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. It requires completion of the CAP exam and adherence to the CAP Code of Ethics.

Big Data 124

Big Data Data Analytics Analytics Predictive Modeling 124

The Unofficial Google Data Science Blog

APRIL 23, 2024

If $Y$ at that point is (statistically and practically) significantly better than our current operating point, and that point is deemed acceptable, we update the system parameters to this better value. Here, $X$ is a vector of tuning parameters that control the system's operating characteristics (e.g.

Experimentation 52

Experimentation Optimization Uncertainty Metrics 52

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

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. If you’re already a software product manager (PM), you have a head start on becoming a PM for artificial intelligence (AI) or machine learning (ML).

Management 333

Management Machine Learning Experimentation Metrics 333

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. Let us understand this in brief. This is called Hawthorne Effect.

Experimentation 96

Experimentation Statistics Measurement Testing 96

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

IBM Big Data Hub

MAY 17, 2024

” Given the statistics—82% of surveyed respondents in a 2023 Statista study cited managing cloud spend as a significant challenge—it’s a legitimate concern. ” This quandary often comes with an accompanying worry: “Are we spending too much money on cloud computing?” Why move to cloud?

Modeling 97

Modeling Cost-Benefit Optimization Digital Transformation 97

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.

Experimentation 103

Experimentation Testing Optimization Measurement 103

Ontotext

FEBRUARY 14, 2024

Based on our experience, we’ve found that there are some common stumbling blocks. Let’s have a look at those. Why You need to make sure the people in your organization know the why of the knowledge graph project. If they know why they are doing it, they’ll be in a better position to deliver what they’ve been asked to do.

Recreation/Entertainment 105

Recreation/Entertainment Data Integration Modeling Data Quality 105

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. For price discovery (e.g.,

Machine Learning 87

Machine Learning Marketing Experimentation Modeling 87

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). But data, at best, can only be a powerful vehicle, or a reliable GPS system. Well, that’s obvious, you say.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

CIO Business Intelligence

MARCH 21, 2022

Data scientists are becoming increasingly important in business, as organizations rely more heavily on data analytics to drive decision-making and lean on automation and machine learning as core components of their IT strategies. Data scientist job description. Semi-structured data falls between the two. Data scientist requirements.

Unstructured Data 135

Unstructured Data Data Analytics Analytics Structured Data 135

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. Edge Computing (and Edge Analytics): Industry 4.0: Industry 4.0

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. For a while, everyone seems to have begun to learn data analysis. New York Times.

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

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. Belcorp operates under a direct sales model in 14 countries.

Digital Transformation 89

Digital Transformation Cost-Benefit Informatics Data mining 89

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 accuracy of machine learning models is highly dependent on the quality of the training data. can make it difficult to analyze the actions of ordinary users.

Data Science 72

Data Science Machine Learning Statistics Modeling 72

DataRobot

JANUARY 13, 2022

Data scientists typically come equipped with skills in three key areas: mathematics and statistics, data science methods, and domain expertise. Many companies find that they have a treasure trove of data but lack the expertise to use it to improve ROI. AI can help them wrangle the data that’s collected and quickly answer important questions.

ROI 52

ROI Machine Learning Experimentation Data Science 52

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. Data analytics ain’t what it used to be. You’re providing data analytics products. . Lean manufacturing.

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

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

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. This kind of analysis, in most cases, can lead to insights that can be later translated to business actions.

Testing 104

Testing Risk Data-driven Statistics 104

Occam's Razor

MARCH 23, 2021

Data Warehousing. And, a thousand other silos (depending on your company size). One outcome of this reality is that while every team works hard to do their very best work, it is rare that they earn strategic influence from their work. That’s not really surprising, if your view of your scope is narrow… Your impact will be narrow as well.

Analytics 131

Analytics Scorecard Strategy Testing 131

O'Reilly on Data

MARCH 23, 2023

ChatGPT, or something built on ChatGPT, or something that’s like ChatGPT, has been in the news almost constantly since ChatGPT was opened to the public in November 2022. What is it, how does it work, what can it do, and what are the risks of using it? A quick scan of the web will show you lots of things that ChatGPT can do. It’s much more.

IT 261

IT Modeling Testing Risk 261

O'Reilly on Data

APRIL 19, 2021

After eliminating 1,580 respondents who didn’t complete the survey, we’re left with 3,574 responses—almost three times as many as last year. It’s possible that pandemic-induced boredom led more people to respond, but we doubt it. Whether they’re putting products into production or just kicking the tires, more people are using AI than ever before.

Enterprise 356

Enterprise Modeling Risk Manufacturing 356

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. But others have criticized the use of personas.

Data-driven 98

Data-driven Testing Marketing Experimentation 98

FineReport

DECEMBER 19, 2019

In fact, it’s a BI analysis tool specifically for business people, helping them get rid of the constraints of traditional IT and complete data analysis work independently. For the super rookie, the learning cost and threshold are relatively low, and it is easy to get started. Its designer interface is similar to Excel.

Knowledge Discovery 98

Knowledge Discovery Visualization Experimentation Reporting 98

IBM Big Data Hub

APRIL 18, 2024

LLMs like ChatGPT are trained on massive amounts of text data, allowing them to recognize patterns and statistical relationships within language. This is the potential of artificial general intelligence (AGI), a hypothetical technology that may be poised to revolutionize nearly every aspect of human life and work.

Cost-Benefit 90

Cost-Benefit Modeling Manufacturing Interactive 90

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., In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. Code-first data science platforms. Jupyter) or IDEs (e.g.,

Data Science 65

Data Science Marketing Data-driven Statistics 65

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

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. So, let’s start with our first question. We all know that for any initiative to succeed, we need the attention and the blessing of company executives. What are your thoughts about it? A graph can do that.

Technology 59

Technology Risk Digital Transformation Measurement 59

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. In this post we discuss the problem, a solution, and practical considerations.

Experimentation 95

Experimentation Statistics Testing Strategy 95

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. I’ve been interested in the area of causal inference in the past few years. However, I’ve found it hard to apply what I’ve learned about causal inference to my work.

Deep Learning 103

Deep Learning Experimentation Data-driven Data Science 103

Alation

SEPTEMBER 28, 2022

What exactly is DataOps ? The term has been used a lot more of late, especially in the data analytics industry, as we’ve seen it expand over the past few years to keep pace with new regulations, like the GDPR and CCPA. In essence, DataOps is a practice that helps organizations manage and govern data more effectively. What Is DataOps?

Cost-Benefit 52

Cost-Benefit Data Quality Manufacturing Testing 52

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.). When analysing pharmacokinetic data to determine the degree of exposure of a drug and associated pharmacokinetic parameters (e.g., Mean residence time. Terminal disposition rate constant. x)), pain_df).

Metrics 59

Metrics Data Science Knowledge Discovery Measurement 59

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. To assist with that process everything's organized into these sections: ~ Digital Marketing: "What is amazing out there? Go Get Your Own.

KPI 124

KPI Metrics Measurement ROI 124

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

Occam's Razor

SEPTEMBER 19, 2011

You need people with deep skills in Scientific Method , Design of Experiments , and Statistical Analysis. How do you figure out if you are spending more money reaching the exact same current or prospective customers multiple times? Maybe it is Search, Email and Facebook. Maybe it is Affiliate and Paid Search. Maybe it is five other things.

Measurement 119

Measurement Advertising Testing Marketing 119