Data Analytics and Predictive Analytics: How Technology Fits into the Equation

2019 ◽  
pp. 103-112
Author(s):  
Brian J. Galli ◽  
Gabrielle Muniz
Keyword(s):  
Data Analytics ◽  
Predictive Analytics

Data Analytics and STEM Student Success: The Impact of Predictive Analytics-Informed Academic Advising Among Undeclared First-Year Engineering Students

2018 ◽  
Vol 22 (3) ◽  
pp. 497-521 ◽  
Author(s):  
Yu (April) Chen ◽  
Sylvester Upah
Keyword(s):  
Higher Education ◽  
Student Success ◽  
Academic Advising ◽  
Data Analytics ◽  
Predictive Analytics ◽  
First Year ◽  
Program Of Study ◽  
Science Technology ◽  
Mathematics Student ◽  
And Mathematics

Science, Technology, Engineering, and Mathematics student success is an important topic in higher education research. Recently, the use of data analytics in higher education administration has gain popularity. However, very few studies have examined how data analytics may influence Science, Technology, Engineering, and Mathematics student success. This study took the first step to investigate the influence of using predictive analytics on academic advising in engineering majors. Specifically, we examined the effects of predictive analytics-informed academic advising among undeclared first-year engineering student with regard to changing a major and selecting a program of study. We utilized the propensity score matching technique to compare students who received predictive analytics-informed advising with those who did not. Results indicated that students who received predictive analytics-informed advising were more likely to change a major than their counterparts. No significant effects was detected regarding selecting a program of study. Implications of the findings for policy, practice, and future research were discussed.

scholarly journals Mathematical Issues in Data Science and Applications for Health care

2019 ◽  
Author(s):  
Sitti Zuhaerah Thalhah ◽  
Mohammad Tohir ◽  
Phong Thanh Nguyen ◽  
K. Shankar ◽  
Robbi Rahim
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Decision Models ◽  
Computational Techniques ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Source Data ◽  
Military Applications

For development in military applications, industrial and government the predictive analytics and decision models have long been cornerstones. In modern healthcare system technologies and big data analytics and modeling of multi-source data system play an increasingly important role. Into mathematical models in these domains various problems arising that can be formulated, by using computational techniques, sophisticated optimization and decision analysis it can be analyzed. This paper studies the use of data science in healthcare applications and the mathematical issues in data science.

Robust Data-Driven Machine-Learning Models for Subsurface Applications: Are We There Yet?

2021 ◽  
Vol 73 (03) ◽  
pp. 25-30
Author(s):  
Srikanta Mishra ◽  
Jared Schuetter ◽  
Akhil Datta-Gupta ◽  
Grant Bromhal
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Oil And Gas ◽  
Model Building ◽  
Model Updating ◽  
Predictive Analytics ◽  
Energy Resources ◽  
Data Driven ◽  
Classical Statistics ◽  
Prescriptive Analytics

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.

Business Analytics and Big Data

2015 ◽  
pp. 1-11 ◽  
Author(s):  
Dennis T. Kennedy ◽  
Dennis M. Crossen ◽  
Kathryn A. Szabat
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Quantitative Methods ◽  
Business Processes ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Database Systems ◽  
Three Dimensions ◽  
Business Analytics ◽  
The People

Big Data Analytics has changed the way organizations make decisions, manage business processes, and create new products and services. Business analytics is the use of data, information technology, statistical analysis, and quantitative methods and models to support organizational decision making and problem solving. The main categories of business analytics are descriptive analytics, predictive analytics, and prescriptive analytics. Big Data is data that exceeds the processing capacity of conventional database systems and is typically defined by three dimensions known as the Three V's: Volume, Variety, and Velocity. Big Data brings big challenges. Big Data not only has influenced the analytics that are utilized but also has affected technologies and the people who use them. At the same time Big Data brings challenges, it presents opportunities. Those who embrace Big Data and effective Big Data Analytics as a business imperative can gain competitive advantage.

An Introduction to Data Analytics

2017 ◽  
pp. 1-14 ◽  
Author(s):  
A. Sheik Abdullah ◽  
S. Selvakumar ◽  
A. M. Abirami
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Educational Institutions ◽  
Social Media Analytics ◽  
Text Analytics ◽  
Raw Data ◽  
Industrial Organizations ◽  
Prescriptive Analytics

Data analytics mainly deals with the science of examining and investigating raw data to derive useful patterns and inference. Data analytics has been deployed in many of the industries to make decisions at proper levels. It focuses upon the assumption and evaluation of the method with the intention of deriving a conclusion at various levels. Various types of data analytical techniques such as predictive analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used by industrial organizations, educational institutions and by government associations. This context mainly focuses towards the illustration of contextual examples for various types of analytical techniques and its applications.

Predict and Surveil

2020 ◽  
Author(s):  
Sarah Brayne
Keyword(s):  
Big Data ◽  
Law Enforcement ◽  
Los Angeles ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Civil Liberties ◽  
Big Data Analytics ◽  
Police Department ◽  
Law Enforcement Agencies ◽  
Criminal Suspects

The scope of criminal justice surveillance, from policing to incarceration, has expanded rapidly in recent decades. At the same time, the use of big data has spread across a range of fields, including finance, politics, health, and marketing. While law enforcement’s use of big data is hotly contested, very little is known about how the police actually use it in daily operations and with what consequences. This book offers an inside look at how police use big data and new surveillance technologies, leveraging on-the-ground fieldwork with one of the most technologically advanced law enforcement agencies in the world—the Los Angeles Police Department. Drawing on original interviews and ethnographic observations from over two years of fieldwork with the LAPD, the text examines the causes and consequences of big data and algorithmic control. It reveals how the police use predictive analytics and new surveillance technologies to deploy resources, identify criminal suspects, and conduct investigations; how the adoption of big data analytics transforms police organizational practices; and how the police themselves respond to these new data-driven practices. While big data analytics has the potential to reduce bias, increase efficiency, and improve prediction accuracy, the book argues that it also reproduces and deepens existing patterns of inequality, threatens privacy, and challenges civil liberties.

scholarly journals Predictive privacy: towards an applied ethics of data analytics

2021 ◽  
Author(s):  
Rainer Mühlhoff
Keyword(s):  
Machine Learning ◽  
Data Protection ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Applied Ethics ◽  
Big Data Analytics ◽  
Training Data ◽  
Sensitive Information ◽  
Ethical Implications ◽  
Individual Privacy

AbstractData analytics and data-driven approaches in Machine Learning are now among the most hailed computing technologies in many industrial domains. One major application is predictive analytics, which is used to predict sensitive attributes, future behavior, or cost, risk and utility functions associated with target groups or individuals based on large sets of behavioral and usage data. This paper stresses the severe ethical and data protection implications of predictive analytics if it is used to predict sensitive information about single individuals or treat individuals differently based on the data many unrelated individuals provided. To tackle these concerns in an applied ethics, first, the paper introduces the concept of “predictive privacy” to formulate an ethical principle protecting individuals and groups against differential treatment based on Machine Learning and Big Data analytics. Secondly, it analyses the typical data processing cycle of predictive systems to provide a step-by-step discussion of ethical implications, locating occurrences of predictive privacy violations. Thirdly, the paper sheds light on what is qualitatively new in the way predictive analytics challenges ethical principles such as human dignity and the (liberal) notion of individual privacy. These new challenges arise when predictive systems transform statistical inferences, which provide knowledge about the cohort of training data donors, into individual predictions, thereby crossing what I call the “prediction gap”. Finally, the paper summarizes that data protection in the age of predictive analytics is a collective matter as we face situations where an individual’s (or group’s) privacy is violated using data other individuals provide about themselves, possibly even anonymously.

scholarly journals Application of Data Analytics Principles in Healthcare

2019 ◽  
Vol 8 (2S11) ◽  
pp. 3151-3155
Keyword(s):  
Data Analytics ◽  
Data Science ◽  
Predictive Analytics ◽  
Medical Sciences ◽  
Quality Healthcare ◽  
Healthcare Data ◽  
Adoption And Implementation ◽  
Proposed Model ◽  
Patient Health ◽  
Patients At Risk

Information technology has transformed the healthcare field worldwide. In many areas of the healthcare industry, implementations of data analytics tools are commonly used recently. Applying data analytics principles in medical sciences appropriately transforms the mere storage of medical records in to discovery of drugs. Data science and analytics are essential tools because they can help make better decisions when it comes to spending and reducing inefficiencies in healthcare. The proposed model of healthcare data analytics provides a framework to accelerate the adoption and implementation of predictive analytics in healthcare. Healthcare data analytics can be applied to prove formulated hypotheses, test those using standard analytics models and predict patient health conditions. It can be used to classify patients at risk of developing diseases such as diabetes, asthma, and other life-long illnesses. In spite of the challenges faced while applying data science predictive analytics in the healthcare environment, there is an enormous opportunity for its usage in providing quality healthcare for patients

scholarly journals An Evaluation of Big Data Analytics Projects and the Project Predictive Analytics Approach

2020 ◽  
Vol 12 (4) ◽  
pp. 132-146
Author(s):  
Gabriel Kabanda
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Document Analysis ◽  
Heterogeneous Data ◽  
Discourse Theory ◽  
Mining Machine ◽  
Data Types ◽  
Qualitative Research Methodology

Big Data is the process of managing large volumes of data obtained from several heterogeneous data types e.g. internal, external, structured and unstructured that can be used for collecting and analyzing enterprise data. The purpose of the paper is to conduct an evaluation of Big Data Analytics Projects which discusses why the projects fail and explain why and how the Project Predictive Analytics (PPA) approach may make a difference with respect to the future methods based on data mining, machine learning, and artificial intelligence. A qualitative research methodology was used. The research design was discourse analysis supported by document analysis. Laclau and Mouffe’s discourse theory was the most thoroughly poststructuralist approach.

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