Octopai

OCTOBER 26, 2022

DataOps is an approach to best practices for data management that increases the quantity of data analytics products a data team can develop and deploy in a given time while drastically improving the level of data quality. Automated workflows for data product creation, testing and deployment.

Data Quality 52

Data Quality Data Analytics Analytics Manufacturing 52

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. Candidates have 90 minutes to complete the exam.

Big Data 127

Big Data Data Analytics Analytics Predictive Modeling 127

Octopai

MAY 2, 2022

Today, your business users have the same perspective on data analytics. Your dashboards, charts, visualizations… they’re all products. . A successful data analytics team is one that can increase the quantity of data analytics products they develop in a given time while ensuring (and ideally, improving) the level of data quality.

Manufacturing 87

Manufacturing Testing Experimentation Data Quality 87

Smarten

NOVEMBER 3, 2017

The tools exist today for augmented analytics, augmented data discovery, self-serve data preparation and other features and modules that provide sophisticated functionality and algorithms in an easy-to-use dashboard and environment that is designed to support business users, as well as data scientists and IT staff.

Analytics 40

Analytics Visualization Experimentation Cost-Benefit 40

Occam's Razor

AUGUST 22, 2011

Slay The Analytics Data Quality Dragon & Win Your HiPPO's Love! Web Data Quality: A 6 Step Process To Evolve Your Mental Model. The Ultimate Web Analytics Data Reconciliation Checklist. The "Action Dashboard" (An Alternative To Crappy Dashboards). Six Data Visualizations That Rock!

KPI 124

KPI Metrics Measurement ROI 124

DataKitchen

APRIL 13, 2021

DataOps needs a directed graph-based workflow that contains all the data access, integration, model and visualization steps in the data analytic production process. It orchestrates complex pipelines, toolchains, and tests across teams, locations, and data centers. OwlDQ — Predictive data quality.

Testing 307

Testing Machine Learning Consulting Data Quality 307

Domino Data Lab

JUNE 23, 2019

Skomoroch proposes that managing ML projects are challenging for organizations because shipping ML projects requires an experimental culture that fundamentally changes how many companies approach building and shipping software. ” There’s either incomplete data, missing tracking data or duplicative tracking data, things like that.

Management 110

Management Machine Learning Experimentation Metrics 110