CIO Business Intelligence

OCTOBER 13, 2023

Here are the top 11 roles companies are currently hiring for, or have plans to hire for, to directly address their emerging gen AI strategies. Data scientist As companies embrace gen AI, they need data scientists to help drive better insights from customer and business data using analytics and AI.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Business Over Broadway

SEPTEMBER 27, 2018

In this paper, I show you how marketers can improve their customer retention efforts by 1) integrating disparate data silos and 2) employing machine learning predictive analytics. Your marketing strategy is only as good as your ability to deliver measurable results. Machine Learning and Predictive Modeling of Customer Churn.

Machine Learning 76

Machine Learning Data Integration Marketing Predictive Modeling 76

O'Reilly on Data

DECEMBER 12, 2019

You’ve even discovered a few problems with your ML model. That’s where remediation strategies come in. We discuss seven remediation strategies below. Data augmentation. ML models learn from data to become accurate, and ML models require data that’s truly representative of the entire problem space being modeled.

Machine Learning 187

Machine Learning Modeling Testing Risk Management 187

Birst BI

AUGUST 23, 2018

What if some of these data science tasks could be automated using AI, increasing data science productivity to tackle more AI use cases? Automating data science tasks leaves room to build more AI applications with the same amount of data science resources. Source: Gartner (April 2018).

Machine Learning 73

Machine Learning Data Science Testing Metrics 73

Insight

AUGUST 5, 2020

Summary statistics (i.e. This created a summary features matrix of 7472 recordings x 176 summary features, which was used for training emotion label prediction models. Prediction models An Exploratory Data Analysis showed improved performance was dependent on gender and emotion. the Mel-frequency cepstrum).

Predictive Modeling 98

Predictive Modeling Statistics Modeling Measurement 98

O'Reilly on Data

MARCH 20, 2019

The objective here is to brainstorm on potential security vulnerabilities and defenses in the context of popular, traditional predictive modeling systems, such as linear and tree-based models trained on static data sets. If an attacker can receive many predictions from your model API or other endpoint (website, app, etc.),

Modeling 219

Modeling Machine Learning Predictive Modeling Consulting 219

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. Let’s also look at the basic descriptive statistics for all attributes. 3f" % x) dataDF.describe().

Statistics 69

Statistics Machine Learning Modeling Metrics 69

IBM Big Data Hub

DECEMBER 20, 2023

ML is a computer science, data science and artificial intelligence (AI) subset that enables systems to learn and improve from data without additional programming interventions. With repetition, the agent learns the best strategies. What is machine learning?

Machine Learning 78

Machine Learning Modeling Deep Learning Predictive Modeling 78

Domino Data Lab

JULY 9, 2019

Chris Wiggins , Chief Data Scientist at The New York Times, presented “Data Science at the New York Times” at Rev. Wiggins also indicated that data science, data engineering, and data analysis are different groups at The New York Times. Session Summary. Transcript. Feel free to email me.

Data Science 112

Data Science Machine Learning Advertising Modeling 112

Domino Data Lab

OCTOBER 23, 2019

As a result, there has been a recent explosion in individual statistics that try to measure a player’s impact. Eighty percent of this problem is collecting the data and then transforming the data. The other 20 percent is ML- and data science–related tasks like finding the right model, doing EDA, and feature engineering.

Statistics 59

Statistics Machine Learning Testing Modeling 59

Rocket-Powered Data Science

OCTOBER 6, 2023

What is the point of those obvious statistical inferences? The point is that the 100% association between the event and the preceding condition has no special predictive or prescriptive power. How do predictive and prescriptive analytics fit into this statistical framework?

Business Analytics 157

Business Analytics Prescriptive Analytics Analytics Diagnostic Analytics 157

The Unofficial Google Data Science Blog

MARCH 31, 2016

Finally, through a case study of a real-world prediction problem, we also argue that Random Effect models should be considered alongside penalized GLM's even for pure prediction problems. Random effects models are a useful tool for both exploratory analyses and prediction problems. 5] Anoop Korattikara, et al.

Modeling 40

Modeling Statistics Advertising Testing 40

Domino Data Lab

AUGUST 20, 2019

So, if we need to dump some data, how do we do it? There are three major strategies: manual techniques based on our knowledge of the problem and the data, random sampling techniques that use coin flipping to keep or discard data, and model-based techniques that attempt to keep features that interact well with our learning model.

Testing 68

Testing Modeling Interactive Measurement 68

Smarten

MARCH 13, 2024

For a number of years, Gartner and other technology research and analysis firms have predicted and monitored the growth of this phenomenon. In fact, Gartner predicted that, ‘…40% of data science tasks will be automated, resulting in increased productivity and broader usage by citizen data scientists.’

Analytics 52

Analytics Predictive Modeling Strategy Enterprise 52

datapine

NOVEMBER 27, 2019

In 2020, BI tools and strategies will become increasingly customized. It is not only important to gather as much information possible, but the quality and the context in which data is being used and interpreted serves as the main focus for the future of business intelligence. Source: Business Application Research Center *.

Business Intelligence 361

Business Intelligence Analytics Prescriptive Analytics Data Quality 361

datapine

DECEMBER 4, 2019

Applied to business, it is used to analyze current and historical data in order to better understand customers, products, and partners and to identify potential risks and opportunities for a company. The commercial use of predictive analytics is a relatively new thing. Last but not least, there is the human factor again.

Business Intelligence 212

Business Intelligence Prescriptive Analytics Analytics Predictive Analytics 212

Data Science and Beyond

JANUARY 8, 2017

Indeed, several people have commented on the flaws in Kissmetrics’ approach, which is reminiscent of the Dilbert strip where the pointy-haired boss asks Dilbert to average and multiply wrong data. If you only take away one thing from this article, let it be the reminder that it is unwise to blindly accept any formula or model.

Statistics 100

Statistics Modeling Predictive Modeling Marketing 100

Jet Global

MAY 9, 2024

Data pipelines are designed to automate the flow of data, enabling efficient and reliable data movement for various purposes, such as data analytics, reporting, or integration with other systems. There are many types of data pipelines, and all of them include extract, transform, load (ETL) to some extent.

Data Lake 52

Data Lake Data Warehouse Business Intelligence Machine Learning 52