The Unofficial Google Data Science Blog

APRIL 17, 2017

Selection and aggregation of forecasts from an ensemble of models to produce a final forecast. Calendaring was therefore an explicit feature of models within our framework, and we made considerable investment in maintaining detailed regional calendars. Adjustments for effects: holiday, seasonality, and day-of-week effects.

Forecasting 83

Forecasting Modeling Statistics Uncertainty 83

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. Figure 2: Spreading measurements out makes estimates of model (slope of line) more accurate. And sometimes even if it is not[1].)

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

Occam's Razor

MARCH 12, 2012

Clickstream + qualitative data + rigorous statistical analysis of outcomes + deep mining of data from competitive intelligence sources + rapid experiments + more. Avoiding big disappointment and the hows were on my mind as I prepared my keynote for Strata 2012 Big Data conference.

Big Data 127

Big Data Data-driven Unstructured Data Marketing 127

Domino Data Lab

JULY 22, 2019

how “the business executives who are seeing the value of data science and being model-informed, they are the ones who are doubling down on their bets now, and they’re investing a lot more money.” He was saying this doesn’t belong just in statistics. Key highlights from the session include. Transcript. Tukey did this paper.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

AWS Big Data

JUNE 28, 2023

Create a role in the target account with the following permissions: { "Version":"2012-10-17", "Statement":[ { "Effect":"Allow", "Action":[ "redshift:DescribeClusters", "redshift-serverless:ListNamespaces" ], "Resource":[ "*" ] } ] } The role must have the following trust policy, which specifies the target account ID. Choose Create policy.

Data Warehouse 81

Data Warehouse Analytics Metrics Dashboards 81

Domino Data Lab

APRIL 21, 2021

We’ll use a gradient boosting technique via XGBoost to create a model and I’ll walk you through steps you can take to avoid overfitting and build a model that is fit for purpose and ready for production. Let’s also look at the basic descriptive statistics for all attributes. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

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. Things have changed considerably since 2012.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

DataRobot Blog

APRIL 4, 2022

MMM stands for Marketing Mix Model and it is one of the oldest and most well-established techniques to measure the sales impact of marketing activity statistically. As with any type of statistical model, data is key and GIGO (“Garbage In, Garbage Out”) principle definitely applies. Classical Modeling Considerations.

Machine Learning 52

Machine Learning Sales Measurement ROI 52

Domino Data Lab

AUGUST 8, 2019

Meanwhile, many organizations also struggle with “late in the pipeline issues” on model deployment in production and related compliance. then building machine learning models to recommend methods and potential collaborators to scientists. The real fun begins once models get deployed in production.

Data Science 71

Data Science Machine Learning Data Governance Statistics 71

The Unofficial Google Data Science Blog

JULY 11, 2017

SCOTT Time series data are everywhere, but time series modeling is a fairly specialized area within statistics and data science. This post describes the bsts software package, which makes it easy to fit some fairly sophisticated time series models with just a few lines of R code. by STEVEN L. Forecasting (e.g.

Forecasting 64

Forecasting Statistics Modeling Software 64

Smart Data Collective

OCTOBER 17, 2022

zettabytes in 2012. Consider the statistics from Domo that the number of home-based workers has increased from roughly 15% 18 months ago to more than 50% now (it was close to 100% at times during the epidemic). Reports and models stutter as they try to interpret the massive amounts of data flowing through them. Poor performance.

Big Data 104

Big Data Data-driven Recreation/Entertainment Data Governance 104

The Unofficial Google Data Science Blog

MARCH 31, 2016

KUEHNEL, and ALI NASIRI AMINI In this post, we give a brief introduction to random effects models, and discuss some of their uses. Through simulation we illustrate issues with model fitting techniques that depend on matrix factorization. Random effects models are a useful tool for both exploratory analyses and prediction problems.

Modeling 40

Modeling Statistics Advertising Testing 40

CIO Business Intelligence

JULY 2, 2022

For example, IDC data shows that 2021 there was a boom in monitor sales, with the highest volume of monitors shipped since 2012, at 143.6 million , and this figure is likely due to many professionals giving their home offices a refresh. Additionally, this upgrade can be used as an opportunity to help improve workplace productivity.

Statistics 95

Statistics Sales Visualization Technology 95

CIO Business Intelligence

JULY 5, 2022

For example, IDC data shows that 2021 there was a boom in monitor sales, with the highest volume of monitors shipped since 2012, at 143.6 million , and this figure is likely due to many professionals giving their home offices a refresh. Additionally, this upgrade can be used as an opportunity to help improve workplace productivity.

Statistics 89

Statistics Sales Visualization Technology 89

IBM Big Data Hub

OCTOBER 12, 2023

Early iterations of the AI applications we interact with most today were built on traditional machine learning models. These models rely on learning algorithms that are developed and maintained by data scientists. Reactive AI stems from statistical math and can analyze vast amounts of data to produce a seemingly intelligence output.

Machine Learning 75

Machine Learning Deep Learning Interactive Modeling 75

AWS Big Data

MARCH 21, 2024

recursive Connect to your RDS for MySQL cluster and create a database or schema for the TICKIT data model, verify that the tables in that schema have a primary key, and initiate the load process: mysql -h -u admin -p password --local-infile=1 Use the following CREATE TABLE commands. Choose Create policy.

Data Warehouse 84

Data Warehouse Metrics Optimization Statistics 84

Depict Data Studio

NOVEMBER 4, 2022

I published my first video on November 4, 2012…. ” I’d been a formal statistics tutor and Spanish tutor in college through a small invite-only program. Being a full-time YouTuber is an entirely different business model from being a dataviz speaker and course creator. and the rest was history! My Latest YouTube Stats.

Dashboards 75

Dashboards Testing Software Consulting 75

Alation

JULY 7, 2022

In 2012 Davenport and Patil declared the data scientist was “ The Sexiest Job of the 21st Century.” The Bureau of Labor Statistics projects the job outlook for data scientists to grow 22% from 2020 to 2030. The rapid growth of data roles critical to data-centric business models demonstrate an awareness of this need.

Metadata 52

Metadata Data-driven Insurance Statistics 52

The Unofficial Google Data Science Blog

MAY 31, 2016

Statistical power is traditionally given in terms of a probability function, but often a more intuitive way of describing power is by stating the expected precision of our estimates. This is a quantity that is easily interpretable and summarizes nicely the statistical power of the experiment.

Advertising 42

Advertising Testing Sales Statistics 42

The Unofficial Google Data Science Blog

AUGUST 27, 2019

But importance sampling in statistics is a variance reduction technique to improve the inference of the rate of rare events, and it seems natural to apply it to our prevalence estimation problem. Statistical Science. Statistics in Biopharmaceutical Research, 2010. [4] 5] Ray Chambers, Robert Clark (2012). 7] Neyman, J.

Metrics 98

Metrics Statistics Uncertainty Optimization 98

Domino Data Lab

MARCH 3, 2019

What’s been the impact of using ML models on culture and organization? Who builds their models? We also used maturity , in other words how long had an enterprise organization been deploying ML models in production? “Three surveys of AI adoption reveal key advice from more mature practices”. Management.

Data Science 49

Data Science Deep Learning Machine Learning Modeling 49

datapine

DECEMBER 28, 2021

1) What Is A Misleading Statistic? 2) Are Statistics Reliable? 3) Misleading Statistics Examples In Real Life. 4) How Can Statistics Be Misleading. 5) How To Avoid & Identify The Misuse Of Statistics? If all this is true, what is the problem with statistics? What Is A Misleading Statistic?

Statistics 363

Statistics Advertising Visualization Data mining 363

Occam's Razor

SEPTEMBER 21, 2015

First, someone worked really hard on this and created a really nice model for a smarter decision to be made for 2014. Second, between 2012 and 2013. You are comparing 2012 and 2013, add a row of data at the top that shows your computation of the size of the opportunity for 2014. You are a Ninja, it will likely take you less.

Visualization 160

Visualization Key Performance Indicator Slice and Dice Strategy 160

Sisense

DECEMBER 11, 2019

And he demonstrated how the Periscope Data platform overcomes the challenges of huge data volumes that can’t be easily modeled by traditional BI. From a startup in 2012, it is now valued at $3.2 Citing Tinder as a major example, Kyle explained how it constantly uses data to enhance users’ interactions and calibrate the user experience.

Data Lake 80

Data Lake Big Data Sales Data-driven 80

Occam's Razor

JANUARY 10, 2011

10% of your time should be spent in implementing tools, not 15 months with an eye towards analysis in the middle of 2012. And possess at least some knowledge of the fundamentals of statistics. You can win with Omniture or WebTrends or IBM or Google. Stop switching tools! After five years of success worry about tools.

Analytics 120

Analytics Measurement Data-driven Optimization 120

O'Reilly on Data

MAY 21, 2019

Companies successfully adopt machine learning either by building on existing data products and services, or by modernizing existing models and algorithms. For example, in a July 2018 survey that drew more than 11,000 respondents, we found strong engagement among companies: 51% stated they already had machine learning models in production.

Machine Learning 238

Machine Learning Technology Deep Learning Data Science 238

Domino Data Lab

SEPTEMBER 25, 2019

A big part of statistics, particularly for financial and econometric data, is analyzing time series, data that are autocorrelated over time. One of the most common ways of fitting time series models is to use either autoregressive (AR), moving average (MA) or both (ARMA). Chapter Introduction: Time Series and Autocorrelation.

Forecasting 87

Forecasting Modeling Statistics Optimization 87

CIO Business Intelligence

FEBRUARY 3, 2023

Power Advisor tracks statistics about performance to locate bottlenecks and other issues. AI Builder can also create and deploy models that make predictions and even decisions, taking more work off of users’ shoulders. Rocketbot Orquestador will manage them, running them as needed while compiling statistics.

Data-driven 145

Data-driven Interactive Enterprise Statistics 145

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. This question is statistical or methodological in nature. Know what matters.

Experimentation 61

Experimentation Testing Statistics Metrics 61

datapine

MAY 17, 2021

Businesses can benefit from improved data driven decision making as well as enhanced business processes and models and share insights across departments more fluently while propelling intelligent business strategies. What is a discovery model, and how do you use it in a real-world business context? 5) Develop a data discovery model.

Visualization 161

Visualization Data-driven Business Intelligence Metrics 161

Edwin Chen

JANUARY 7, 2013

debates — Romney’s Binder (@RomneysBinder) October 17, 2012. We’ll describe a novel real-time human computation engine we built that allows us to find search queries as soon as they’re trending, send these queries to real humans to be judged, and finally incorporate these human annotations into our backend models.

Statistics 73

Statistics Machine Learning Advertising Metadata 73