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.

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

KDnuggets

2019 Dec Opinions Advice Data Science Experimentation UltralearnIn this fourth and final part of the ultralearning data science series, it's time to take the final steps toward developing a deep understanding of the fundamentals and learning how to experiment -- the two aspects that are the ultimate keys to ultralearning.

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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.

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. 2019 Dec Opinions Advice Data Science Experimentation Optimization Ultralearn

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.

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?

Corinium Meets: Quantum Metric Head of Behavioural Research Marina Shapira

Corinium

Ahead of her presentation at CDAO UK, we spoke with Quantum Metric’s Marina Shapira about predictive analytics, why companies should embrace a culture of experimentation how and CAOs and CXOs can work together effectively. What is behavioural research? And what role should it play in an organization's data and analytics strategy? Behavioural research seeks to understand what motivates people, how they perceive the world, make decisions and form habit. Customer Experience and Management

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

billion in 2021, as per different industries’ market share. Looking ahead at 2020, enterprises become laser-focused on AI value and leap out of experimentation mode, and ground themselves in reality to accelerate adoption.Here are the top 10 trends in AI that are likely to continue or emerge in the year 2020: Read More on Datafloq. Global spending on cognitive and AI systems will reach $57.6

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.

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.

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

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

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

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. Talk about characteristics of blockchain is diminishing and discussion around use cases are soaring. Organizations are investing in blockchain development. What’s coming now is the new face of blockchain, endearingly called blockchain 2.0.

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].

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.

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.

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 ).

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. Experimentation at the beginning of your journey is essential to make sure you understand where you are starting. That’s a pretty hardcore attitude to experimentation but taking it seriously is definitely worth your while.

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.,

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. Test early and often: expect continuous improvement, encourage and reward a culture of experimentation, learn from failure, “Test, or get fired!” Written by Dr. Kirk Borne.

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. When I was in middle school (quite a few years ago), I started to realize that I was pretty good at math. I had done okay before, but the problems and concepts were becoming more difficult.

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.

How to get powerful and actionable insights from any and all of your data, without delay

Cloudera

A large oil and gas company was suffering over not being able to offer users an easy and fast way to access the data needed to fuel their experimentation. Today’s data tool challenges.

Data for Enterprise AI: at the very forefront of innovation

Cloudera

Acceptance that it will be an experiment — ML really requires a lot of experimentation, and often times you don’t know what’s going to be successful. 2020 may well go down as the year where what seems impossible today, did become possible tomorrow.

Apply Modern CRM Dashboards & Reports Into Your Business – Examples & Templates

datapine

When we say “optimal design,” we don’t mean cramming piles of information into one space or being overly experimental with colors. Niche or industry aside, it’s likely that your customers are the beating heart of your entire operation. To ensure that your customer-facing communications and efforts are constantly improving and evolving, investing in customer relationship management (CRM) is vital.

Announcing Domino 3.3: Datasets and Experiment Manager

Domino Data Lab

Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale. Data science is different from other workstreams like software development in that it involves open-ended exploration and experimentation to find optimal solutions.

Who Does the Machine Learning and Data Science Work?

Business Over Broadway

Experimentation and iteration to improve existing ML models (39%). A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions.

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.

Is Artificial Intelligence Revolutionizing Environmental Health?

Simply Statistics

Concurrently, while population data are booming, toxicology is creating a variety of experimental models to advance our understanding of how chemicals and environmental exposures may pose risks to human health. NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D.

Interview with: Sankar Narayanan, Chief Practice Officer at Fractal Analytics

Corinium

It is also important to have a strong test and learn culture to encourage rapid experimentation. Will you please describe your role at Fractal Analytics? I am the Chief Practice Officer for Insurance, Healthcare, and Hi-Tech verticals at Fractal. The Insurance practice is currently engaged with several top 10 P&C insurers in the US, across the Insurance value chain through AI, Engineering, Design & Behavioural Sciences programs.

Hey Siri, What’s My Forecasted EBITDA Look Like?

Jedox

Experimental” Technology. Is AI truly experimental technology? Even though we have so much advanced technology surrounding us, we still cannot just ask, “ Hey Siri, what’s my forecasted EBITDA look like ?” There are many reasons why such technology isn’t available yet—insufficient data, unstructured data and some human knowledge that is not yet transferable to machine.

Ask Why! Finding motives, causes, and purpose in data science

Data Science and Beyond

Causality and experimentation. Some people equate predictive modelling with data science, thinking that mastering various machine learning techniques is the key that unlocks the mysteries of the field. However, there is much more to data science than the What and How of predictive modelling. I recently gave a talk where I argued the importance of asking Why , touching on three different topics: stakeholder motives, cause-and-effect relationships, and finding a sense of purpose.

AI in Analytics: The NLQ Use Case

Sisense

When the app is first opened, the user may be searching for a specific song that was heard while passing by the neighborhood cafe, or the user may want to be surprised with, let’s say, a song from the new experimental album by a Yemen Reggae folk artist. There are many activities going on with AI today, from experimental to actual use cases.

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?

7 Requirements for Digital Transformation

Cloudera

A disruptive mindset creates an environment that embraces constant experimentation and change. Digital transformation is not just about technological transformation of the organization, it’s about transforming the culture of an organization.

Keynote Takeaways From Gartner Data & Analytics Summit

Sisense

Gartner chose to group the rest of the keynote into three main messages according to the following categories: Here are some of the highlights as presented for each of them: Data Driven – “Adopt an Experimental Mindset”. I was very happy to see that the first key message – “Data Driven” – was subtitled with “Adopt an Experimental Mindset”. At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

Why models fail to deliver value and what you can do about it.

Domino Data Lab

This means many projects get stuck in endless research and experimentation. Building models requires a lot of time and effort.

Managing Risk in Data Projects

Dataiku

It’s probably safe to say that for at least some of those explorers, the prospect of risk when it comes to data and AI projects is paralyzing, causing them to stay in a phase of experimentation. In 2018, O’Reilly conducted a survey regarding the stage of machine learning adoption in organizations, and among the more than 11,000 respondents, almost half were still in the exploration phase.

The most practical causal inference book I’ve read (is still a draft)

Data Science and Beyond

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. I’ve been interested in the area of causal inference in the past few years. In my opinion it’s more exciting and relevant to everyday life than more hyped data science areas like deep learning. However, I’ve found it hard to apply what I’ve learned about causal inference to my work.

Evaluating Ray: Distributed Python for Massive Scalability

Domino Data Lab

for model serving (experimental), are implemented with Ray internally for its scalable, distributed computing and state management benefits, while providing a domain-specific API for the purposes they serve. Ray recently introduced experimental implementations of these APIs that allow your applications to scale to a cluster. Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters.