the year Harvard Business Review announced data scientist to be the sexiest job of the 21st century. Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

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

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

Business Over Broadway

FEBRUARY 21, 2021

The top activities across all data roles were related to analyzing data to influence decisions and building prototypes. The practice of data science is about extracting value from data to help inform decision making and improve algorithms. But what exactly do different data professionals do at work?

Machine Learning 60

Machine Learning Statistics Data Science Experimentation 60

Domino Data Lab

JUNE 23, 2019

Pete Skomoroch ’s “ Product Management for AI ”session at Rev provided a “crash course” on what product managers and leaders need to know about shipping machine learning (ML) projects and how to navigate key challenges. Be aware that machine learning often involves working on something that isn’t guaranteed to work.

Management 110

Management Machine Learning Experimentation Metrics 110

Domino Data Lab

MAY 8, 2019

I’ve been out themespotting and this month’s article features several emerging threads adjacent to the interpretability of machine learning models. The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Machine learning model interpretability.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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

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. Introduction.

Testing 79

Testing Modeling Machine Learning Metrics 79

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