Domino Data Lab

AUGUST 8, 2019

Having participated in several Foo Camps—and even co-chaired the Ed Foo series in 2016-17— most definitely, a Foo will turn your head around. He’s been out of Wolfram for a while and writing exquisite science books including Elements: A Visual Explanation of Every Known Atom in the Universe and Molecules: The Architecture of Everything.

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. They may contain parameters in the statistical sense, but often they simply contain strategically placed 0's and 1's indicating which bits of $alpha_t$ are relevant for a particular computation. by STEVEN L.

Forecasting 64

Forecasting Statistics Modeling Software 64

Occam's Razor

SEPTEMBER 13, 2016

If you are open to being challenged… then here are the short-stories inside this post… The World Needs Reporting Squirrels. The World Needs Reporting Squirrels. If you are curious, here is a April 2011 post: The Difference Between Web Reporting And Web Analysis. Visual perception of information. Bottom-line.

Modeling 127

Modeling Reporting Strategy Consulting 127

Smarten

SEPTEMBER 6, 2019

The traditional definition of data preparation describes an iterative process, typically executed by IT staff or analysts to extract and transform raw data so that the data can be used for discovery, analytics and reporting. Users can control the data elements, the volume and the timing of the analysis and reporting.

Analytics 51

Analytics IT Business Intelligence Testing 51

The Unofficial Google Data Science Blog

MARCH 13, 2017

Typically, causal inference in data science is framed in probabilistic terms, where there is statistical uncertainty in the outcomes as well as model uncertainty about the true causal mechanism connecting inputs and outputs. A note on visualization The most convenient way to inspect our feature importances (attributions) is to visualize them.

IT 68

IT Visualization Modeling Uncertainty 68

datapine

AUGUST 29, 2022

It was lately revised and updated in January 2016. He recognizes big online companies like Google or Facebook as the originators of the top big data tools and technologies, as well as data-driven management reporting and best practices. 8) Data Smart: Using Data Science to Transform Information into Insight, by John W.

Big Data 263

Big Data Data Analytics Analytics Data mining 263

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

Depict Data Studio

APRIL 10, 2023

Visualizing data in charts, graphs, dashboards, and infographics is one of the most powerful strategies for getting your numbers out of your spreadsheets and into real-world conversations. But it can be overwhelming to get started with data visualization. If so, this step-by-step data visualization guide is for you!

Visualization 105

Visualization Dashboards Testing Reporting 105

Occam's Razor

JULY 13, 2017

In service of that belief, there are few things that bring me as much joy as visualizing data (smart segmentation comes close). While I am partial to the simplest of visualizations in a business data context, I love a simple Bar Chart just as much as a Chord or Fisher-Yates Shuffle. 6: Turbocharging Data Visuals with Storytelling.

Visualization 124

Visualization Strategy Interactive Statistics 124