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


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). It is important to realize that experimentation and testing might sound big and complex but it is not.

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


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.

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.

Corinium Meets: Quantum Metric Head of Behavioural Research Marina Shapira


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.

Top 10 AI Trends To Watch Out in 2020


Global spending on cognitive and AI systems will reach $57.6

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 testing

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. 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

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


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

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.

Establishing More Trust In 2020 With Blockchain


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

Quantum Computing and Blockchain: Facts and Myths


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.

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. These steps also reflect the experimental nature of ML product management.

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.

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. Written by Dr. Kirk Borne.

12 Marketing Reports Examples You Can Use For Annual, Monthly, Weekly And Daily Reporting Practice


A daily marketing report will also allow you for faster experimentation: running small operations to answer small questions. Let’s face it: every serious business that wants to generate leads and revenue needs to have a marketing strategy that will help them in their quest for profit.

Announcing Domino 3.3: Datasets and Experiment Manager

Domino Data Lab

Data science is different from other workstreams like software development in that it involves open-ended exploration and experimentation to find optimal solutions. Our mission at Domino is to enable organizations to put models at the heart of their business.

Drug Discovery Needs AI To Discover More Treatments

Smart Data Collective

The greatest advantage of AI is that it can digest vast amounts of medical knowledge — from thousands of published reports and scientific papers, say — and devise novel predictions and formulations that would take human researchers years of inefficient experimentation to find.

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


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.

Methods of Study Design – Experiments

Data Science 101

Some pitfalls of this type of experimentation include: Suppose an experiment is performed to observe the relationship between the snack habit of a person while watching TV. Bias can cause a huge error in experimentation results so we need to avoid them.

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.

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.

Managing Risk in Data Projects


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.

Keynote Takeaways From Gartner Data & Analytics Summit


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”.

AI in Analytics: The NLQ Use Case


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.

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


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.

Expert Speak | Transforming Digital Enterprises


With increasing mainstream acceptance and adoption of AI-led technologies, C-suite executives today have gone beyond committing ‘digital experimentation’ to large scale Digital Transformation, be it pan-enterprise or functional.

Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline?

Sanjeev Mohan

GCP has gained acceptance for development and experimentation and more enterprise customers are putting it into production. Is Google Cloud Platform Ready to Run Your Data Analytics Pipeline? This is the focus of my latest research which published in Jan 2019.

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.

How Do Super Rookies Start Learning Data Analysis?


In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. For super rookies, the first task is to understand what data analysis is.

10 Books that Data Analyst Should Read


In the past few years, the term “data science” has been widely used, and people seem to see it in every field. Big Data”, “Business Intelligence”, “ Data Analysis ” and “ Artificial Intelligence ” came into being. For a while, everyone seems to have begun to learn data analysis.

Open Data Science and Machine Learning for Business with Cloudera Data Science Workbench on HDP


Data scientists require on-demand access to data, powerful processing infrastructure, and multiple tools and libraries for development and experimentation.

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.

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.

Q&A Tuesday: Jonathan Reichental on Digital Transformation and 21st-Century Excellence

Jet Global

Organizations need to become really comfortable with experimentation. The innovation process, where experimentation might live in an organization, has grown in popularity in the last few years. Jonathan Reichental, Ph.D.,

Augmented Data Discovery Provides Users with Crucial Answers


Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation. Advanced Data Discovery Can and Should Be Available to All!

Further Exploration #13: 3D Treemap Spheres and Cylinders

The Data Visualisation Catalogue

This experimental visualisation uses a polar layout and extends into into 3D with a cylindrical form. Another experimental variation of a Treemap was carried out by Schulz and co, by taking the 3D Polar Treemap a step further by combining it with elements of the Steptree.

Sisense for Cloud Data Teams: a Step Forward for Builders Everywhere


There’s no pressure to produce perfect results, we’ve built an atmosphere that encourages continual experimentation and rewards those who help others.

Predictive Analytics Can Guide the Organization to Success


Plug n’ Play Predictive Analysis for Accurate Forecasting! There are numerous considerations when a business looks at upgrading or acquiring an analytical solution. One very important capability is Put n’ Play predictive analysis.

Comparing the Functionality of Open Source Natural Language Processing Libraries

Domino Data Lab

If you are in research, excellent libraries like Allen NLP and NLP Architect are designed to make experimentation easier, although at the expense of feature completeness, speed and robustness.

Glossary of Digital Terminology for Career Relevance

Rocket-Powered Data Science

Computer Vision: Data Mining: Data Science: Application of scientific method to discovery from data (including Statistics, Machine Learning, data visualization, exploratory data analysis, experimentation, and more). Career Relevance. Definitions of terminology frequently seen and used in discussions of emerging digital technologies. NOTE: This page is a WIP = Work In Progress.).