Experimentation in Data Science

TDAN

Modern business is all about data, and when it comes to increasing your advantage over competitors, there is nothing like experimentation. Experiments in data science are the future of big data. Innovations can now win the future. Already, data scientists are making big leaps forward.

Building a Culture of Experimentation

Dataiku

As more and more organizations continue to invest in their data and analytics practices, the question we repeatedly hear from analytics leaders is, “How can I streamline and scale my teams’ efforts in order to drive even more impact?”. Dataiku Company Scaling AI Featured

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How To “Ultralearn” Data Science: deep understanding and experimentation, Part 4

KDnuggets

2019 Dec Opinions Advice Data Science Experimentation Ultralearn

Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). I covered Experimentation and Testing during my recent speech at the Emetrics Summit and here is the text from that slide: Experiment or Go Home: Customers yell our problems (when they call or see us), they b h, they rarely provide solutions. It is important to realize that experimentation and testing might sound big and complex but it is not.

Best Practices for Creating Long-Lasting and Continuous Discovery Habits

Speaker: Teresa Torres, Internationally Acclaimed Author, Speaker, and Coach at ProductTalk.org

Join internationally acclaimed author, speaker, and coach Teresa Torres as she explores the what, how, and why behind creating continuous discovery habits that give your team a clear benchmark to aspire to.

How To “Ultralearn” Data Science: summary, for those in a hurry

KDnuggets

For those of you in a hurry and interested in ultralearning (which should be all of you), this recap reviews the approach and summarizes its key elements -- focus, optimization, and deep understanding with experimentation -- geared toward learning Data Science.

Six Nudges: Creating A Sense Of Urgency For Higher Conversion Rates!

Occam's Razor

Marketing Tips Usability Voice of Customer conversion rate experimentation and testing user experienceBy every indicator available, ecommerce is continuing to grow at an insane speed. Although it may seem impossible to imagine with ecommerce already totaling up to 5% of overall commerce, there’s astronomical growth still to come. Still, I’m heartbroken that some the simplest elements of ecommerce stink so much. It is 2018—why are there still light gray below-the-fold add to cart buttons?

The Lean Analytics Cycle: Metrics > Hypothesis > Experiment > Act

Occam's Razor

Advanced Analytics Big Data Digital Analytics Web Analytics Web Insights Web Metrics actionable analytics business optimization experimentation and testing key performance indicators This should not be news to you. To win in business you need to follow this process: Metrics > Hypothesis > Experiment > Act. Online, offline or nonline. Yet this structure rarely exists in companies.

Measuring Incrementality: Controlled Experiments to the Rescue!

Occam's Razor

Having read this post what might be the biggest downside to experimentation? Advanced Analytics Marketing Tips Search Engine Marketing acquisition portfolio optimization actionable web analytics excellent analytics tips experimentation and testingWith a plethora of digital media channels at our disposal and new ones on the way every day(!), how do you prioritize your efforts? How do you figure out which channels to invest in more and which to kill?

Top 10 AI Trends To Watch Out in 2020

DataFloq

Global spending on cognitive and AI systems will reach $57.6

The Most Complete Guide to PyTorch for Data Scientists

MLWhiz

I remember picking PyTorch up only after some extensive experimentation a couple of years back. PyTorch has sort of became one of the de facto standards for creating Neural Networks now, and I love its interface. Yet, it is somehow a little difficult for beginners to get a hold of.

Driving Discovery and Experimentation in your Organization

Speaker: Teresa Torres, Product Discovery Coach, Product Talk, David Bland, Founder and CEO, Precoil, and Hope Gurion, Product Coach and Advisor, Fearless Product LLC

If you want to build what matters, you can't move forward blindly. But to make progress, you can't let things slow to a crawl while you focus resources on gathering data. This is where continuous discovery and experimentation come in. Join Teresa Torres (Product Discovery Coach, Product Talk), David Bland (Founder, Precoil), and Hope Gurion (Product Coach and Advisor, Fearless Product) in a panel discussion as they cover how - and why - to build a culture of discovery and experimentation in your organization.

Experiment or Die. Five Reasons And Awesome Testing Ideas.

Occam's Razor

There is a tendency to think experimentation and testing is optional. So as my tiny gift for you here are five experimentation and testing ideas for you. I hope that you'll find both compelling reasons for starting experimentation and I have managed to stretch your mind beyond "honey let's start testing shopping cart buttons" There is so much you can do. This recession season buy your CEO the gift that keeps giving, a experimentation and testing tool.

Eight Silly Data Things Marketing People Believe That Get Them Fired.

Occam's Razor

competitive intelligence Digital Analytics Digital Marketing Marketing Tips Search Engine Marketing Social Media Web Metrics actionable web analytics digital marketing experimentation and testing marketing metrics It turns out that Marketers, especially Digital Marketers, make really silly mistakes when it comes to data. Big data. Small data. Any data. In the last couple months I've spent a lot of time with senior level marketers on three different continents.

IBM expands data and AI excellence with data cataloging technology in Cloud Pak for Data

IBM Big Data Hub

Describing the breadth of IBM's leadership and experimentation in the data and AI space is no small task. IBM has been working with more than 200 production blockchain networks , thousands of regulatory documents and datasets across industries, and hundreds of AI research projects

Industrializing your AI and data science models with IBM Cloud Private for Data

IBM Big Data Hub

The next chapter is all about moving from experimentation to true transformation. Companies are entering “chapter two” of their digital transformation. It’s about gaining speed and scale. We are helping businesses activate data as a strategic asset, with desire to maximize the impact of AI as core to the business strategy

SpaceX Starship prototype rocket explodes on landing after test launch

DataFloq

(Reuters) - A SpaceX Starship prototype rocket exploded on landing after an otherwise successful high-altitude experimental launch from Boca Chica, Texas, on Tuesday, in a repeat of an accident that destroyed a previous test rocket.

How to apply machine learning and deep learning methods to audio analysis

KDnuggets

Find out how data scientists and AI practitioners can use a machine learning experimentation platform like Comet.ml to apply machine learning and deep learning to methods in the domain of audio analysis. 2019 Nov News Audio Machine Learning Speech Recognition

JD.com becomes first online platform to accept China's digital currency

DataFloq

JD Digits, the company's fintech arm, will accept digital yuan as payment for some products on its online mall, as part of an experimental giveaway of digital yuan to citizens of Suzhou, near Shanghai, according to a post on the company's official WeChat account.

Twitter a goldmine for tracking consumer mood on prices, Bank of Italy finds

DataFloq

ROME (Reuters) - The Bank of Italy said on Monday a set of experimental indicators it created from the content of millions of tweets accurately tracked consumer mood on price, offering scope for a powerful new monetary policy tool. By Stefano Bernabei.

Top Five Methods to Identify Outliers in Data

DataFloq

An outlier may be due to variability in the measurement or it may indicate the experimental error; the latter are sometimes excluded from the data set. Outliers – the twisted tale of data!But But what is an outlier?

Four Popular AI Implementations to Revolutionise Healthcare

DataFloq

It involves effective administration of the processes, be it finding experimental subjects, monitoring test sites, or identifying potential health risks of processes and.

Enterprise Data Science Workflows with AMPs and Streamlit

Cloudera

Here in the virtual Fast Forward Lab at Cloudera , we do a lot of experimentation to support our applied machine learning research, and Cloudera Machine Learning product development. Only through hands-on experimentation can we discern truly useful new algorithmic capabilities from hype.

Don’t Just Sit There, Experiment!

Decision Management Solutions

Randomly select groups of customers and use the experimental approach on them, to prevent bias, and ensure a clean test Keep information on both groups – what you would normally do and what you experimented on – so you can compare the approaches later.

Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

Instead, we focus on the case where an experimenter has decided to run a full traffic ramp-up experiment and wants to use the data from all of the epochs in the analysis. In order to mitigate risk, the experimenter decides to “play it safe” and not assign too many users to the new arm.

Establishing More Trust In 2020 With Blockchain

DataFloq

In the last decade, blockchain has garnered the attention of technologists, entrepreneurs and industry stalwarts, leading to experimentation and exploration. 2020 is here.

The 12 Rules of DataOps to Avert a DataOops

Kirk Borne

This can be overcome with small victories (MVP minimum viable products, or MLP minimum lovable products) and with instilling ( i.e., encouraging and rewarding) a culture of experimentation across the organization. DataOps accepts a fail-fast, learn-fast culture of experimentation.

Quantum Computing and Blockchain: Facts and Myths

DataFloq

Google states that its experiment is the first experimental challenge against the extended Church-Turing thesis — also known as computability thesis — which claims that traditional computers can effectively carry out any “reasonable” model of computation. The biggest danger to Blockchain networks from quantum computing is its ability to break traditional encryption [3].

Top 10 Data Innovation Trends During 2020

Rocket-Powered Data Science

MLOps takes the modeling, algorithms, and data wrangling out of the experimental “one off” phase and moves the best models into deployment and sustained operational phase. The year 2020 was remarkably different in many ways from previous years. In at least one way, it was not different, and that was in the continued development of innovations that are inspired by data.

STELLAR Analytics for the Win – an A for Enterprise Analytics Mastery

Kirk Borne

Adopt a culture of experimentation and a “data for all” (data literacy) mindset across the whole organization. T eam Analytics: a culture of experimentation that celebrates and validates the power in diversification, collaboration, data-sharing, data reuse, and data democratization.

Machine Learning Product Management: Lessons Learned

Domino Data Lab

Pete indicates, in both his November 2018 and Strata London talks, that ML requires a more experimental approach than traditional software engineering. It is more experimental because it is “an approach that involves learning from data instead of programmatically following a set of human rules.” Because the nature and approach of ML projects is more experimental, industry people and their companies won’t know what will happen until they try it (i.e.,

Bringing ML to Agriculture: Transforming a Millennia-old Industry

Domino Data Lab

Experimentation and collaboration are built into the core of the platform. This ability enhances the efficiency of operational management and optimizes the cost of experimentation. Guest post by Jeff Melching, Distinguished Engineer / Chief Architect Data & Analytics.

10 Steps to Achieve Enterprise Machine Learning Success

Cloudera

Unfortunately, most organizations run into trouble when it comes to bridging the gap that exists between experimentation and full-scale ML production. Proper science takes experimentation and observation, as well as a willingness to accept the failures alongside the successes.

How We Teach The Leaders of Tomorrow To Be Curious, Ask Questions and Not Be Afraid To Fail Fast To Learn Fast

Rocket-Powered Data Science

5) Is your organizational culture ready for this (for data-informed decisions; an experimentation mindset; continuous learning; fail fast to learn fast; with principled AI and data governance)? (6) I recently enjoyed recording a podcast with Joe DosSantos (Chief Data Officer at Qlik ).

Do You Need a DataOps Dojo?

DataKitchen

A COE typically has a full-time staff that focuses on delivering value for customers in an experimentation-driven, iterative, result-oriented, customer-focused way.

Omdia Selects DataRobot as Recommended MLOps Vendor

DataRobot

They went on to say that investing in MLOps directly answers one of the biggest questions facing AI practitioners in the enterprise: how to move from experimentation to transformation.

How Analytics by Design Tackles The Yin and Yang of Metrics and Data

Kirk Borne

By applying an agile methodology, we are able to adopt a culture of experimentation that permits us to fail fast in order to learn fast and that delivers both the minimum viable product and the minimum lovable product. Therefore, in sequential order, the four principles of Analytics by Design are: Adopt a culture of experimentation — “ test or get fired ” is the mantra within one successful analytics-driven organization. Written by Dr. Kirk Borne.

Bringing Augmented Intelligence to All

DataRobot

Last fall, I penned a blog post around our Series F funding, focused on the fact that the era of experimental AI is over.

Misadventures in experiments for growth

The Unofficial Google Data Science Blog

by MICHAEL FORTE Large-scale live experimentation is a big part of online product development. This means a small and growing product has to use experimentation differently and very carefully. This blog post is about experimentation in this regime. Now how is it possible for these fledgling products exist, do something, have enough users that one could contemplate experimentation, and yet still not have market fit?

How Enterprise MLOps Works Throughout the Data Science Lifecycle

Domino Data Lab

The Workbench is Domino’s notebook-based environment where data scientists can do their R&D and experimentation.

Welcome to the Era of the Augmented Marketer

Sisense

We know in marketing that one of the most powerful ideas is experimentation,” Scott told Sisense. What has held that back is that the gap between idea and implementation has been a real bottleneck for how much experimentation can happen.”.

The DataOps Vendor Landscape, 2021

DataKitchen

Comet.ML — Allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility. Download the 2021 DataOps Vendor Landscape here.

The Role of Theory in Data Analysis

Simply Statistics

Many data analysts are not involved in the data collection process or the experimental design and so it is important to inquire about he process by which the data came to them. But when she visited the lab one day to see how the experiments were done, she discovered that the experimental units were all processed in one batch and the control units were all processed in a different batch at a different time, thereby confounding any treatment effect with the batch.