Sisense

MARCH 6, 2019

Gartner chose to group the rest of the keynote into three main messages according to the following categories: Here are some of the highlights as presented for each of them: Data Driven – “Adopt an Experimental Mindset”. At Sisense we’ve been preaching for BI prototyping and experimentation for quite a while now.

Data Analytics 75

Data Analytics Analytics Experimentation Data-driven 75

O'Reilly on Data

SEPTEMBER 11, 2019

For nearly a decade, it’s provided a venue for developers, data and ML engineers, data architects, data scientists, and others to acquire or hone skills, explore provocative ideas, and network with peers. 221) to 2019 (No. Meanwhile, “data preparation" is in freefall: it sits at No. 2 in 2016 to No. 30 in 2018.

IoT 20

IoT Big Data Data Warehouse Uncertainty 20

DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Acquired by DataRobot June 2019).

Testing 300

Testing Machine Learning Consulting Data Quality 300

Domino Data Lab

FEBRUARY 4, 2019

We’ll unpack curiosity as a core attribute of effective data science, look at how that informs process for data science (in contrast to Agile, etc.), and dig into details about where science meets rhetoric in data science. That body of work has much to offer the practice of leading data science teams. ethics in AI.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Corinium

APRIL 25, 2019

It definitely depends on the type of data, no one method is always better than the other. For a large volume of structured data, for example, a customer master or data warehouse, where there are many stakeholders in your organization who need to see different subsets, tokenization is generally better.

Insurance 150

Insurance Risk IoT Cost-Benefit 150

Domino Data Lab

JULY 2, 2019

See also: Caroline Lemieux’s slides for that NeurIPS talk, and Rohan Bavishi’s video from the RISE Summer Retreat 2019. The authors of AutoPandas observed that: The APIs for popular data science packages tend to have relatively steep learning curves. Program Synthesis 101 ” – Alexander Vidiborskiy (2019-01-20).

Metadata 105

Metadata Machine Learning Data Science Data-driven 105