Occam's Razor

FEBRUARY 7, 2022

Since you're reading a blog on advanced analytics, I'm going to assume that you have been exposed to the magical and amazing awesomeness of experimentation and testing. And yet, chances are you really don’t know anyone directly who uses experimentation as a part of their regular business practice. Wah wah wah waaah.

Experimentation 101

Experimentation Testing Optimization Marketing 101

CIO Business Intelligence

JUNE 14, 2023

Candidates are required to complete a minimum of 12 credits, including four required courses: Algorithms for Data Science, Probability and Statistics for Data Science, Machine Learning for Data Science, and Exploratory Data Analysis and Visualization.

Big Data 124

Big Data Data Analytics Analytics Predictive Modeling 124

Data Science 101

JANUARY 15, 2020

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. Statistics Essential for Dummies by D. REFERENCES. McCabe & B.

Experimentation 96

Experimentation Statistics Measurement Testing 96

Occam's Razor

MAY 22, 2006

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

Experimentation 103

Experimentation Testing Optimization Measurement 103

CIO Business Intelligence

JANUARY 2, 2024

He plans to scale his company’s experimental generative AI initiatives “and evolve into an AI-native enterprise” in 2024. But at the end of the day, it boils down to statistics. Statistics can be very misleading. That’s the case for Yi Zhou, CTO and CIO with Adaptive Biotechnologies.

IT 144

IT Cost-Benefit Statistics Consulting 144

Business Over Broadway

AUGUST 4, 2020

Experimentation and iteration to improve existing ML models (39%). In prior research, I found that data professionals who self-identified as Researchers have a strong math/statistics/research skill set. The entire list of activities (shown in Figure 1) were: Analyze and understand data to influence product or business decisions (63%).

Machine Learning 145

Machine Learning Data Science Statistics Experimentation 145

O'Reilly on Data

MARCH 31, 2020

All you need to know for now is that machine learning uses statistical techniques to give computer systems the ability to “learn” by being trained on existing data. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

Management 332

Management Machine Learning Experimentation Metrics 332

Rocket-Powered Data Science

JULY 7, 2019

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). They cannot process language inputs generally. Examples: (1) Automated manufacturing assembly line. (2) 4) Prosthetics.

Internet of Things 157

Internet of Things Machine Learning Manufacturing IoT 157

FineReport

DECEMBER 11, 2019

. – Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions. The big news is that we no longer need to be proficient in math or statistics, or even rely on expensive modeling software to analyze customers. By Michael Milton. – Data Divination: Big Data Strategies.

Big Data 98

Big Data Statistics Experimentation Data Strategy 98

The Unofficial Google Data Science Blog

JANUARY 14, 2016

Unlike experimentation in some other areas, LSOS experiments present a surprising challenge to statisticians — even though we operate in the realm of “big data”, the statistical uncertainty in our experiments can be substantial. We must therefore maintain statistical rigor in quantifying experimental uncertainty.

Experimentation 41

Experimentation Statistics Metrics Measurement 41

DataRobot Blog

JANUARY 17, 2023

Not actually being a machine learning problem: Value-at-Risk modeling is the classic example here—VaR isn’t a prediction of anything, it’s a statistical summation of simulation results. As discussed, we massively accelerate that process of experimentation.

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

Ontotext

FEBRUARY 14, 2024

As a statistical model, LLM inherently is random. Experimentation is important, but be explicit when you do. So, we’ve learned our lesson. AI will solve it We’ve seen a massive increase in interest in LLMs and ‘AI’ But LLM without guide rails could lead to unbridled hallucinations. Start with “why?”

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

Octopai

OCTOBER 26, 2022

When DataOps principles are implemented within an organization, you see an increase in collaboration, experimentation, deployment speed and data quality. Continuous pipeline monitoring with SPC (statistical process control). What DataOps best practices put you on track to achieving this ideal? Let’s take a look. Results (i.e.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

CIO Business Intelligence

MARCH 21, 2022

According to William Chen, Data Science Manager at Quora , the top five skills for data scientists include a mix of hard and soft skills: Programming: The “most fundamental of a data scientist’s skill set,” programming improves your statistics skills, helps you “analyze large datasets,” and gives you the ability to create your own tools, Chen says.

Unstructured Data 133

Unstructured Data Data Analytics Analytics Structured Data 133

The Unofficial Google Data Science Blog

DECEMBER 14, 2021

Some of that uncertainty is the result of statistical inference, i.e., using a finite sample of observations for estimation. But there are other kinds of uncertainty, at least as important, that are not statistical in nature. Among these, only statistical uncertainty has formal recognition.

Uncertainty 56

Uncertainty Statistics Measurement Cost-Benefit 56

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Most data scientists are strong in one or two of these areas, but not all three.

ROI 52

ROI Machine Learning Experimentation Data Science 52

DataRobot Blog

JULY 28, 2022

Initially, the customer tried modeling using statistical methods to create typical features, such as moving averages, but the model metrics (R-square) was only 0.5 The first baseline model we created used spectrograms of speech waveform data, statistical features, and spectrogram images. This approach got us to an R-squared of 0.7,

Data Science 72

Data Science Machine Learning Statistics Modeling 72

CIO Business Intelligence

JUNE 28, 2023

As Belcorp considered the difficulties it faced, the R&D division noted it could significantly expedite time-to-market and increase productivity in its product development process if it could shorten the timeframes of the experimental and testing phases in the R&D labs.

Digital Transformation 85

Digital Transformation Cost-Benefit Informatics Data mining 85

Octopai

MAY 2, 2022

Common elements of DataOps strategies include: Collaboration between data managers, developers and consumers A development environment conducive to experimentation Rapid deployment and iteration Automated testing Very low error rates. Issue detected?

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

Sisense

JANUARY 21, 2020

This is an example of Simpon’s paradox , a statistical phenomenon in which a trend that is present when data is put into groups reverses or disappears when the data is combined. It’s time to introduce a new statistical term. So how do we get totally different results when breaking the data down by gender? See Kievit, Rogier, et al. ).

Testing 104

Testing Risk Data-driven Statistics 104

CIO Business Intelligence

DECEMBER 6, 2023

And in addition to having generative AI cite the sources of key information, consider ways to highlight elements that are important to double check, like dates, statistics, policies, or precedents that are being relied on. Human reviewers should be trained to critically assess AI output, not just accept it at face value.”

IT 129

IT Risk Management Interactive Risk 129

Domino Data Lab

JANUARY 24, 2019

This group of solutions targets code-first data scientists who use statistical programming languages and spend their days in computational notebooks (e.g., While these solutions offer great breadth of functionality, users must leverage the proprietary user interfaces or programming languages to express their logic. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

Occam's Razor

MARCH 23, 2021

This is very hard to do, we now have a proven seven-step experimentation process, with one of the coolest algorithms to pick matched-markets (normally the kiss of death of any large-scale geo experiment). What does the diminishing returns curve look like for TV GRPs for our company? More shouting is not really better – and it is expensive!

Analytics 131

Analytics Scorecard Strategy Testing 131

DataRobot

JUNE 8, 2021

Although modern medicine is founded on rigorous experimental design and statistical analysis, and many research studies have shown the superiority of objective analysis over human intuition, medical AI adoption will depend on consumer receptivity and trust in this new technology.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

FineReport

DECEMBER 19, 2019

In addition, Jupyter Notebook is also an excellent interactive tool for data analysis and provides a convenient experimental platform for beginners. Pandas incorporates a large number of analysis function methods, as well as common statistical models and visualization processing. From Google. Data Analysis Libraries.

Knowledge Discovery 98

Knowledge Discovery Visualization Reporting Experimentation 98

The Unofficial Google Data Science Blog

JANUARY 16, 2018

Experimentation on networks A/B testing is a standard method of measuring the effect of changes by randomizing samples into different treatment groups. However, the downside of using a larger unit of randomization is that we lose experimental power. This simulation is based on the actual user network of GCP.

Testing 58

Testing Experimentation Measurement Modeling 58

The Unofficial Google Data Science Blog

JULY 22, 2020

For example, imagine a fantasy football site is considering displaying advanced player statistics. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. One reason to do ramp-up is to mitigate the risk of never before seen arms.

Experimentation 95

Experimentation Statistics Testing Strategy 95

Ontotext

APRIL 3, 2024

Nimit Mehta: I think that 2024 is going to be a buckle-down year, but, at the same time, we’ll see a rapid explosion of experimentation. These are not statistical inferences. So, this is a big driver for the outcome because when you are saving money for the business, you can measure it and see its value. What is a customer?

Technology 59

Technology Risk Digital Transformation Measurement 59

O'Reilly on Data

APRIL 19, 2021

We’ll look at this later, but being able to reproduce experimental results is critical to any science, and it’s a well-known problem in AI. It’s more concerning that workflow reproducibility (3%) is in second-to-last place. This makes sense, given that we don’t see heavy usage of tools for model and data versioning. Maturity by Continent.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

Occam's Razor

AUGUST 22, 2011

Build A Great Web Experimentation & Testing Program. Experimentation and Testing: A Primer. Tip #9: Leverage Statistical Control Limits. Tip#1: Statistical Significance. Web Analytics Career Advice: Statistics, Business, IT & Mushrooms. Eight Tips For Choosing An Online Survey Provider. Got Surveys?

KPI 124

KPI Metrics Measurement ROI 124

Data Science and Beyond

DECEMBER 23, 2018

The book focuses on randomised controlled trials and well-defined interventions as the basis of causal inference from both experimental and observational data. Hence, the book is full of practical examples.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Alation

SEPTEMBER 28, 2022

Technical environments and IDEs must be disposable so that experimental costs can be kept to a minimum. Quality must be monitored continuously to catch unexpected variation cases and produce statistics on its operation. Everything must be versioned so that the code and configurations made are reproducible. Disposable environments.

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

The Unofficial Google Data Science Blog

JUNE 30, 2016

In an ideal world, experimentation through randomization of the treatment assignment allows the identification and consistent estimation of causal effects. Identification We now discuss formally the statistical problem of causal inference. We start by describing the problem using standard statistical notation.

Statistics 44

Statistics Optimization Modeling Experimentation 44

Domino Data Lab

DECEMBER 4, 2020

Domino Lab supports both interactive and batch experimentation with all popular IDEs and notebooks (Jupyter, RStudio, SAS, Zeppelin, etc.). We can group by study arm and calculate various statistics as mean and standard deviation. In this tutorial we will use JupyterLab. We can extract the two in a separate DataFrame.

Metrics 59

Metrics Knowledge Discovery Data Science Measurement 59

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. The team did the normal modeling to ensure that the results were statistically significant (large enough sample set, sufficient number of conversions in each variation). * ask for a raise. It is that simple. Okay, it is not simple.

Measurement 119

Measurement Testing Advertising Marketing 119

Insight

APRIL 28, 2020

You’ll often see the name “data challenge” used when the take-home assignment involves machine learning or statistics or “coding challenge” when the focus is on evaluating a candidate’s software engineering skills. Length: Highly Variable.

Data-driven 53

Data-driven Data Science Experimentation Measurement 53

Business Over Broadway

MAY 22, 2019

According to Gartner, companies need to adopt these practices: build culture of collaboration and experimentation; start with a 3-way partnership among executives leading digital initiative, line of business and IT. Also, loyalty leaders infuse analytics into CX programs, including machine learning, data science and data integration.

Machine Learning 40

Machine Learning Technology Digital Transformation Data Science 40