Discover and read the best of Twitter Threads about #DataQuality
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#SQL Cheatsheet #infographic
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#Databases #DataScientists #DataScience #DataManagement #DataProfiling #DataLiteracy #DataQuality
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#Databases #DataScientists #DataScience #DataManagement #DataProfiling #DataLiteracy #DataQuality
12 Best #SQL Online Course Certificate Programs for #DataScience in 2023 — compiled by @tut_ml
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#Databases #BigData #DataScientists
mltut.com/best-sql-onlin…
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#Databases #BigData #DataScientists
mltut.com/best-sql-onlin…
7 Best Advanced #SQL Courses & Training Online You Must Know in 2023 — compiled by @tut_ml
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#BigData #DataScience #DataScientists #Coding #Database #Analytics
mltut.com/best-advanced-…
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#BigData #DataScience #DataScientists #Coding #Database #Analytics
mltut.com/best-advanced-…
Hello Data Analysts,
Let’s talk about data quality.
Bad data can have a big impact on a company's bottom line.
Poor-quality data is frequently blamed for operational blunders, incorrect analytics, and poorly thought-out company initiatives.
What should be reviewed?
Let’s talk about data quality.
Bad data can have a big impact on a company's bottom line.
Poor-quality data is frequently blamed for operational blunders, incorrect analytics, and poorly thought-out company initiatives.
What should be reviewed?
Organizations can detect data mistakes that need to be fixed and determine whether the data in their IT systems is suitable for the intended use by measuring data quality levels.
1. Check for completeness/uniqueness. Presence of missing data? Are data entries duplicated?
1. Check for completeness/uniqueness. Presence of missing data? Are data entries duplicated?
2. Check for accuracy and consistency. Are the formulas correct and consistent? Are the entered Data accurate?
3. Check for conformance and validity. Do the data meet required specifications?
4. Timeliness. Is it up to date? Is it readily available?
3. Check for conformance and validity. Do the data meet required specifications?
4. Timeliness. Is it up to date? Is it readily available?
Understanding data quality is crucial for reliable ML. In our #ICML2022 paper, @NabeelSeedat01, @JonathanICrabbe & @MihaelaVDS present a Data-Centric framework for the understudied problem of identifying incongruous examples of in-distribution data.
🧵1/10
🧵1/10
TLDR.
*Do you want to know which examples will be reliably predicted, independent of the downstream predictive model?
* Do you want to get insights into your data to understand possible limitations?
If so, Data-SUITE our new #DataCentricAI framework is for you!
2/10
*Do you want to know which examples will be reliably predicted, independent of the downstream predictive model?
* Do you want to get insights into your data to understand possible limitations?
If so, Data-SUITE our new #DataCentricAI framework is for you!
2/10
There has been a significant focus on out-of-distribution data (OOD) for reliable ML.
However, in Data-SUITE we tackle an equally important yet understudied problem.
How do we assess In-Distribution data, with feature space heterogeneity?
3/10
However, in Data-SUITE we tackle an equally important yet understudied problem.
How do we assess In-Distribution data, with feature space heterogeneity?
3/10
Tweet 🧵
1/15
Screening People with Tuberculosis for High Risk of Severe Illness at Notification: Programmatic Experience from Karnataka, India mdpi.com/1151162 in journal #TMID via @MDPIOpenAccess
#OperationalResearch led by @SharathBN and me
1/15
Screening People with Tuberculosis for High Risk of Severe Illness at Notification: Programmatic Experience from Karnataka, India mdpi.com/1151162 in journal #TMID via @MDPIOpenAccess
#OperationalResearch led by @SharathBN and me
2/15
Before #COVID19, #TB was the leading infectious disease killer
People with TB r not systematically screened for severe illness @ diagnosis. Something we do in COVID19
To #EndTBDeaths, @TbDivision recommends assessment of severity @ diagnosis n referral 4 inpatient care
Before #COVID19, #TB was the leading infectious disease killer
People with TB r not systematically screened for severe illness @ diagnosis. Something we do in COVID19
To #EndTBDeaths, @TbDivision recommends assessment of severity @ diagnosis n referral 4 inpatient care
3/15
Existing #TBProgramme guidance to assess severity among people with TB requires clinical capacity and diagnostic and radiology infrastructure
This is usually not present in peripheral health institutes where patients are diagnosed
Existing #TBProgramme guidance to assess severity among people with TB requires clinical capacity and diagnostic and radiology infrastructure
This is usually not present in peripheral health institutes where patients are diagnosed
