Cloudera

FEBRUARY 14, 2018

We share the same obstacles our customers face – most of which are around data collection, data quality (or lack thereof), data governance as well as misalignment or miscommunication about who is responsible and accountable for managing and analyzing different datasets and analytical outcomes. Replicate success.

Data-driven 44

Data-driven Data Quality Risk Interactive 44

AWS Big Data

FEBRUARY 1, 2024

Set up Athena to run interactive SQL. Despite the original C4 data being part of Common Crawl, Meta opted for the reprocessed version of this data. In this section, we cover common ways to interact, filter, and process the Common Crawl dataset. For instructions, refer to Create your first S3 bucket.

Metadata 99

Metadata Modeling Data Processing Unstructured Data 99

Jet Global

OCTOBER 2, 2019

And how can the data collected across multiple touchpoints, from retail locations to the supply chain to the factory be easily integrated? Enter data warehousing. In the near future, AI algorithms will be able to be seamlessly applied to your existing data stores, unlocking further insight for your enterprise.

Enterprise 40

Enterprise OLAP Business Intelligence Cost-Benefit 40

Alation

JULY 7, 2022

In 2018, American Family Insurance became an Alation customer and I became the product owner for the AmFam catalog program. Supporting the next data-literate generation. Data collection has exploded, and this poses both challenges and opportunities. Our paths converge. By now, I was confident in my role.

Metadata 52

Metadata Data-driven Insurance Statistics 52

Depict Data Studio

SEPTEMBER 15, 2020

I would help train their nonprofits on evaluation, data collection, data analysis, and data visualization. I went for it in Spring of 2018. I’ve got this perfect demonstration I did at a conference in 2018 on how to survey children. You can make it interactive just like you do a regular presentation.

Visualization 105

Visualization Consulting Dashboards Reporting 105

Domino Data Lab

MAY 8, 2019

The lens of reductionism and an overemphasis on engineering becomes an Achilles heel for data science work. Instead, consider a “full stack” tracing from the point of data collection all the way out through inference. 2018-06-21). data collection”) show the “process” steps that a team performs, while the boxes (e.g.,

Machine Learning 105

Machine Learning Data Science Modeling Visualization 105

Domino Data Lab

FEBRUARY 4, 2019

People who attended JupyterCon 2017–2018 can attest, an “industry poster session” includes an open bar, catered hors d’oeuvres, lots of mingling … to paraphrase feedback from JupyterCon, “As a tech person, would I get up extra early to meet strangers for coffee at 8:00 am? Pierre-Yves Oudeyer (2018). This is not that. Or something.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66

Domino Data Lab

JULY 22, 2019

By virtue of that, if you take those log files of customers interactions, you aggregate them, then you take that aggregated data, run machine learning models on them, you can produce data products that you feed back into your web apps, and then you get this kind of effect in business. It’s also a driver for data science.

Data Science 109

Data Science Machine Learning Data Governance Modeling 109

Domino Data Lab

OCTOBER 23, 2019

df = pd.DataFrame(data=records). The last function stats_df, can now be used to interactively explore the results of a Twitter API call. With all the data collected, there are some interesting new plots to test out. By combining the endorsement, team, and player data, it is possible to make a couple of fascinating plots.

Statistics 59

Statistics Machine Learning Testing Modeling 59