O'Reilly on Data

JULY 28, 2020

Without clarity in metrics, it’s impossible to do meaningful experimentation. AI PMs must ensure that experimentation occurs during three phases of the product lifecycle: Phase 1: Concept During the concept phase, it’s important to determine if it’s even possible for an AI product “ intervention ” to move an upstream business metric.

Marketing 362

Marketing Experimentation Metrics Testing 362

CIO Business Intelligence

MAY 12, 2022

The tools include sophisticated pipelines for gathering data from across the enterprise, add layers of statistical analysis and machine learning to make projections about the future, and distill these insights into useful summaries so that business users can act on them. A free plan allows experimentation. On premises or in SAP cloud.

Predictive Analytics 136

Predictive Analytics Analytics Statistics Machine Learning 136

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

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 121

Big Data Data Analytics Analytics Predictive Modeling 121

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

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

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

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

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

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

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

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

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

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

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

Smarten

DECEMBER 11, 2017

Advanced Data Discovery ensures data democratization and can drastically reduce the time and cost of analysis and experimentation. A business user with average skills can do all of this without specialized skills, knowledge of statistical analysis or support from IT or professional data scientists.

Experimentation 65

Experimentation Statistics Visualization Dashboards 65

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 132

Unstructured Data Data Analytics Analytics Structured Data 132

Business Over Broadway

FEBRUARY 21, 2021

As such, data science requires three broad skill sets , including subject matter expertise, statistics/math and technology/programming. Experimentation and iteration to improve existing ML models (25%). The practice of data science is about extracting value from data to help inform decision making and improve algorithms.

Machine Learning 60

Machine Learning Statistics Data Science Experimentation 60

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 84

Digital Transformation Cost-Benefit Informatics Data mining 84

The Unofficial Google Data Science Blog

APRIL 16, 2019

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. But these are not usually amenable to A/B experimentation.

Experimentation 68

Experimentation Sales Metrics Measurement 68

The Unofficial Google Data Science Blog

OCTOBER 12, 2017

1]" Statistics, as a discipline, was largely developed in a small data world. More people than ever are using statistical analysis packages and dashboards, explicitly or more often implicitly, to develop and test hypotheses. Data was expensive to gather, and therefore decisions to collect data were generally well-considered.

Experimentation 61

Experimentation Testing Statistics Metrics 61

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

The Unofficial Google Data Science Blog

FEBRUARY 29, 2016

In this post we explore why some standard statistical techniques to reduce variance are often ineffective in this “data-rich, information-poor” realm. Despite a very large number of experimental units, the experiments conducted by LSOS cannot presume statistical significance of all effects they deem practically significant.

Experimentation 40

Experimentation Metrics Statistics Measurement 40

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

Data Science and Beyond

JULY 22, 2018

Two years later, I published a post on my then-favourite definition of data science , as the intersection between software engineering and statistics. Like other authors, they argue that causal inference has been neglected by traditional statistics and some scientific disciplines. In a recent article , Hernán et al.

Data Science 79

Data Science Machine Learning Statistics Predictive Modeling 79

Paul DeBeasi

JANUARY 30, 2019

Machine learning projects are inherently different from traditional IT projects in that they are significantly more heuristic and experimental, requiring skills spanning multiple domains, including statistical analysis, data analysis and application development.

Machine Learning 56

Machine Learning IoT Experimentation Statistics 56

Smarten

MAY 11, 2017

Advanced Data Discovery ensures data democratization by enabling users to drastically reduce the time and cost of analysis and experimentation. Plug n’ Play Predictive Analysis enables business users to explore power of predictive analytics without indepth understanding of statistics and data science.

Experimentation 49

Experimentation Visualization Business Intelligence Predictive Analytics 49

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

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

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 Experimentation Reporting 98

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

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

MARCH 22, 2018

But what if users don't immediately uptake the new experimental version? Background At Google, experimentation is an invaluable tool for making decisions and inference about new products and features. by DANIEL PERCIVAL Randomized experiments are invaluable in making product decisions, including on mobile apps.

Experimentation 62

Experimentation Measurement Modeling Statistics 62

Depict Data Studio

MARCH 27, 2023

About the Instructor: Stefanie Posavec According to Stefanie’s website , she is “a designer, artist, and author exploring experimental approaches to communicating data and information to all ages and audiences.” But nothing compares to quality online training where your brain is fully immersed in the topic alongside the instructor and peers.

Visualization 52

Visualization Consulting Experimentation Dashboards 52