Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. Taking a pulse.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

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. The biggest time sink is often around data collection, labeling and cleaning.

Management 110

Management Machine Learning Experimentation Metrics 110

AWS Big Data

JULY 3, 2023

Buy Experimentation findings The following table shows Sharpe Ratios for various holding periods and two different trade entry points: announcement and effective dates. By using a scalable Amazon EMR on Amazon EKS stack, researchers can easily handle the entire investment research lifecycle, from data collection to backtesting.

Snapshot 82

Snapshot Data Lake Testing Strategy 82

Cloudera

JUNE 6, 2019

When a mix of batch, interactive, and data serving workloads are added to the mix, the problem becomes nearly intractable. Lakshmi Randall is Director of Product Marketing at Cloudera, the enterprise data cloud company. Conclusion and future work. Learn more about Cloudera’s platform here.

Metadata 85

Metadata Data Lake Optimization Strategy 85

Occam's Razor

DECEMBER 12, 2016

You got me, I am ignoring all the data layer and custom stuff! But, at the end of the day presence of a Tag Manager communicates to me that the company is serious about data collection and data quality. with responsibility for every facet of the entire company's data collection, data reporting and data analysis.

Analytics 141

Analytics Data-driven Strategy Consulting 141

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. Keep in mind that data science is fundamentally interdisciplinary. Let’s look through some antidotes.

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Occam's Razor

SEPTEMBER 15, 2014

In this post we will look mobile sites first, both data collection and analysis, and then mobile applications. Media-Mix Modeling/Experimentation. Upsight (nee Kontagent) provides mobile app analytics, with a pinch of advanced segmentation (including sweet cohort analysis ) and big data mining thrown in for good measure.

Metrics 141

Metrics Reporting Analytics Marketing 141

Occam's Razor

MAY 27, 2015

(even if you've never visited the site) has access to tons of intent signals from you right now, tons of third-party cookies that litter your browser right now, and immense Big Data and algorithms. It is being hyper-conservative when it comes to creativity and experimentation because of quant-issues. Does Yahoo!

Marketing Analytics 153

Marketing Analytics Marketing Analytics Advertising 153

Occam's Razor

AUGUST 13, 2012

The central team is responsible for analytics frameworks, centralized contracts (tools, consultants), for aggregated company level analysis, complex project execution (experimentation, media mix models etc) and for setting standards. See point #4 here: A Big Data Imperative: Driving Big Action.

Modeling 124

Modeling Metrics Data Quality Data-driven 124

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

Implicitly, there was a prior belief about some interesting causal mechanism or an underlying hypothesis motivating the collection of the data. As computing and storage have made data collection cheaper and easier, we now gather data without this underlying motivation.

Experimentation 61

Experimentation Testing Statistics Metrics 61

Occam's Razor

JANUARY 7, 2014

First… it is important to realize that big data's big imperative is driving big action. Second… well there is no second, it is all about the big action and getting a big impact on your bottom-line from your big investment in analytics processes, consulting, people and tools.

Data-driven 140

Data-driven Consulting Reporting Marketing 140

Occam's Razor

APRIL 29, 2013

It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Small data. Here's why… Real-time data is very expensive. It is expensive from a systems/platforms/data processing/data reporting perspective. PPS: I've mentioned one exception in the past.

Marketing 166

Marketing Metrics Advertising Measurement 166

Occam's Razor

OCTOBER 12, 2017

The tiny downside of this is that our parents likely never had to invest as much in constant education, experimentation and self-driven investment in core skills. Years and years of practice with R or "Big Data." There is never a boring moment, there is never time when you can’t do something faster or smarter.

Marketing 136

Marketing Analytics Machine Learning Strategy 136