data quality


Approaching Machine learning problem – Bhushan Shewale – Medium

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An average data scientist deal with lots of data daily, around 60–70% time spend on data cleaning, data munging and convert the data into suitable form so that we can apply machine learning model on that data. This blog focuses on applying machine learning models, including the preprocessing steps. Many Data science enthusiast ask me how to solve machine learning problem? Before applying the machine learning models, the data must be converted to a tabular form. There is two types of data Numerical variable and Categorical variable.


How to tackle common data cleaning issues in R

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This tutorial is an excerpt from the book, Statistics for Data Science written by James D. Miller and published by Packt Publishing. R is a language and environment that is easy to learn, very flexible in nature, and very focused on statistical computing, making it a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. Editor's Note: While the author has named the example data as'Gamming Data', it is simply the gaming data that he uses to demonstrate his code. The simplest explanation for what outliers are might be is to say that outliers are those data points that just don't fit the rest of your data. Upon observance, any data that is either very high, very low, or just unusual (within the context of your project), is an outlier.


Graphic Art Recordings and Data Management Education at Enterprise Data World 2018 - DATAVERSITY

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The first panel depicts a collection of talks by Doug Pontious of Amerisure titled Cultivating an Analytics-Driven Culture to Ensure Successful Insight Generation, and Jacob Ablowitz and William Hickson at dmi.io titled "What's My Data Worth?" Pontious' session discussed some best practices learned at Amerisure as they unified many different data sources into an enterprise repository. Ablowitz and Hickson's presentation covered the fundamentals of commercializing data. Bradley A. Rhine of Fulton Financial Corporation and Kristin M. Love of GSK – Not the Return on Investment: Alternatives to Measuring Your Data Integration Strategy; Peter Haynes Aiken at Data Blueprint, Ed Kelly at the State of Texas, Jeffrey Kriseman at the State of Tennessee, and Michael Leahy at the State of Maryland – Challenges Facing the "First" State CDO (Not Initially Different from the Private Sector); JG Cowper of Healthbridge – How Prescribing "Data Glasses" to Eye Surgeons Is Transforming How They See Their Industry; Michael Scofield of Loma Linda University – Good Data, Bad Information – Why the Disconnect; and, Ian Rowlands of ASG Technologies – Data for Everyone: A Changing Data World. Cathy S Normand of ExxonMobil – Making Metadata Valuable – ExxonMobil's Journey Collecting and Cataloging Metadata; Lori Hurley and Denise Janci at Allstate – Divergent Approaches to Metadata Management: Lessons Learned; Ron Klein at Klein Admonition – Deriving New Business Terms from Technical Metadata; Liju Fan of OFR – Semantic Metadata Management: Leveraging Intuitive Ontologies Developed with Best Practices; David N Plotkin of MUFG – Metadata Quality: Ignore at Your Own Risk!; and, Susan Swanson at HCSC – Leveraging the Enterprise Metadata Repository for Data Governance Oversight and Data Quality Monitoring.


Pandas Data Cleaning and Modeling with Python LiveLessons

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In Pandas Data Cleaning and Modeling with Python LiveLessons, Daniel Y. Chen builds upon the foundation he built in Pandas Data Analysis with Python Fundamentals LiveLessons. In this LiveLesson Dan teaches you the techniques and skills you need to know to be able to clean and process your data. Dan shows you how to do data munging using some of the built-in Python libraries that can be used to clean data loaded into Pandas. Once your data is clean you are going to want to analyze it, so next Dan introduces you to other libraries that are used for model fitting. Daniel Y. Chen is a graduate student in the interdisciplinary Ph.D. program in Genetics, Bioinformatics & Computational Biology (GBCB) at Virginia Tech.


Six Core Aspects of Semantic AI

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Hybrid approach: Semantic AI is the combination of methods derived from symbolic AI and statistical AI. Virtuously playing the AI piano means that for a given use case various stakeholders, not only data scientists, but also process owners or subject matter experts, choose from available methods and tools, and collaboratively develop workflows that are most likely a good fit to tackle the underlying problem. For example, one can combine entity extraction based on machine learning with text mining methods based on semantic knowledge graphs and related reasoning capabilities to achieve the optimal results. Data Quality: Semantically enriched data serves as a basis for better data quality and provides more options for feature extraction. This results in higher precision of prediction & classification calculated by machine learning algorithms.


How machine learning streamlines location data with the Kalman filter - IoT Agenda

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We have spoken about machine learning and the internet of things as tools to optimize location analytics in logistics and supply chain management. It's an accepted fact that technology, especially cloud-based, can benefit companies by optimizing routes and predicting the accurate estimated time of arrivals (ETAs). The direct business value of this optimization lies in the streamlining of various fixed and variable costs associated with logistics. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers. In this handbook, learn how to meet the security, analytics, and testing requirements for IoT applications.


Data Quality Evolution with Big Data and Machine Learning Transforming Data with Intelligence

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When big data is combined with machine learning, enterprises must be alert to new data quality issues. IT departments have been struggling with data quality issues for decades, and satisfactory solutions have been found for ensuring quality in structured data warehouses. However, big data solutions, unstructured data, and machine learning are creating new types of quality issues that must be addressed. Big data affects quality because its defining features of volume, variety, and velocity make verification difficult. The elusive "fourth V," the veracity component (concerning data reliability), is challenging due to the large number of data sources that might be brought together, each of which might be subject to different quality problems.



Artificial Intelligence and Bad Data – Towards Data Science

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Facebook, Google, and twitter lawyers gave testimony to congress on how they missed the Russian influence campaign. Even though the ads were bought in Russian currency on platforms chalk full of analytics engines, the problematic nature of the influence campaign went undetected. "Rubles US politics" did not trigger an alert, because the nature of off-the-shelf deep learning is that it only looks for what it knows to look for, and on a deeper level, it is learning from really messy (unstructured) or corrupted and biased data. Understanding the unstructured nature of public data (mixed with private data) is improving by leaps and bounds every day. That's one of the main things I work on.


Machine learning can't save your bad data

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Most sales-driven organizations have needed a customer retention model at some point or another. The request is fairly straightforward: identify the customers that a business might lose. But the process can create a nightmare. They may not truly understand what kind of data they've collected or how to create a narrative from the information on hand. Even worse, many CMOs believe they're looking at a complete view of their customers, only to learn after countless working hours, the results aren't all that helpful.