Data Analytics And Learning

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Data Science in Education Using R

Author : Ryan A. Estrellado
Publisher : Routledge
ISBN 13 : 1000200906
Total Pages : 315 pages
Book Rating : 4.0/5 (2 download)

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Book Synopsis Data Science in Education Using R by : Ryan A. Estrellado

Download or read book Data Science in Education Using R written by Ryan A. Estrellado and published by Routledge. This book was released on 2020-10-26 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science in Education Using R is the go-to reference for learning data science in the education field. The book answers questions like: What does a data scientist in education do? How do I get started learning R, the popular open-source statistical programming language? And what does a data analysis project in education look like? If you’re just getting started with R in an education job, this is the book you’ll want with you. This book gets you started with R by teaching the building blocks of programming that you’ll use many times in your career. The book takes a "learn by doing" approach and offers eight analysis walkthroughs that show you a data analysis from start to finish, complete with code for you to practice with. The book finishes with how to get involved in the data science community and how to integrate data science in your education job. This book will be an essential resource for education professionals and researchers looking to increase their data analysis skills as part of their professional and academic development.

Adoption of Data Analytics in Higher Education Learning and Teaching

Author : Dirk Ifenthaler
Publisher : Springer Nature
ISBN 13 : 3030473929
Total Pages : 464 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Adoption of Data Analytics in Higher Education Learning and Teaching by : Dirk Ifenthaler

Download or read book Adoption of Data Analytics in Higher Education Learning and Teaching written by Dirk Ifenthaler and published by Springer Nature. This book was released on 2020-08-10 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to advance global knowledge and practice in applying data science to transform higher education learning and teaching to improve personalization, access and effectiveness of education for all. Currently, higher education institutions and involved stakeholders can derive multiple benefits from educational data mining and learning analytics by using different data analytics strategies to produce summative, real-time, and predictive or prescriptive insights and recommendations. Educational data mining refers to the process of extracting useful information out of a large collection of complex educational datasets while learning analytics emphasizes insights and responses to real-time learning processes based on educational information from digital learning environments, administrative systems, and social platforms. This volume provides insight into the emerging paradigms, frameworks, methods and processes of managing change to better facilitate organizational transformation toward implementation of educational data mining and learning analytics. It features current research exploring the (a) theoretical foundation and empirical evidence of the adoption of learning analytics, (b) technological infrastructure and staff capabilities required, as well as (c) case studies that describe current practices and experiences in the use of data analytics in higher education.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

Big Data and Learning Analytics in Higher Education

Author : Ben Kei Daniel
Publisher : Springer
ISBN 13 : 3319065203
Total Pages : 272 pages
Book Rating : 4.3/5 (19 download)

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Book Synopsis Big Data and Learning Analytics in Higher Education by : Ben Kei Daniel

Download or read book Big Data and Learning Analytics in Higher Education written by Ben Kei Daniel and published by Springer. This book was released on 2016-08-27 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on the uses of big data in the context of higher education. The book describes a wide range of administrative and operational data gathering processes aimed at assessing institutional performance and progress in order to predict future performance, and identifies potential issues related to academic programming, research, teaching and learning. Big data refers to data which is fundamentally too big and complex and moves too fast for the processing capacity of conventional database systems. The value of big data is the ability to identify useful data and turn it into useable information by identifying patterns and deviations from patterns.

Intelligent Systems and Learning Data Analytics in Online Education

Author : Santi Caballé
Publisher : Academic Press
ISBN 13 : 0128231270
Total Pages : 427 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Intelligent Systems and Learning Data Analytics in Online Education by : Santi Caballé

Download or read book Intelligent Systems and Learning Data Analytics in Online Education written by Santi Caballé and published by Academic Press. This book was released on 2021-06-15 with total page 427 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply. Presents the application of innovative AI techniques to collaborative learning activities Offers strategies to provide automatic and effective tutoring to students’ activities Offers methods to collect, analyze and correctly visualize learning data in educational environments

Learning Analytics

Author : John R Mattox II
Publisher : Kogan Page Publishers
ISBN 13 : 0749476362
Total Pages : 256 pages
Book Rating : 4.7/5 (494 download)

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Book Synopsis Learning Analytics by : John R Mattox II

Download or read book Learning Analytics written by John R Mattox II and published by Kogan Page Publishers. This book was released on 2016-09-03 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Faced with organizations that are more dispersed, a workforce that is more diverse and the pressure to reduce costs, CEOs and CFOs are increasingly asking what the return on investment is from training and development programmes. Learning Analytics provides a framework for understanding how to work with learning analytics at an advanced level. It focuses on the questions that training evaluation is intended to answer: is training effective and how can it be improved? It discusses the field of learning analytics, outlining how and why analytics can be useful, and takes the reader through examples of approaches to answering these questions and looks at the valuable role that technology has to play. Even where technological solutions are employed, the HR or learning and development practitioner needs to understand what questions they should be asking of their data to ensure alignment between training and business needs. Learning Analytics enables both senior L&D and HR professionals as well as CEOs and CFOs to see the transformational power that effective analytics has for building a learning organization, and the impacts that this has on performance, talent management, and competitive advantage. It helps learning and development professionals to make the business case for their activities, demonstrating what is truly adding value and where budgets should be spent, and to deliver a credible service to their business by providing metrics based on which sound business decisions can be made.

A Hands-On Introduction to Data Science

Author : Chirag Shah
Publisher : Cambridge University Press
ISBN 13 : 1108472443
Total Pages : 459 pages
Book Rating : 4.1/5 (84 download)

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Book Synopsis A Hands-On Introduction to Data Science by : Chirag Shah

Download or read book A Hands-On Introduction to Data Science written by Chirag Shah and published by Cambridge University Press. This book was released on 2020-04-02 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.

Data Analytics Applications in Education

Author : Jan Vanthienen
Publisher : CRC Press
ISBN 13 : 1351650181
Total Pages : 259 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Data Analytics Applications in Education by : Jan Vanthienen

Download or read book Data Analytics Applications in Education written by Jan Vanthienen and published by CRC Press. This book was released on 2017-09-29 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: The abundance of data and the rise of new quantitative and statistical techniques have created a promising area: data analytics. This combination of a culture of data-driven decision making and techniques to include domain knowledge allows organizations to exploit big data analytics in their evaluation and decision processes. Also, in education and learning, big data analytics is being used to enhance the learning process, to evaluate efficiency, to improve feedback, and to enrich the learning experience. As every step a student takes in the online world can be traced, analyzed, and used, there are plenty of opportunities to improve the learning process of students. First, data analytics techniques can be used to enhance the student’ s learning process by providing real-time feedback, or by enriching the learning experience. Second, data analytics can be used to support the instructor or teacher. Using data analytics, the instructor can better trace, and take targeted actions to improve, the learning process of the student. Third, there are possibilities in using data analytics to measure the performance of instructors. Finally, for policy makers, it is often unclear how schools use their available resources to "produce" outcomes. By combining structured and unstructured data from various sources, data analytics might provide a solution for governments that aim to monitor the performance of schools more closely. Data analytics in education should not be the domain of a single discipline. Economists should discuss the possibilities, issues, and normative questions with a multidisciplinary team of pedagogists, philosophers, computer scientists, and sociologists. By bringing together various disciplines, a more comprehensive answer can be formulated to the challenges ahead. This book starts this discussion by highlighting some economic perspectives on the use of data analytics in education. The book begins a rich, multidisciplinary discussion that may make data analytics in education seem as natural as a teacher in front of a classroom.

Formative Assessment, Learning Data Analytics and Gamification

Author : Santi Caballé
Publisher : Morgan Kaufmann
ISBN 13 : 0128036672
Total Pages : 382 pages
Book Rating : 4.1/5 (28 download)

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Book Synopsis Formative Assessment, Learning Data Analytics and Gamification by : Santi Caballé

Download or read book Formative Assessment, Learning Data Analytics and Gamification written by Santi Caballé and published by Morgan Kaufmann. This book was released on 2016-05-10 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student progress given the explosion of e-learning environments, such as MOOCs and online courses that incorporate activities such as design and modeling. This book shows educators how to effectively garner intelligent data from online educational environments that combine assessment and gamification. This data, when used effectively, can have a positive impact on learning environments and be used for building learner profiles, community building, and as a tactic to create a collaborative team. Using numerous illustrative examples and theoretical and practical results, leading international experts discuss application of automatic techniques for e-assessment of learning activities, methods to collect, analyze, and correctly visualize learning data in educational environments, applications, benefits and challenges of using gamification techniques in academic contexts, and solutions and strategies for increasing student participation and performance. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS Discusses application of automatic techniques for e-assessment of learning activities Presents strategies to provide immediate and useful feedback on students’ activities Provides methods to collect, analyze, and correctly visualize learning data in educational environments Explains the applications, benefits, and challenges of using gamification techniques in academic contexts Offers solutions to increase students’ participation and performance while lowering drop-out rates and retention levels

Edge Learning for Distributed Big Data Analytics

Author : Song Guo
Publisher : Cambridge University Press
ISBN 13 : 1108832377
Total Pages : 231 pages
Book Rating : 4.1/5 (88 download)

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Book Synopsis Edge Learning for Distributed Big Data Analytics by : Song Guo

Download or read book Edge Learning for Distributed Big Data Analytics written by Song Guo and published by Cambridge University Press. This book was released on 2022-02-10 with total page 231 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces fundamental theory, basic and advanced algorithms, and system design issues. Essential reading for experienced researchers and developers, or for those who are just entering the field.

Python Machine Learning for Beginners

Author : Leonard Deep
Publisher :
ISBN 13 : 9781097858309
Total Pages : 236 pages
Book Rating : 4.8/5 (583 download)

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Book Synopsis Python Machine Learning for Beginners by : Leonard Deep

Download or read book Python Machine Learning for Beginners written by Leonard Deep and published by . This book was released on 2019-05-13 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Are you interested to get into the programming world? Do you want to learn and understand Python and Machine Learning? Python Machine Learning for Beginners is the guide for you. Python Machine Learning for Beginners is the ultimate guide for beginners looking to learn and understand how Python programming works. Python Machine Learning for Beginners is split up into easy to learn chapters that will help guide the readers through the early stages of Python programming. It's this thought out and systematic approach to learning which makes Python Machine Learning for Beginners such a sought-after resource for those that want to learn about Python programming and about Machine Learning using an object-oriented programming approach. Inside Python Machine Learning for Beginners you will discover: An introduction to Machine Learning The main concepts of Machine Learning The basics of Python for beginners Machine Learning with Python Data Processing, Analysis, and Visualizations Case studies and much more! Throughout the book, you will learn the basic concepts behind Python programming which is designed to introduce you to Python programming. You will learn about getting started, the keywords and statements, data types and type conversion. Along with different examples, there are also exercises to help ensure that the information sinks in. You will find this book an invaluable tool for starting and mastering Machine Learning using Python. Once you complete Python Machine Learning for Beginners, you will be more than prepared to take on any Python programming. Scroll back up to the top of this page and hit BUY IT NOW to get your copy of Python Machine Learning for Beginners! You won't regret it!

Computational Learning Approaches to Data Analytics in Biomedical Applications

Author : Khalid Al-Jabery
Publisher : Academic Press
ISBN 13 : 0128144831
Total Pages : 312 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Computational Learning Approaches to Data Analytics in Biomedical Applications by : Khalid Al-Jabery

Download or read book Computational Learning Approaches to Data Analytics in Biomedical Applications written by Khalid Al-Jabery and published by Academic Press. This book was released on 2019-11-20 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained. Includes an overview of data analytics in biomedical applications and current challenges Updates on the latest research in supervised learning algorithms and applications, clustering algorithms and cluster validation indices Provides complete coverage of computational and statistical analysis tools for biomedical data analysis Presents hands-on training on the use of Python libraries, MATLAB® tools, WEKA, SAP-HANA and R/Bioconductor

Data Analytics and Psychometrics

Author : Hong Jiao
Publisher : IAP
ISBN 13 : 1641133287
Total Pages : 248 pages
Book Rating : 4.6/5 (411 download)

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Book Synopsis Data Analytics and Psychometrics by : Hong Jiao

Download or read book Data Analytics and Psychometrics written by Hong Jiao and published by IAP. This book was released on 2018-12-01 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: The general theme of this book is to encourage the use of relevant methodology in data mining which is or could be applied to the interplay of education, statistics and computer science to solve psychometric issues and challenges in the new generation of assessments. In addition to item response data, other data collected in the process of assessment and learning will be utilized to help solve psychometric challenges and facilitate learning and other educational applications. Process data include those collected or available for collection during the process of assessment and instructional phase such as responding sequence data, log files, the use of help features, the content of web searches, etc. Some book chapters present the general exploration of process data in large-scale assessment. Further, other chapters also address how to integrate psychometrics and learning analytics in assessment and survey, how to use data mining techniques for security and cheating detection, how to use more assessment results to facilitate student’s learning and guide teacher’s instructional efforts. The book includes both theoretical and methodological presentations that might guide the future in this area, as well as illustrations of efforts to implement big data analytics that might be instructive to those in the field of learning and psychometrics. The context of the effort is diverse, including K-12, higher education, financial planning, and survey utilization. It is hoped that readers can learn from different disciplines, especially those who are specialized in assessment, would be critical to expand the ideas of what we can do with data analytics for informing assessment practices.

Deep Learning for Data Analytics

Author : Himansu Das
Publisher : Academic Press
ISBN 13 : 0128226080
Total Pages : 220 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Deep Learning for Data Analytics by : Himansu Das

Download or read book Deep Learning for Data Analytics written by Himansu Das and published by Academic Press. This book was released on 2020-05-29 with total page 220 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning, a branch of Artificial Intelligence and machine learning, has led to new approaches to solving problems in a variety of domains including data science, data analytics and biomedical engineering. Deep Learning for Data Analytics: Foundations, Biomedical Applications and Challenges provides readers with a focused approach for the design and implementation of deep learning concepts using data analytics techniques in large scale environments. Deep learning algorithms are based on artificial neural network models to cascade multiple layers of nonlinear processing, which aids in feature extraction and learning in supervised and unsupervised ways, including classification and pattern analysis. Deep learning transforms data through a cascade of layers, helping systems analyze and process complex data sets. Deep learning algorithms extract high level complex data and process these complex sets to relatively simpler ideas formulated in the preceding level of the hierarchy. The authors of this book focus on suitable data analytics methods to solve complex real world problems such as medical image recognition, biomedical engineering, and object tracking using deep learning methodologies. The book provides a pragmatic direction for researchers who wish to analyze large volumes of data for business, engineering, and biomedical applications. Deep learning architectures including deep neural networks, recurrent neural networks, and deep belief networks can be used to help resolve problems in applications such as natural language processing, speech recognition, computer vision, bioinoformatics, audio recognition, drug design, and medical image analysis. Presents the latest advances in Deep Learning for data analytics and biomedical engineering applications. Discusses Deep Learning techniques as they are being applied in the real world of biomedical engineering and data science, including Deep Learning networks, deep feature learning, deep learning toolboxes, performance evaluation, Deep Learning optimization, deep auto-encoders, and deep neural networks Provides readers with an introduction to Deep Learning, along with coverage of deep belief networks, convolutional neural networks, Restricted Boltzmann Machines, data analytics basics, enterprise data science, predictive analysis, optimization for Deep Learning, and feature selection using Deep Learning

SQL in 10 Minutes, Sams Teach Yourself

Author : Ben Forta
Publisher : Sams Publishing
ISBN 13 : 013304808X
Total Pages : 287 pages
Book Rating : 4.1/5 (33 download)

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Book Synopsis SQL in 10 Minutes, Sams Teach Yourself by : Ben Forta

Download or read book SQL in 10 Minutes, Sams Teach Yourself written by Ben Forta and published by Sams Publishing. This book was released on 2012-10-25 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sams Teach Yourself SQL in 10 Minutes, Fourth Edition New full-color code examples help you see how SQL statements are structured Whether you're an application developer, database administrator, web application designer, mobile app developer, or Microsoft Office users, a good working knowledge of SQL is an important part of interacting with databases. And Sams Teach Yourself SQL in 10 Minutes offers the straightforward, practical answers you need to help you do your job. Expert trainer and popular author Ben Forta teaches you just the parts of SQL you need to know–starting with simple data retrieval and quickly going on to more complex topics including the use of joins, subqueries, stored procedures, cursors, triggers, and table constraints. You'll learn methodically, systematically, and simply–in 22 short, quick lessons that will each take only 10 minutes or less to complete. With the Fourth Edition of this worldwide bestseller, the book has been thoroughly updated, expanded, and improved. Lessons now cover the latest versions of IBM DB2, Microsoft Access, Microsoft SQL Server, MySQL, Oracle, PostgreSQL, SQLite, MariaDB, and Apache Open Office Base. And new full-color SQL code listings help the beginner clearly see the elements and structure of the language. 10 minutes is all you need to learn how to... Use the major SQL statements Construct complex SQL statements using multiple clauses and operators Retrieve, sort, and format database contents Pinpoint the data you need using a variety of filtering techniques Use aggregate functions to summarize data Join two or more related tables Insert, update, and delete data Create and alter database tables Work with views, stored procedures, and more Table of Contents 1 Understanding SQL 2 Retrieving Data 3 Sorting Retrieved Data 4 Filtering Data 5 Advanced Data Filtering 6 Using Wildcard Filtering 7 Creating Calculated Fields 8 Using Data Manipulation Functions 9 Summarizing Data 10 Grouping Data 11 Working with Subqueries 12 Joining Tables 13 Creating Advanced Joins 14 Combining Queries 15 Inserting Data 16 Updating and Deleting Data 17 Creating and Manipulating Tables 18 Using Views 19 Working with Stored Procedures 20 Managing Transaction Processing 21 Using Cursors 22 Understanding Advanced SQL Features Appendix A: Sample Table Scripts Appendix B: Working in Popular Applications Appendix C : SQL Statement Syntax Appendix D: Using SQL Datatypes Appendix E: SQL Reserved Words

Advanced Data Analytics Using Python

Author : Sayan Mukhopadhyay
Publisher : Apress
ISBN 13 : 1484234502
Total Pages : 195 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Advanced Data Analytics Using Python by : Sayan Mukhopadhyay

Download or read book Advanced Data Analytics Using Python written by Sayan Mukhopadhyay and published by Apress. This book was released on 2018-03-29 with total page 195 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain a broad foundation of advanced data analytics concepts and discover the recent revolution in databases such as Neo4j, Elasticsearch, and MongoDB. This book discusses how to implement ETL techniques including topical crawling, which is applied in domains such as high-frequency algorithmic trading and goal-oriented dialog systems. You’ll also see examples of machine learning concepts such as semi-supervised learning, deep learning, and NLP. Advanced Data Analytics Using Python also covers important traditional data analysis techniques such as time series and principal component analysis. After reading this book you will have experience of every technical aspect of an analytics project. You’ll get to know the concepts using Python code, giving you samples to use in your own projects. What You Will Learn Work with data analysis techniques such as classification, clustering, regression, and forecasting Handle structured and unstructured data, ETL techniques, and different kinds of databases such as Neo4j, Elasticsearch, MongoDB, and MySQL Examine the different big data frameworks, including Hadoop and Spark Discover advanced machine learning concepts such as semi-supervised learning, deep learning, and NLP Who This Book Is For Data scientists and software developers interested in the field of data analytics.

Machine Learning Paradigms

Author : Maria Virvou
Publisher : Springer
ISBN 13 : 3030137430
Total Pages : 223 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Machine Learning Paradigms by : Maria Virvou

Download or read book Machine Learning Paradigms written by Maria Virvou and published by Springer. This book was released on 2019-03-16 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.