#dataanlytics — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #dataanlytics, aggregated by home.social.
-
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source
https://quadexcel.com/wp/mistake-to-avoid-using-tableau-dataanlytics-datascience-tech-analytics/
-
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source
https://quadexcel.com/wp/mistake-to-avoid-using-tableau-dataanlytics-datascience-tech-analytics/
-
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source
https://quadexcel.com/wp/mistake-to-avoid-using-tableau-dataanlytics-datascience-tech-analytics/
-
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics
Mistake to avoid using tableau #dataanlytics #datascience #tech #analytics Scene 1 (Hook - 5s) Text on screen: "New to ... source
https://quadexcel.com/wp/mistake-to-avoid-using-tableau-dataanlytics-datascience-tech-analytics/
-
Learn Python for Data Analysis #shorts
python #dataanlytics. source
https://quadexcel.com/wp/learn-python-for-data-analysis-shorts/
-
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics
-
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics
-
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics
-
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics
-
How to use Filter Formula for Stock inventory #excel #exceltutorial #datascience #dataanlytics
-
How to calculate Rank in Excel #excel #datascience #dataanlytics #exceltutorial #girlcode
-
How to calculate Rank in Excel #excel #datascience #dataanlytics #exceltutorial #girlcode
-
How to calculate Rank in Excel #excel #datascience #dataanlytics #exceltutorial #girlcode
-
How to calculate Rank in Excel #excel #datascience #dataanlytics #exceltutorial #girlcode
-
How to calculate Rank in Excel #excel #datascience #dataanlytics #exceltutorial #girlcode
-
My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.
All are new positions in a growing national non-profit, established back in the 90s and still going strong.
Data Analytics Manager
IT Project Manager
Helpdesk Technicianhttps://www.aanp.org/aanp-careers
#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening
-
My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.
All are new positions in a growing national non-profit, established back in the 90s and still going strong.
Data Analytics Manager
IT Project Manager
Helpdesk Technicianhttps://www.aanp.org/aanp-careers
#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening
-
My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.
All are new positions in a growing national non-profit, established back in the 90s and still going strong.
Data Analytics Manager
IT Project Manager
Helpdesk Technicianhttps://www.aanp.org/aanp-careers
#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening
-
My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.
All are new positions in a growing national non-profit, established back in the 90s and still going strong.
Data Analytics Manager
IT Project Manager
Helpdesk Technicianhttps://www.aanp.org/aanp-careers
#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening
-
My team has a few positions open. We've been working through our candidate lists but there is still a little time to apply if you're interested.
All are new positions in a growing national non-profit, established back in the 90s and still going strong.
Data Analytics Manager
IT Project Manager
Helpdesk Technicianhttps://www.aanp.org/aanp-careers
#FediHire #DataAnlytics #ProjectManagement #Helpdesk #JobOpening
-
Big Data Test Infrastructure (BDTI) is supporting data reuse for better policies and more efficient #PublicServices.
We're launching the BDTI Kitchen newsletter to serve you fresh #DataAnlytics recipes right to your mailbox! 👨🍳
Sign up! 👉 https://europa.eu/!j4dTPf
-
Big Data Test Infrastructure (BDTI) is supporting data reuse for better policies and more efficient #PublicServices.
We're launching the BDTI Kitchen newsletter to serve you fresh #DataAnlytics recipes right to your mailbox! 👨🍳
Sign up! 👉 https://europa.eu/!j4dTPf
-
Big Data Test Infrastructure (BDTI) is supporting data reuse for better policies and more efficient #PublicServices.
We're launching the BDTI Kitchen newsletter to serve you fresh #DataAnlytics recipes right to your mailbox! 👨🍳
Sign up! 👉 https://europa.eu/!j4dTPf
-
Question for #data people -- I'm trying to understand headless BI. In particular, what it solves that SQL doesn't. In other words, why dbt (pre-analytics layer) isn't enough.
From what I can tell, it solves the problem that SQL can't easily be parameterized. You can make views that slice and dice your transactional data, but those views would hardcode a bunch of decisions better left up to the consumer. You can also denormalize the heck out of your data to make all conceivable queries easy, but then you end up with an analytical table that's way too tall and wide.
It seems like what these semantic layer / headless BI tools do is apply the metadata to your transactional data that allow for BI tools to offer slick query builder interfaces, which ultimately are generating SQL. Furthermore, the logic for different types of analysis can be standardized and controlled, compared to people handwriting their queries.
Do I have this right?
#semanticlayer #analyticslayer #headlessbi #businessintelligence #analytics #dataanlytics #datamastodon
-
Question for #data people -- I'm trying to understand headless BI. In particular, what it solves that SQL doesn't. In other words, why dbt (pre-analytics layer) isn't enough.
From what I can tell, it solves the problem that SQL can't easily be parameterized. You can make views that slice and dice your transactional data, but those views would hardcode a bunch of decisions better left up to the consumer. You can also denormalize the heck out of your data to make all conceivable queries easy, but then you end up with an analytical table that's way too tall and wide.
It seems like what these semantic layer / headless BI tools do is apply the metadata to your transactional data that allow for BI tools to offer slick query builder interfaces, which ultimately are generating SQL. Furthermore, the logic for different types of analysis can be standardized and controlled, compared to people handwriting their queries.
Do I have this right?
#semanticlayer #analyticslayer #headlessbi #businessintelligence #analytics #dataanlytics #datamastodon
-
Question for #data people -- I'm trying to understand headless BI. In particular, what it solves that SQL doesn't. In other words, why dbt (pre-analytics layer) isn't enough.
From what I can tell, it solves the problem that SQL can't easily be parameterized. You can make views that slice and dice your transactional data, but those views would hardcode a bunch of decisions better left up to the consumer. You can also denormalize the heck out of your data to make all conceivable queries easy, but then you end up with an analytical table that's way too tall and wide.
It seems like what these semantic layer / headless BI tools do is apply the metadata to your transactional data that allow for BI tools to offer slick query builder interfaces, which ultimately are generating SQL. Furthermore, the logic for different types of analysis can be standardized and controlled, compared to people handwriting their queries.
Do I have this right?
#semanticlayer #analyticslayer #headlessbi #businessintelligence #analytics #dataanlytics #datamastodon
-
Question for #data people -- I'm trying to understand headless BI. In particular, what it solves that SQL doesn't. In other words, why dbt (pre-analytics layer) isn't enough.
From what I can tell, it solves the problem that SQL can't easily be parameterized. You can make views that slice and dice your transactional data, but those views would hardcode a bunch of decisions better left up to the consumer. You can also denormalize the heck out of your data to make all conceivable queries easy, but then you end up with an analytical table that's way too tall and wide.
It seems like what these semantic layer / headless BI tools do is apply the metadata to your transactional data that allow for BI tools to offer slick query builder interfaces, which ultimately are generating SQL. Furthermore, the logic for different types of analysis can be standardized and controlled, compared to people handwriting their queries.
Do I have this right?
#semanticlayer #analyticslayer #headlessbi #businessintelligence #analytics #dataanlytics #datamastodon