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GEMAR BELAJAR AGAMA KRISTEN – Jilid 2

Buku Penunjang Pembelajaran Pendidikan Agama Kristen

Dunia pendidikan tak pernah luput dari perhatian setiap insane manusia karena ujung tombak pembangunan sebuah bangsa terletak dalam kerangka pendidikan bangsa tersebut. Pembangunan pendidikan suatu bangsa yang mengalami perkembangan kurang begitu signifikan tidak dapat menyeimbangkan laju pertumbuhan global yang sangat pesat sehingga bangsa tersebut menjadi tertinggal. Menyadari sangat besar peranan pendidikan dalam pembangunan suatu bangsa, penulis berupaya mengambil bagian tersebut. Sebab penulis sadar sebagai nara didik memiliki andil besar dalam menumbuhkan kecerdasan anak banga. Setiap ide pemikiran yang penulis miliki telah dituangkan dalam bahan ajar ‘Gemar Belajar Agama Kristen.’ Bahan ajar ini berupaya menuntun serta membimbing peserta didik bukan hanya kaya pengetahuan keagamaan secara kognitif semata melainkan kekayaan tersebut dalam segi afektif maupun psikomotorik.

Dunia pendidikan tak pernah luput dari perhatian setiap insane manusia karena ujung tombak pembangunan sebuah bangsa terletak dalam kerangka pendidikan bangsa tersebut.

Data Mining and Data Warehousing

Principles and Practical Techniques

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Provides a comprehensive textbook covering theory and practical examples for a course on data mining and data warehousing.

Data Mining for Business Analytics

Concepts, Techniques and Applications in Python

Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python presents an applied approach to data mining concepts and methods, using Python software for illustration Readers will learn how to implement a variety of popular data mining algorithms in Python (a free and open-source software) to tackle business problems and opportunities. This is the sixth version of this successful text, and the first using Python. It covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, recommender systems, clustering, text mining and network analysis. It also includes: A new co-author, Peter Gedeck, who brings both experience teaching business analytics courses using Python, and expertise in the application of machine learning methods to the drug-discovery process A new section on ethical issues in data mining Updates and new material based on feedback from instructors teaching MBA, undergraduate, diploma and executive courses, and from their students More than a dozen case studies demonstrating applications for the data mining techniques described End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented A companion website with more than two dozen data sets, and instructor materials including exercise solutions, PowerPoint slides, and case solutions Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python is an ideal textbook for graduate and upper-undergraduate level courses in data mining, predictive analytics, and business analytics. This new edition is also an excellent reference for analysts, researchers, and practitioners working with quantitative methods in the fields of business, finance, marketing, computer science, and information technology. “This book has by far the most comprehensive review of business analytics methods that I have ever seen, covering everything from classical approaches such as linear and logistic regression, through to modern methods like neural networks, bagging and boosting, and even much more business specific procedures such as social network analysis and text mining. If not the bible, it is at the least a definitive manual on the subject.” —Gareth M. James, University of Southern California and co-author (with Witten, Hastie and Tibshirani) of the best-selling book An Introduction to Statistical Learning, with Applications in R

This is the sixth version of this successful text, and the first using Python.

Introduction to Data Mining

Introduction to Data Mining, Second Edition, is intended for use in the Data Mining course. It is also suitable for individuals seeking an introduction to data mining. The text assumes only a modest statistics or mathematics background, and no database knowledge is needed. Introduction to Data Mining presents fundamental concepts and algorithms for those learning data mining for the first time. Each concept is explored thoroughly and supported with numerous examples. The text requires only a modest background in mathematics. Each major topic is organized into two chapters, beginning with basic concepts that provide necessary background for understanding each data mining technique, followed by more advanced concepts and algorithms. Teaching and Learning Experience This program will provide a better teaching and learning experience-for you and your students. It will help: Present Fundamental Concepts and Algorithms: Written for the beginner, this text provides both theoretical and practical coverage of all data mining topics. Support Learning: Instructor resources include solutions for exercises and a complete set of lecture slides.

This edition improves on the first iteration of the book, published over a decade ago, by addressing the significant changes in the industry as a result of advanced technology and data growth.