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Author: Ryan A. Estrellado Publisher: Routledge ISBN: 1000200906 Category : Education Languages : en Pages : 331
Book Description
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.
Author: Ryan A. Estrellado Publisher: Routledge ISBN: 1000200906 Category : Education Languages : en Pages : 331
Book Description
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.
Author: Dirk Ifenthaler Publisher: Springer Nature ISBN: 3030473929 Category : Education Languages : en Pages : 464
Book Description
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.
Author: John R Mattox II Publisher: Kogan Page Publishers ISBN: 0749476362 Category : Business & Economics Languages : en Pages : 256
Book Description
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.
Author: John D. Kelleher Publisher: MIT Press ISBN: 0262361108 Category : Computers Languages : en Pages : 853
Book Description
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.
Author: Chirag Shah Publisher: Cambridge University Press ISBN: 1108472443 Category : Business & Economics Languages : en Pages : 459
Book Description
An introductory textbook offering a low barrier entry to data science; the hands-on approach will appeal to students from a range of disciplines.
Author: Ben Kei Daniel Publisher: Springer ISBN: 3319065203 Category : Education Languages : en Pages : 287
Book Description
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.
Author: Himansu Das Publisher: Academic Press ISBN: 0128197641 Category : Science Languages : en Pages : 218
Book Description
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.
Author: Santi Caballé Publisher: Morgan Kaufmann ISBN: 0128036672 Category : Education Languages : en Pages : 384
Book Description
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. - 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
Author: Gordon S. Linoff Publisher: John Wiley & Sons ISBN: 0470952520 Category : Computers Languages : en Pages : 698
Book Description
Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.
Author: Debi Prasanna Acharjya Publisher: Springer Nature ISBN: 3030758559 Category : Technology & Engineering Languages : en Pages : 271
Book Description
This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Author: Ryan A. Estrellado
Author: Ryan A. Estrellado
Author: Dirk Ifenthaler
Author: John R Mattox II
Author: John D. Kelleher
Author: Chirag Shah
Author: Ben Kei Daniel
Author: Himansu Das
Author: Santi Caballé
Author: Gordon S. Linoff
Author: Debi Prasanna Acharjya