FineReport

DECEMBER 11, 2019

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. Effective use of big data helps companies analyze critical information more accurately, ultimately improving operational efficiency, reducing costs, reducing risk, accelerating innovation, and increasing revenue.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

Domino Data Lab

JANUARY 24, 2019

This group of solutions targets code-first data scientists who use statistical programming languages and spend their days in computational notebooks (e.g., These data scientists require the flexibility to use a constantly-evolving software and hardware stack to optimize each step of their model lifecycle. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

The Unofficial Google Data Science Blog

JULY 22, 2020

One reason to do ramp-up is to mitigate the risk of never before seen arms. For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Jet Global

APRIL 27, 2023

Tax teams of multinational enterprises (MNEs) in the manufacturing industry face increasing challenges to manage business and market risks effectively. For your tax team to be agile, you’ll need to optimize tax technology and processes so you can both spot data insights and mitigate risk.

Manufacturing 52

Manufacturing Marketing Experimentation Reporting 52

Corinium

APRIL 25, 2019

For example, P&C insurance strives to understand its customers and households better through data, to provide better customer service and anticipate insurance needs, as well as accurately measure risks. Life insurance needs accurate data on consumer health, age and other metrics of risk. Now, there is a data risk here.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

O'Reilly on Data

MARCH 23, 2023

What is it, how does it work, what can it do, and what are the risks of using it? Many of these go slightly (but not very far) beyond your initial expectations: you can ask it to generate a list of terms for search engine optimization, you can ask it to generate a reading list on topics that you’re interested in. What Are the Risks?

IT 262

IT Modeling Testing Risk 262

Occam's Razor

NOVEMBER 17, 2008

There is a tendency to think experimentation and testing is optional. You can start for free with a superb tool: Google's Website Optimizer. 4 Big Bets, Low Risks, Happy Customers. You have just launched something risky, yet you have controlled the risk by reducing exposure of the risky idea. Think about it this way.

Testing 112

Testing Experimentation Risk Optimization 112

AWS Big Data

JULY 3, 2023

This helps traders determine the potential profitability of a strategy and identify any risks associated with it, enabling them to optimize it for better performance. Buy Experimentation findings The following table shows Sharpe Ratios for various holding periods and two different trade entry points: announcement and effective dates.

Snapshot 87

Snapshot Data Lake Testing Strategy 87

Domino Data Lab

MAY 8, 2019

That’s a risk in case, say, legislators – who don’t understand the nuances of machine learning – attempt to define a single meaning of the word interpret. Given how so much of IT gets driven by concerns about risks and costs, in practice auditability tops the list for many business stakeholders. Ergo, less interpretable.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

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. Yet, this challenge is not insurmountable. for what is and isn’t possible) to address these challenges.

Management 110

Management Machine Learning Experimentation Metrics 110

Occam's Razor

JANUARY 9, 2012

I've discovered that if we can just get them to imagine a better existence, undertake serious risks, experiment with new better ideas, and spend money executing them… they will ask for more robust measurement! All while constantly optimizing your portfolio via controlled experiments. AND you can control for risk!

Marketing 140

Marketing B2B Measurement Risk 140

Cloudera

NOVEMBER 25, 2020

How can he make it easy to see statistics, and do calculations, on discovered commonalities, across structured and unstructured data? Innovate on serviceability and optimize utilization. It would enable faster experimentation with easy, protected, and governed access to a variety of data.

Data Warehouse 57

Data Warehouse Unstructured Data Analytics Visualization 57

Occam's Razor

MARCH 23, 2010

Part of it is fueled by a vocal minority genuinely upset that 10 years on we are still not a statistically powered bunch doing complicated analysis that is shifting paradigms. Yet case studies in some sense reduced risk, even if they were simply over blown marketing fluff written by the vendor. Part of it fueled by some Consultants.

Analytics 118

Analytics Consulting Metrics Experimentation 118

Domino Data Lab

AUGUST 26, 2019

When hyperparameters are also used for optimization and tuned for a specific dataset, they also impact predictive accuracy. Searching is a very general approach to solving or optimizing problems. I will continue using parameters (which are optimized during training) and hyperparameters (which are tuned via cross-validation).

Testing 79

Testing Modeling Machine Learning Metrics 79

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

IBM Big Data Hub

SEPTEMBER 25, 2023

With a few taps on a mobile device, riders request a ride; then, Uber’s algorithms work to match them with the nearest available driver and calculate the optimal price. This allowed them to focus on SQL-based query optimization to the nth degree. Large, untested workloads run the risk of hogging all the resources.

OLAP 90

OLAP Data Lake Data-driven Snapshot 90