Big Data Implementasi Hadoop Mapreduce Pada Pemetaan Sekolah Menggunakan Python

BIG DATA: IMPLEMENTASI HADOOP MAPREDUCE PADA PEMETAAN SEKOLAH MENGGUNAKAN PYTHON

Telah hadir buku “Big Data: Implementasi Hadoop MapReduce pada Pemetaan Sekolah Menggunakan Python”. Buku dengan total 237 halaman ini akan membahas terkait Big Data seperti gambaran umum terkait Big Data itu bagaimana, pengenalan tentang Apache Hadoop, pembahasan lebih detail HDFS dan MapReduce, pengenalan tentang bahasa pemrograman Python dan bagaimana sebuah algoritma itu bekerja serta algoritma yang akan digunakan pada studi kasus yang ada. Selanjutnya, buku ini membahas tentang contoh implementasi konsep Big Data dimana pada kasus yangdigunakan adalah proses melakukan pemetaan sekolah menggunakan bahasa pemrograman Python. Dimulai pada implementasinya menggunakan MapReduce sebagai salah satu tools pengolahan yang terdapat dalam Apache Hadoop yang diterapkan pada VirtualBox. Dimana proses pemetaan sekolah ini seperti pemetaan berdasarkan provinsi, wilayah serta sekolah serta memberikan rekomendasi-rekomendasi sekolah-sekolah terdekat dari sekolah yang akan dijadikan tujuan utama. Diharapkan dengan adanya buku ini, bisa memberikan gambaran umum terkait penerapan konsep Big Data itu sendiri bagaimana dalam kehidupan sehari-hari. Serta dengan dilakukan pemetaan sekolah dalam proses penerimaan mahasiswa baru untuk membantu strategi marketing.

Introduction to Data Science

Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.

Theory of Adoption