CIO Business Intelligence

JUNE 7, 2022

Predictive analytics applies techniques such as statistical modeling, forecasting, and machine learning to the output of descriptive and diagnostic analytics to make predictions about future outcomes. Data analytics and data science are closely related. Data analytics vs. data analysis.

Data Analytics 133

Data Analytics Diagnostic Analytics Management Analytics 133

CIO Business Intelligence

OCTOBER 13, 2023

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. For most companies, AI systems rely on large datasets, which require the expertise of data scientists to navigate.

Deep Learning 143

Deep Learning Machine Learning Modeling Software 143

Smart Data Collective

SEPTEMBER 16, 2020

Predictive analytics, sometimes referred to as big data analytics, relies on aspects of data mining as well as algorithms to develop predictive models. These predictive models can be used by enterprise marketers to more effectively develop predictions of future user behaviors based on the sourced historical data.

Predictive Analytics 122

Predictive Analytics Analytics Predictive Modeling Data mining 122

Sisense

APRIL 10, 2020

One of the most-asked questions from aspiring data scientists is: “What is the best language for data science? People looking into data science languages are usually confused about which language they should learn first: R or Python. NLP can be used on written text or speech data. R or Python?”.

Deep Learning 98

Deep Learning Data Science Machine Learning Visualization 98

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

AUGUST 1, 2021

The interest in interpretation of machine learning has been rapidly accelerating in the last decade. This can be attributed to the popularity that machine learning algorithms, and more specifically deep learning, has been gaining in various domains. Methods for explaining Deep Learning. References.

Modeling 139

Modeling Deep Learning Machine Learning Knowledge Discovery 139

IBM Big Data Hub

AUGUST 28, 2023

One of the best ways to take advantage of social media data is to implement text-mining programs that streamline the process. Text representation In this stage, you’ll assign the data numerical values so it can be processed by machine learning (ML) algorithms, which will create a predictive model from the training inputs.

Data mining 59

Data mining Machine Learning Deep Learning Marketing 59

O'Reilly on Data

DECEMBER 12, 2019

Figure 1 illustrates an example adversarial search for an example credit default ML model. If you’re using Python and deep learning libraries, the CleverHans and Foolbox packages can also help you debug models and find adversarial examples. Interpretable ML models and explainable ML.

Machine Learning 191

Machine Learning Modeling Testing Risk Management 191

IBM Big Data Hub

DECEMBER 20, 2023

What is machine learning? 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. Here, we’ll discuss the five major types and their applications.

Machine Learning 82

Machine Learning Modeling Deep Learning Predictive Modeling 82

Alation

APRIL 14, 2022

A useful feature for exposing patterns in the data. Visual Profiling. Supports the ability to interact with the actual data and perform analysis on it. The vision of big data freed organizations to capture more data sources at lower levels of detail and in vastly greater volumes. Parametrization. Pattern Matching.

Metadata 59

Metadata Data Governance Modeling Data-driven 59

O'Reilly on Data

SEPTEMBER 11, 2019

Machine learning, artificial intelligence, data engineering, and architecture are driving the data space. The Strata Data Conferences helped chronicle the birth of big data, as well as the emergence of data science, streaming, and machine learning (ML) as disruptive phenomena. 221) to 2019 (No.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20