Market Analysis: Data Science and Data Analytics
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Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report)
EDUCAUSE CENTER FOR APPLIED RESEARCH Analytics in Higher Education Benefits, Barriers, Progress, and Recommendations EDUCAUSE CENTER FOR APPLIED RESEARCH Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations Contents Executive Summary 3 Key Findings and Recommendations 4 Introduction 5 Defining Analytics 6 Findings 8 Higher Education’s Progress to Date: Analytics Maturity 20 Conclusions 25 Recommendations 26 Methodology 27 Acknowledgments 29 Author Jacqueline Bichsel, EDUCAUSE Center for Applied Research Citation for this work: Bichsel, Jacqueline. Analytics in Higher Education: Benefits, Barriers, Progress, and Recommendations (Research Report). Louisville, CO: EDUCAUSE Center for Applied Research, August 2012, available from http://www.educause.edu/ecar. Analytics in Higher Education Executive Summary Many colleges and universities have demonstrated that analytics can help significantly advance an institution in such strategic areas as resource allocation, student success, and finance. Higher education leaders hear about these transformations occurring at other institutions and wonder how their institutions can initiate or build upon their own analytics programs. Some question whether they have the resources, infrastruc- ture, processes, or data for analytics. Some wonder whether their institutions are on par with others in their analytics endeavors. It is within that context that this study set out to assess the current state of analytics in higher education, outline the challenges and barriers to analytics, and provide a basis for benchmarking progress in analytics. Higher education institutions, for the most part, are collecting more data than ever before. However, this study provides evidence that most of these data are used to satisfy credentialing or reporting requirements rather than to address strategic ques- tions, and much of the data collected are not used at all. -
Professional Master of Computational Data Analytics
A Proposal for a Professional Master’s Degree Program in Computational Data Analytics at New Mexico State University Administered by the College of Arts and Sciences, the College of Business, the College of Engineering, and the Graduate School Las Cruces, New Mexico Contact person who can answer specific questions about the program: Dr. Enrico Pontelli Dr. Huiping Cao Dr. Loui Reyes Dean Associate Professor Dean College of Arts & Sciences Dept. Computer Science The Graduate School New Mexico State Univ. New Mexico State Univ. New Mexico State Univ. [email protected] [email protected] [email protected] (575) 646-3500 (575) 646-4600 (575) 646-5745 1 1 EXECUTIVE SUMMARY ................................................................................................................... 1 2 PURPOSE AND MISSION OF THE PROGRAM ................................................................................... 1 2.1 OVERVIEW OF THE PROPOSED PROGRAM ............................................................................................. 1 2.2 PROGRAM DESCRIPTION ................................................................................................................... 2 2.3 ACADEMIC OBJECTIVES ..................................................................................................................... 3 2.4 PROGRAM CURRICULUM ................................................................................................................... 3 2.4.1 CURRICULUM ...................................................................................................................................... -
Using Data Analytics to Detect Fraud
Using Data Analytics to Detect Fraud Gerard M. Zack, CFE, CPA, CIA, CCEP President, Zack, P.C. Introduction to Data Analytics ®2014 Association of Certified Fraud Examiners, Inc. 1 of 16 ®2014 Association of Certified Fraud Examiners, Inc. Course Objectives . How data analytics can be used to detect fraud . Different tools to perform data analytics . How to walk through the full data analytics process . Red flags of fraud that appear in the data . Data analytics tests that can be used to detect fraud . How to analyze non-numeric data, such as text and timelines, for signs of fraud ®2014 Association of Certified Fraud Examiners, Inc. 2 of 16 Introduction to Data Analytics . Data analytics, as it applies to fraud examination, refers to the use of analytics software to identify trends, patterns, anomalies, and exceptions within data. ®2014 Association of Certified Fraud Examiners, Inc. 3 of 16 Introduction to Data Analytics . Especially useful when fraud is hidden in large data volumes and manual checks are insufficient . Can be used reactively or proactively ®2014 Association of Certified Fraud Examiners, Inc. 4 of 16 Introduction to Data Analytics . Effective data analysis requires: • Translating knowledge of organization and common fraud indicators into analytics tests • Effectively using technological tools • Resolving errors in data output due to incorrect logic or scripts • Applying fraud investigation skills to the data analysis results in order to detect potential instances of fraud ®2014 Association of Certified Fraud Examiners, Inc. 5 of 16 Introduction to Data Analytics . Data analysis techniques alone are unlikely to detect fraud; human judgment is needed to decipher results. -
Google Analytics User Guide
Page | 1 What is Google Analytics? Google Analytics is a cloud-based analytics tool that measures and reports website traffic. It is the most widely used web analytics service on the Internet. Why should we all use it? Google Analytics helps you analyze visitor traffic and paint a complete picture of your audience and their needs. It gives actionable insights into how visitors find and use your site, and how to keep them coming back. In a nutshell, Google Analytics provides information about: • What kind of traffic does your website generate – number of sessions, users and new users • How your users interact with your website & how engaged they are – pages per session, average time spent on the website, bounce rate, how many people click on a specific link, watch a video, time spent on the webpage • What are the most and least interesting pages – landing and exit pages, most and least visited pages • Who visits your website – user`s geo location (i.e. city, state, country), the language they speak, the browser they are using, the screen resolution of their device • What users do once they are on your website – how long do users stay on the website, which page is causing users to leave most often, how many pages on average users view • When users visit your website – date & time of their visits, you can see how the user found you. • Whether visitors came to your website through a search engine (Google, Bing, Yahoo, etc.), social networks (Facebook, Twitter, etc.), a link from another website, or a direct type-in. -
Nine Common Types of Data Mining Techniques Used in Predictive Analytics
1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better understand the variables driving success. Predictive analytics relies on formulas that compare past examples of success and failure, and then use these formulas to predict future outcomes. Predictive analytics, pattern recognition and classification problems are not new. Long used in the financial services and insurance industries, predictive analytics is about using statistics, data mining and game theory to analyze current and historical facts in order to make predictions about future events. The value of predictive analytics is relatively obvious. The more you understand customer behavior and motivations the more effective your marketing. The more you know why some customers are loyal, how to attract and retain different customer segments, the more you can develop relevant compelling messages and offers. Predicting customer buying and product preferences and habits requires an analytical framework that enables you to discover meaningful patterns and relationships within customer data in order to do better message targeting, drive customer value and loyalty. Predictive models have been used in business to assess the risk or potential associated with a particular set of conditions as a way to guide decision making. Predictive models analyze past performance to assess how likely a customer is to exhibit a specific behavior in the future in order to improve marketing effectiveness. Marketing and sales professionals are beginning to capture and analyze many different types of customer data: attitudinal, behavioral, and 2 transactional related to purchasing and product preferences in order to make predictions about future buying behavior. -
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data Review Balancing Plurality and Educational Essence: Higher Education Between Data-Competent Professionals and Data Self-Empowered Citizens Nils Hachmeister * , Katharina Weiß, Juliane Theiß and Reinhold Decker BiCDaS, Bielefeld University, 33501 Bielefeld, Germany; [email protected] (K.W.); [email protected] (J.T.); [email protected] (R.D.) * Correspondence: [email protected] or [email protected]; Tel.: +49-521-106-67599 Abstract: Data are increasingly important in central facets of modern life: academics, professions, and society at large. Educating aspiring minds to meet highest standards in these facets is the mandate of institutions of higher education. This, naturally, includes the preparation for excelling in today’s data-driven world. In recent years, an intensive academic discussion has resulted in the distinction between two different modes of data related education: data science and data literacy education. As a large number of study programs and offers is emerging around the world, data literacy in higher education is a particular focus of this paper. These programs, despite sharing the same name, differ substantially in their educational content, i.e., a high plurality can be observed. This paper explores this plurality, comments on the role it might play and suggests ways it can be dealt with by maintaining a high degree of adaptiveness and plurality while simultaneously establishing a consistent educational “essence”. It identifies a skill set, data self-empowerment, as a potential part of this essence. Data science and literacy education are still experiencing changeability in their Citation: Hachmeister, N.; Weiß, K.; emergence as fields of study, while additionally being stirred up by rapid developments, bringing Theiß, J.; Decker, R. -
Masters Presentation May 2021
www.upc.edu www.upc.edu Master in Artificial Intelligence (MAI) Master in Informatics Engineering (MEI) Master in Informatics Engineering – Industrial Modality Master in Innovation and Research in Informatics (MIRI) Master in Data Science (MDS) Màster per al Professorat de Secundària, Master in Cybersecurity Erasmus Mundus Master in Big Data Management and Analytics More Master Information [email protected] Daniel Jiménez-González Vice-dean of Postgraduates Studies FIB: graduate curricula www.upc.edu Coordinated by FIB: ● Màster en Enginyeria Informàtica (Spanish/Catalan) → MEI ● (NEW) Màster en Enginyeria Informàtica Modalitat Empresa ● Master in Innovation and Research in Informatics (English) → MIRI ● Master in Data Science (English) → MDS ● Interuniversity Master in Artificial Intelligence (English) → MAI Màster de Formació per al Professorat de Secundària (Català/Castellà) Participate: Master’s degree in Cybersecurity (2020/21 - English) Interuniversity Master in Pure and Applied Logics Interuniversity Master in Atomistic and Multiscale Computational Modelling in Physics, Chemistry and Biochemistry ERASMUS MUNDUS: 3 Master Erasmus Mundus in Big Data Management and Analytics Summary Knowledge Areas – www.upc.edu MAI/MIRI/MEI/MDS Computer Networks, MDS: Distributed Data Science, Systems, Big Data Advanced Security Computing, Analytics Management Algorithms Business Artificial Intelligence Computer Graphics, Supercomputing, Virtual and MEI Parallelism, Augmented Reality MAI Microprocessor Design MIRI 4 www.upc.edu I could -
Dimmwitted: a Study of Main-Memory Statistical Analytics
DimmWitted: A Study of MainMemory Statistical Analytics Ce Zhang†‡ Christopher Re´ † †Stanford University ‡University of WisconsinMadison {czhang, chrismre}@cs.stanford.edu ABSTRACT these methods are the core algorithm in systems such as ML- We perform the first study of the tradeoff space of access lib, GraphLab, and Google Brain. Our study examines an- methods and replication to support statistical analytics us- alytics on commodity multi-socket, multi-core, non-uniform ing first-order methods executed in the main memory of a memory access (NUMA) machines, which are the de facto Non-Uniform Memory Access (NUMA) machine. Statistical standard machine configuration and thus a natural target for analytics systems differ from conventional SQL-analytics in an in-depth study. Moreover, our experience with several the amount and types of memory incoherence that they can enterprise companies suggests that, after appropriate pre- tolerate. Our goal is to understand tradeoffs in accessing the processing, a large class of enterprise analytics problems fit data in row- or column-order and at what granularity one into the main memory of a single, modern machine. While should share the model and data for a statistical task. We this architecture has been recently studied for traditional study this new tradeoff space and discover that there are SQL-analytics systems [9], it has not been studied for sta- tradeoffs between hardware and statistical efficiency. We tistical analytics systems. argue that our tradeoff study may provide valuable infor- Statistical analytics systems are different from traditional mation for designers of analytics engines: for each system SQL-analytics systems. -
Data Analytics and Decision-Making in Education: Towards the Educational Data Scientist As a Key Actor in Schools and Higher Education Institutions
1 Data Analytics and Decision-Making in Education: Towards the Educational Data Scientist as a Key Actor in Schools and Higher Education Institutions Tommaso Agasisti Alex J. Bowers Politecnico di Milano School of Management Teachers College, Columbia University [email protected] [email protected] ABSTRACT:1 1990s, school principals, teachers, parents, stakeholders In this chapter, we outline the importance of data usage for and policy-makers started looking at quantitative data as improving policy-making (at the system level), an indispensable source for making decisions, formulating management of educational institutions and pedagogical diagnoses about strengths and weaknesses of institutions, approaches in the classroom. We illustrate how traditional and assessing the effects of initiatives and policies, etc. data analyses are becoming gradually substituted by more (Bowers, 2008; Mandinach, Honey, Light, & Brunner, sophisticated forms of analytics, and we provide a 2008; Wayman, 2005; Wayman, Stringfield, & classification for these recent movements (in particular Yakimowski, 2004). The diffusion of organizations and learning analytics, academic analytics and educational data initiatives such as Education for the Future mining). After having illustrated some examples of recent (http://eff.csuchico.edu) in the USA, or the DELECA – applications, we warn against potential risks of inadequate Developing Leadership Capacity for data-informed school analytics in education, and list a number of barriers that improvement project (http://www.deleca.org) in Europe, impede the widespread application of better data use. As give testimony to a growing attention to the clever, implications, we call for a development of a more robust intensive and informed use of data for improving schools’ professional role of data scientists applied to education, activities and results. -
Data Mining and Big Data Analytics (2 Credits)
Course: Data mining and Big Data analytics (2 credits) Instructors: Fosca Giannotti and Dino Pedreschi Learning goals The course provides an introduction to data mining and knowledge discovery from data, i.e., the analytical process of extracting useful information from large raw data generated and collected by various ICTs. The key data mining methods of clustering, classification and pattern mining are illustrated, together with practical tools for their execution. Next, we focus on Big Data, the digital traces of human activities at societal scale, which provide a powerful social microscope that together with social mining – the methods for discovering knowledge from these data – can help us understand and forecast many complex socio-economic phenomena. The key methods for Big Data sensing and acquisition are discussed, together with basics of social media mining and sentiment analysis, mobility data mining, social network mining, retail market data mining, and sport data mining. We conclude with an introduction to big data visualization. Syllabus 1. Data mining and the knowledge discovery process. Overview of data mining and machine learning techniques. Exemplar case studies in clustering, classification and pattern mining. 05.10.2015 (Dino & Fosca) 2. Data. Data types and formats. Exploratory data analysis and data understanding. Visual data exploration with KNIME. Data preparation. 06.10.2015 (Dino) 3. Clustering. Taxonomy of clustering concepts: distance-based (separation, centroids, contiguity), density-based, partitional vs. hierarchical. Methods for centroid-based clustering (k-means), hierarchical clustering (single, complete and average linkage), density-based clustering (DBSCAN). Practical clustering with KNIME. 07.10.2015 (Dino) 4. Classification and prediction models. Model learning and model validation. -
Inspired to Teach: PERSPECTIVES from NEW FACULTY BRYANT UNIVERSITY MISSION
Inspired to Teach: PERSPECTIVES FROM NEW FACULTY BRYANT UNIVERSITY MISSION Educate and inspire students to discover their passion and become innovative leaders with character around the world. At Bryant, our dedication to student learning is at the heart of our mission. We are proud to be known for our strong commitment to undergraduate teaching and our continued investments in exciting approaches to education. In particular, over the last two years, we’ve hired 29 new faculty whose credentials and passion for teaching are second to none. This next generation of Bryant academics are accomplished scholars who have worked and studied at top institutions such as Yale, Notre Dame, Penn, Auburn, Duke, the U.S. Military Academy at West Point, U.C. Berkeley, and the London School of Economics. They’re energized by the opportunity to contribute to Bryant’s mission. Individually and collectively, they are the future of Bryant. We are proud to introduce you to this group of talented scholars, teachers, and leaders. Glenn M. Sulmasy Provost and Chief Academic Officer 2 RENOWNED AUTHORITIES Richard Gorvett, FCAS, CERA, James Shanahan, Ph.D. MAAA, ARM, FRM, Ph.D. Jeanne & John Rowe Chair of Mathematics Professor of Data Science, Department and Professor Associate Professor of of Mathematics Information Systems and Analytics, and Executive Director BEFORE BRYANT: Staff Actuary, of the Data Science Initiative Casualty Actuarial Society (CAS), Arlington, VA, where he was also a BEFORE BRYANT: Founding faculty member of The Automated Vehicles Task member -
Evaluating Business Intelligence / Business Analytics Software for Use in the Information Systems Curriculum
Information Systems Education Journal (ISEDJ) 13 (1) ISSN: 1545-679X January 2015 Evaluating Business Intelligence / Business Analytics Software for Use in the Information Systems Curriculum Gary Alan Davis [email protected] Charles R. Woratschek [email protected] Computer and Information Systems Robert Morris University Moon Township, PA 15108 USA Abstract Business Intelligence (BI) and Business Analytics (BA) Software has been included in many Information Systems (IS) curricula. This study surveyed current and past undergraduate and graduate students to evaluate various BI/BA tools. Specifically, this study compared several software tools from two of the major software providers in the BI/BA field. The participants in the study evaluated each software tool according to three key criteria: 1) functionality, 2) ease of use, and 3) learning effectiveness. The “learning effectiveness” criterion was used to determine which BI/BA tools provided the most effective learning of BI/BA concepts in the IS classroom. The three criteria were used to develop recommendations for including specific BI/BA software tools in the IS curriculum. Based on the findings of the study, the authors recommend that colleges and universities consider the use of the IBM-Cognos suite of tools as a viable means for teaching BI/BA concepts in their Information Systems curricula. The results of the study are relevant to any college or university that currently includes (or is considering the inclusion of) Business Intelligence / Business Analytics concepts in its Information Systems curriculum. Keywords: Business Intelligence, Business Analytics, Software Evaluation, Information Systems Curriculum 1. INTRODUCTION students who are attending/have attended BI/BA-related courses.