WorldCat Identities

Pervaiz, Fahad

Overview
Works: 4 works in 4 publications in 1 language and 7 library holdings
Genres: Academic theses 
Roles: Other, Author
Publication Timeline
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Most widely held works by Fahad Pervaiz
Formulation and evaluation of interpenetrating network of xanthan gum and polyvinylpyrrolidone as a hydrophilic matrix for controlled drug delivery system by Maryam Anwar( )

1 edition published in 2020 in English and held by 2 WorldCat member libraries worldwide

Design and Characterization of PVA-Methacrylic Acid Based Smart Polymeric System for Controlled Release of Metoprolol by Jawad Ahmad Khan( )

1 edition published in 2016 in English and held by 2 WorldCat member libraries worldwide

Understanding challenges in the data pipeline for development data by Fahad Pervaiz( )

1 edition published in 2019 in English and held by 1 WorldCat member library worldwide

The developing world is relying more and more on data driven policies. Numerous development agencies have pushed for on-ground data collection to support the development work they pursue. Many governments have launched efforts for more frequent information gathering. Overall, the amount of data collected is tremendous, yet we face significant issues in doing useful analysis. Most of these barriers are around data cleaning and merging, and they require a data engineer to support some parts of the analysis. This thesis aims to understand the pain points of cleaning development data. It also proposes solutions that harness the thought process of a data engineer to reduce the manual workload of the tedious process of cleaning such data. To achieve these goals, two research areas are critical: (1) to discern current data usage patterns and to build a taxonomy of data cleaning in the developing world; and (2) to create algorithms to support automated data cleaning, which target selected problems including matching transliterated names. With these goals, this thesis will empower regular data users to easily do the necessary data cleaning and scrubbing for analysis
 
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Audience Level
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