Sentences with phrase «data at every step in the process»

Not exact matches

The project is detailed in the contract as a seven step process — with Kogan's company, GSR, generating an initial seed sample (though it does not specify how large this is here) using «online panels»; analyzing this seed training data using its own «psychometric inventories» to try to determine personality categories; the next step is Kogan's personality quiz app being deployed on Facebook to gather the full dataset from respondents and also to scrape a subset of data from their Facebook friends (here it notes: «upon consent of the respondent, the GS Technology scrapes and retains the respondent's Facebook profile and a quantity of data on that respondent's Facebook friends»); step 4 involves the psychometric data from the seed sample, plus the Facebook profile data and friend data all being run through proprietary modeling algorithms — which the contract specifies are based on using Facebook likes to predict personality scores, with the stated aim of predicting the «psychological, dispositional and / or attitudinal facets of each Facebook record»; this then generates a series of scores per Facebook profile; step 6 is to match these psychometrically scored profiles with voter record data held by SCL — with the goal of matching (and thus scoring) at least 2M voter records for targeting voters across the 11 states; the final step is for matched records to be returned to SCL, which would then be in a position to craft messages to voters based on their modeled psychometric scores.

Furthermore, take a look at Jey Pandian's post, in which he details a 10 - step process for using search data to build more specific and practical buyer personas.

«Quantifying the sulfur dioxide bull's - eyes is a two - step process that would not have been possible without two innovations in working with the satellite data,» said co-author Nickolay Krotkov, an atmospheric scientist at NASA's Goddard Space Flight Center in Greenbelt, Maryland.

Any results that are reported to constitute a blinded, independent validation of a statistical model (or mathematical classifier or predictor) must be accompanied by a detailed explanation that includes: 1) specification of the exact «locked down» form of the model, including all data processing steps, algorithm for calculating the model output, and any cutpoints that might be applied to the model output for final classification, 2) date on which the model or predictor was fully locked down in exactly the form described, 3) name of the individual (s) who maintained the blinded data and oversaw the evaluation (e.g., honest broker), 4) statement of assurance that no modifications, additions, or exclusion were made to the validation data set from the point at which the model was locked down and that neither the validation data nor any subset of it had ever been used to assess or refine the model being tested

Some might see this move to be a step backwards, but an essential one, although if you were to look at it in a positive manner, at least it helps streamline the process, and if you were to go through the entire migration process without losing any data or notice anything amiss, it should not matter too much to the end user in the long run, right?

I realize this is more of an engineering scenario, but we meticulously archive all of our code and data using an advanced version control system so that every step in the process can be revisited and evaluated at anytime there is a question about how we got to our final results.

processing is necessary for the performance of a contract to which the data subject is party or in order to take steps at the request of the data subject prior to entering into a contract.

processing is necessary for the performance of a contract to which the data subject is party or in order to take steps at the...

He has also undertaken training on lean methodologies, so he is adept at identifying waste in law firm / legal team processes, and re-designing those processes to trim out unnecessary steps and reduce complexity in legal data systems.

Besides the payment term, the Consent Decree includes provisions requiring Brown & Brown to: take affirmative steps to avoid pregnancy discrimination in the future; create and adopt a pregnancy discrimination policy (to be submitted for approval to the EEOC); distribute copies to every employee and manager, and to every applicant; provide two hours of in - person training on gender discrimination, including pregnancy discrimination, to every manager involved in the hiring process; retain, at the company's cost, a «subject matter expert» (to be agreed upon by the EEC) on sex discrimination to conduct those sessions; provide to non-managers one hour of video or webinar training on the same topic (s); make yearly reports to the EEOC for two years regarding further complaints of pregnancy discrimination, if any; post a Notice of the consent decree at the facility; and retain all documents and data related to compliance with the Consent Decree.

The project is detailed in the contract as a seven step process — with Kogan's company, GSR, generating an initial seed sample (though it does not specify how large this is here) using «online panels»; analyzing this seed training data using its own «psychometric inventories» to try to determine personality categories; the next step is Kogan's personality quiz app being deployed on Facebook to gather the full dataset from respondents and also to scrape a subset of data from their Facebook friends (here it notes: «upon consent of the respondent, the GS Technology scrapes and retains the respondent's Facebook profile and a quantity of data on that respondent's Facebook friends»); step 4 involves the psychometric data from the seed sample, plus the Facebook profile data and friend data all being run through proprietary modeling algorithms — which the contract specifies are based on using Facebook likes to predict personality scores, with the stated aim of predicting the «psychological, dispositional and / or attitudinal facets of each Facebook record»; this then generates a series of scores per Facebook profile; step 6 is to match these psychometrically scored profiles with voter record data held by SCL — with the goal of matching (and thus scoring) at least 2M voter records for targeting voters across the 11 states; the final step is for matched records to be returned to SCL, which would then be in a position to craft messages to voters based on their modeled psychometric scores.