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"This book is a comprehensive introduction to the methods and algorithms and approaches of modern data analytics. It covers data preprocessing, visualization, correlation, regression, forecasting, classification, and clustering. It provides a sound mathematical basis, discusses advantages and drawbacks of different approaches, and enables the reader to design and implement data analytics solutions for real-world applications. Much of the content is based on the results of industrial research and development projects at Siemens."

"Penelitian ini merupakan studi kasus yang dilakukan pada Lembaga X atas penerapan rerangka tata kelola data berdasarkan Data Management Body of Knowledge (DMBOK) yang diterbitkan oleh The Data Management Association pada tahun 2017. Tujuan penelitian ini adalah untuk menganalisis permasalahan pengelolaan data sektor jasa keuangan (SJK) terintegrasi dan memberikan rekomendasi perbaikan program tata kelola data dalam mendukung tugas dan fungsi Lembaga X. Penelitian menggunakan pendekatan kualitatif dalam mendeskripsikan rerangka tata kelola data SJK terintegrasi berdasarkan aktivitas tata kelola data dalam DMBOK. Instrumen penelitian yang digunakan berupa interviu, kuesioner, dan analisis konten dari beberapa dokumen yang dikumpulkan. Hasil penelitian menunjukkan bahwa terdapat permasalahan terhadap pengelolaan data SJK terintegrasi yang disebabkan oleh faktor-faktor terkait tata kelola data: peraturan, proses operasional, sumber daya manusia, dan teknologi. Lembaga X dapat menggunakan pedoman tata kelola data berdasarkan DMBOK dalam mengatasi permasalahan atas pengelolaan data SJK terintegrasi. Secara keseluruhan, program tata kelola data yang dibangun oleh Lembaga X masih memerlukan perbaikan pada aktivitas tata kelola data: perencanaan, operasional, dan pengendalian. Saran perbaikan program tata kelola data SJK terintegrasi pada Lembaga X adalah pembuatan dan penetapan piagam tata kelola data, penyesuaian roadmap, penilaian tingkat kematangan kapabilitas pengelolaan data secara teratur, pendefinisian rerangka operasional tata kelola data, pembentukan tim manajemen perubahan, pembuatan mekanisme dan prosedur penanganan permasalahan data, penyelesaian pembuatan aturan pengelolaan data, pengembangan tools dan teknik yang mendukung keseluruhan program tata kelola data, serta pengembangan matriks pengelolaan data SJK terintegrasi

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This research is a case study conducted at the Institution X on the application of a data governance framework based on the Data Management Body of Knowledge (DMBOK) published by The Data Management Association in 2017. The purpose of this research is to analyze problems with the integrated financial services sector (FSS) data management and provide recommendations for improving data governance programs in support of Institution X's duties and functions. This study used a qualitative approach in describing the integrated FSS data governance framework based on data governance activities in the DMBOK. The research instruments used were interviews, questionnaires, and content analysis of several documents collected. The results showed that there were problems with the integrated FSS data management caused by factors related to data governance: regulations, operational processes, human resources, and technology. Institution X can use data governance guidelines based on DMBOK in overcoming problems with integrated FSS data management. Overall, the data governance program developed by Institution X still requires improvements in data governance activities: planning, operational, and control stages. Suggestions for improving the integrated FSS data governance program at the Institution X are the creation and establishment of a data governance charter, roadmap adjustments, regular assessment of data management capability maturity levels, defining data governance operational frameworks, forming a change management team, establishing mechanisms and procedures for handling data problems, completing data management rules, developing tools and techniques that support the overall data management program, and developing an integrated FSS data governance matrix."

"One of the data integration methods is data consolidation. This method captures data from multiple source systems/data and integrates it into a single persistent data. We examined the performance of data consolidation using k-means and Gaussian mixture clustering. Meanwhile, we use Silhouette index as cluster validation and measure how well of a clustering relative to others. The experiments analyses the data in various data duplication rate and actual number of data cluster. Based on the experimental result, there are two factors affecting the performance of data consolidation. These factors are the rate/percentage of duplicate data and the number of actual cluster contained in a data. The higher percentages of duplicate data and less number of clusters contained in the data would be increasing the performance of clustering algorithm.Salah satu metode dari integrasi data adalah konsolidasi data. Metode ini mengambil data dari beberapa sumber data untuk digabungkan menjadi data persisten tunggal. Peneliti memeriksa kinerja konsolidasi data menggunakan beberapa teknik clustering yaitu k-means dan gaussian mixture clustering. Penulis menggunakan Silhouette index sebagai metode validasi cluster untuk mengukur seberapa baik suatu pengelompokan relatif terhadap data lain. Penelitian ini melakukan analisis data terhadap jumlah rata-rata duplikasi data dan jumlah sebenarnya dari cluster data. Berdasarkan hasil percobaan, ada dua faktor yang mempengaruhi kinerja integrasi data dengan menggunakan konsolidasi data. Faktor-faktor tersebut antara lain adalah tingkat atau persentase dari duplikasi data dan jumlah cluster sebenarnya yang terkandung dalam data. Persentase duplikasi data yang tinggi dan data yang mengandung jumlah cluster yang rendah, akan meningkatkan kinerja dari algoritma clustering."

"The ability to create automated algorithms to process gridded spatial data is increasingly important as remotely sensed datasets increase in volume and frequency. Whether in business, social science, ecology, meteorology or urban planning, the ability to create automated applications to analyze and detect patterns in geospatial data is increasingly important. This book provides students with a foundation in topics of digital image processing and data mining as applied to geospatial datasets. "

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ABSTRACTDoing data science is difficult. Projects are typically very dynamic with requirements that change as data understanding grows. The data itself arrives piecemeal, is added to, replaced, contains undiscovered flaws and comes from a variety of sources. Teams also have mixed skill sets and tooling is often limited. Despite these disruptions, a data science team must get off the ground fast and begin demonstrating value with traceable, tested work products. This is when you need Guerrilla Analytics. n this book, you will learn about:The Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reporting.Reproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutiny.Practice tips and war stories 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and research.Preparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventions.Data gymnastics: over a dozen analytics patterns that your team will encounter again and again in projectsThe Guerrilla Analytics Principles: simple rules of thumb for maintaining data provenance across the entire analytics life cycle from data extraction, through analysis to reportingReproducible, traceable analytics: how to design and implement work products that are reproducible, testable and stand up to external scrutinyPractice tips and war stories: 90 practice tips and 16 war stories based on real-world project challenges encountered in consulting, pre-sales and researchPreparing for battle: how to set up your team's analytics environment in terms of tooling, skill sets, workflows and conventionsData gymnastics: over a dozen analytics patterns that your team will encounter again and again in projects."

"ABSTRAKPeraturan Menteri Komunikasi dan Informatika Republik Indonesia Nomor 20 Tahun 2016 mengenai perlindungan data pribadi dalam sistem elektronik memiliki tujuan untuk melindungi data pribadi milik warga negara Indonesia. Peraturan ini dibentuk sebagai akibat dari pesatnya perkembangan teknologi dan juga besarnya resiko yang dihadapi dari penggunaan data pribadi dan juga dari penyimpanan data pribadi itu sendiri dalam jumlah yang besar di suatu media penyimpanan. Perlindungan terhadap kerahasiaan data di Indonesia sudah dibahas dan diatur di dalam peraturan tersebut. Namun, apakah perlindungan tersebut sudah dapat dikatakan cukup baik dalam memenuhi tujuannya? Oleh karena itu, akan dilakukan analisa terhadapnya yang didasari atas standarisasi yang sudah diakui secara internasional dalam tulisan ini.

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ABSTRACT

Ministry of Communication and Informatics Republic of Indonesia Regulation Number 20 Year 2016 about private data protection in electronic systems intended to protect private data owned by Indonesian citizens. This regulation was made becasue of quick and rapid development of technology and the amount of risks faced by usage and safekeeping of big amount private data in a certain storage media. Protection of data confidentiality in Indonesia already regulated through that regulation. But, is the protection already sufficient enough to fulfill its goals? Therefore, analysis will be made based on a standarization that is already recognized and approved internationally."