Data Science and Beyond

JULY 28, 2017

Ideally, I wanted a well-paid data science-y remote job with an established distributed tech company that offers a good life balance and makes products I care about. While data wrangler may sound less sexy than data scientist , reading the job ad led me to believe that the position may involve interesting data science work.

Data Science 81

Data Science Testing Measurement Uncertainty 81

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 ”. And an LSOS is awash in data, right?

Experimentation 41

Experimentation Statistics Metrics Measurement 41

IBM Big Data Hub

APRIL 18, 2024

Beyond cost savings, organizations seek tangible ways to measure gen AI’s return on investment (ROI), focusing on factors like revenue generation, cost savings, efficiency gains and accuracy improvements, depending on the use case. The AGI would need to handle uncertainty and make decisions with incomplete information.

Cost-Benefit 97

Cost-Benefit Modeling Manufacturing Interactive 97

The Unofficial Google Data Science Blog

APRIL 23, 2024

the weight given to Likes in our video recommendation algorithm) while $Y$ is a vector of outcome measures such as different metrics of user experience (e.g., Crucially, it takes into account the uncertainty inherent in our experiments. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

AWS Big Data

AUGUST 7, 2023

Many businesses use different software tools to analyze historical data and past patterns to forecast future demand and trends to make more accurate financial, marketing, and operational decisions. Forecasting acts as a planning tool to help enterprises prepare for the uncertainty that can occur in the future.

Forecasting 80

Forecasting Data Warehouse Machine Learning Modeling 80

bridgei2i

JUNE 3, 2020

Consumers feel threatened by the prolonged uncertainty, not having had to deal with anything like it, in their lives. Forecasting models have to be created keeping in mind this uncertainty, and key indicators need to be identified for early detection. COVID-19 as a social zeitgeist and its impact on the consumer psyche (Gartner).

Manufacturing 52

Manufacturing Forecasting Uncertainty Digital Transformation 52

The Unofficial Google Data Science Blog

DECEMBER 4, 2019

by THOMAS OLAVSON Thomas leads a team at Google called "Operations Data Science" that helps Google scale its infrastructure capacity optimally. This classification is based on the purpose, horizon, update frequency and uncertainty of the forecast. Our team does a lot of forecasting.

Forecasting 113

Forecasting Data Science Statistics Uncertainty 113

The Unofficial Google Data Science Blog

APRIL 17, 2017

Quantification of forecast uncertainty via simulation-based prediction intervals. We conclude with an example of our forecasting routine applied to publicly available Turkish Electricity data. Prediction Intervals A statistical forecasting system should not lack uncertainty quantification.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

Domino Data Lab

JUNE 2, 2019

Co-chair Paco Nathan provides highlights of Rev 2 , a data science leaders summit. We held Rev 2 May 23-24 in NYC, as the place where “data science leaders and their teams come to learn from each other.” If you lead a data science team/org, DM me and I’ll send you an invite to data-head.slack.com ”.

Data-driven 110

Data-driven Data Science Machine Learning Modeling 110

O'Reilly on Data

OCTOBER 13, 2020

Therefore, the PM should consider the team that will reconvene whenever it is necessary to build out or modify product features that: ensure that inputs are present and complete, establish that inputs are from a realistic (expected) distribution of the data, and trigger alarms, model retraining, or shutdowns (when necessary).

Management 244

Management Metrics Machine Learning Modeling 244

The Unofficial Google Data Science Blog

JULY 22, 2020

For this reason we don’t report uncertainty measures or statistical significance in the results of the simulation. From a Bayesian perspective, one can combine joint posterior samples for $E[Y_i | T_i=t, E_i=j]$ and $P(E_i=j)$, which provides a measure of uncertainty around the estimate.

Experimentation 95

Experimentation Statistics Testing Strategy 95

The Unofficial Google Data Science Blog

AUGUST 27, 2019

The measurement may be biased if our samples are generated from a procedure that samples without replacement, such as reservoir sampling , especially if some items have disproportionate weight, i.e., $p(v_i) cdot n$ is large. The difference between the two is whether the same item can appear in the sample multiple times. How Many Strata?

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Insight

FEBRUARY 19, 2020

Using variability in machine learning predictions as a proxy for risk can help studio executives and producers decide whether or not to green light a film project Photo by Kyle Smith on Unsplash Originally posted on Toward Data Science. Are you interested in working on high-impact projects and transitioning to a career in data?

Risk 67

Risk ROI Modeling Metrics 67

Data Science and Beyond

JANUARY 7, 2019

Therefore, bootstrapping has been promoted as an easy way of modelling uncertainty to hackers who don’t have much statistical knowledge. Confidence intervals are a common way of quantifying the uncertainty in an estimate of a population parameter. Don’t compare confidence intervals visually.

Statistics 77

Statistics Uncertainty Testing Modeling 77

Peter James Thomas

AUGUST 12, 2018

This piece was prompted by both Olaf’s question and a recent article by my friend Neil Raden on his Silicon Angle blog, Performance management: Can you really manage what you measure? Data may be perfectly valid, but still not represent reality. It is hard to account for such tweaking in measurement systems.

Metrics 49

Metrics Statistics Data Quality Measurement 49

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

JUNE 23, 2019

These measurement-obsessed companies have an advantage when it comes to AI. Google, Facebook, other leaders, they really have set up a culture of extreme measurement where every part of their product experience is instrumented to optimize clicks and drive user engagement. They have the foundations of data infrastructure.

Management 110

Management Machine Learning Experimentation Metrics 110

The Unofficial Google Data Science Blog

MAY 31, 2016

It is important that we can measure the effect of these offline conversions as well. Panel studies make it possible to measure user behavior along with the exposure to ads and other online elements. Wouldn't it be great if we didn't require individual data to estimate an aggregate effect? days or weeks).

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

APRIL 16, 2019

The metrics to measure the impact of the change might not yet be established. Typically, it takes a period of back-and-forth between logging and analysis to gain the confidence that a metric is actually measuring what we designed for it to measure. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

MARCH 13, 2017

By MUKUND SUNDARARAJAN, ANKUR TALY, QIQI YAN Editor's note: Causal inference is central to answering questions in science, engineering and business and hence the topic has received particular attention on this blog. 2009, " Measuring invariances in deep networks ". CoRR, 2016. [3] Goodfellow, Quoc V. Le, Andrew M.

IT 68

IT Visualization Modeling Uncertainty 68

The Unofficial Google Data Science Blog

APRIL 19, 2021

The numerical value of the signal became decoupled from the event it was measuring even as the ordinal value remained unchanged. bar{pi} (1 - bar{pi})$: This is the irreducible loss due to uncertainty. And users may start receiving a lot more spam! The further your predictions are from the global average the more you improve the loss.

Modeling 122

Modeling IT Metrics Testing 122

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

The result is that experimenters can’t afford to be sloppy about quantifying uncertainty. These typically result in smaller estimation uncertainty and tighter interval estimates. And since the metric average is different in each hour of day, this is a source of variation in measuring the experimental effect.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

Smarten

JULY 18, 2022

By incorporating analytics into day-to-day activities and allowing access for business users, the business can encourage the transition from business user to Citizen Data Scientist and create a comprehensive system of analytics with governance and collaboration to ensure security, appropriate access, mobile use and fact-based decision-making.

Uncertainty 52

Uncertainty Predictive Analytics Forecasting Analytics 52

Jen Stirrup

NOVEMBER 15, 2022

How can systems thinking and data science solve digital transformation problems? Understandably, organizations focus on the data and the technology since data retrieval is often viewed as a data problem. However, the thrust here is not to diminish data science or data engineering.

Data Science 145

Data Science Digital Transformation Data-driven Measurement 145