Data Mining Training in Indonesia

Waikato Environment for Knowledge Analysis (Weka) adalah perangkat lunak visualisasi perlombongan data sumber terbuka. Ini menyediakan koleksi algoritma pembelajaran mesin untuk persiapan data, klasifikasi, kluster, dan kegiatan perlombongan data lainnya. Pelatihan yang dipimpin oleh instruktur, secara langsung (online atau on-site) ini ditujukan untuk analis data dan ilmuwan data yang ingin menggunakan Weka untuk melakukan tugas perlombongan data. Pada akhir latihan ini, peserta akan dapat:

Menginstal dan mengkonfigurasi Weka Memahami lingkungan Weka dan bench kerja. Melakukan tugas perlombongan data menggunakan Weka.

Format Kursus

Konferensi dan diskusi interaktif. Banyak latihan dan praktek. Pelaksanaan tangan di lingkungan live-lab.

Opsi Customization Kursus

Untuk meminta pelatihan tersuai untuk kursus ini, silakan hubungi kami untuk mengatur.

IBM SPSS Modeler adalah perangkat lunak yang digunakan untuk data perlombongan dan analisis teks. Ini menyediakan satu set alat perlombongan data yang dapat membangun model prediksi dan melakukan tugas analisis data. Pelatihan yang dipimpin oleh instruktur, secara langsung (online atau on-site) ini ditujukan kepada analis data atau siapa pun yang ingin menggunakan SPSS Modeler untuk melakukan kegiatan perlombongan data. Pada akhir latihan ini, peserta akan dapat:

Memahami dasar-dasar data perlombongan. Pelajari cara mengimpor dan menilai kualitas data dengan Modeler. Mengembangkan, mengimplementasikan, dan mengevaluasi model data secara efektif.

Format Kursus

Konferensi dan diskusi interaktif. Banyak latihan dan praktek. Pelaksanaan tangan di lingkungan live-lab.

Opsi Customization Kursus

Untuk meminta pelatihan tersuai untuk kursus ini, silakan hubungi kami untuk mengatur.

Knowledge discovery in databases (KDD) is the process of discovering useful knowledge from a collection of data. Real-life applications for this data mining technique include marketing, fraud detection, telecommunication and manufacturing. In this instructor-led, live course, we introduce the processes involved in KDD and carry out a series of exercises to practice the implementation of those processes. Audience

• Data analysts or anyone interested in learning how to interpret data to solve problems

Format of the Course

• After a theoretical discussion of KDD, the instructor will present real-life cases which call for the application of KDD to solve a problem. Participants will prepare, select and cleanse sample data sets and use their prior knowledge about the data to propose solutions based on the results of their observations.

Data mining is the process of identifying patterns in big data with data science methods such as machine learning. Using Excel as a data analytical suite, users can perform data mining and analysis. This instructor-led, live training (online or onsite) is aimed at data scientists who wish to use Excel for data mining. By the end of this training, participants will be able to:

• Explore data with Excel to perform data mining and analysis.
• Use Microsoft algorithms for data mining.
• Understand concepts in Excel data mining.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

The objective of the course is to enable participants to gain a mastery of how to work with the SQL language in Oracle database for data extraction at intermediate level.

R is a programming language and software environment for statistical computing. SAS is a statistical software platform for predictive analysis, data management, advanced analytics, and more. With R in SAS, users can find natural groups of data for cluster analysis that are essential to data mining. This instructor-led, live training (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis. By the end of this training, participants will be able to:

• Use cluster analysis for data mining
• Master R syntax for clustering solutions.
• Implement hierarchical and non-hierarchical clustering.
• Make data-driven decisions to help to improve business operations.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Audience If you try to make sense out of the data you have access to or want to analyse unstructured data available on the net (like Twitter, Linked in, etc...) this course is for you. It is mostly aimed at decision makers and people who need to choose what data is worth collecting and what is worth analyzing. It is not aimed at people configuring the solution, those people will benefit from the big picture though. Delivery Mode During the course delegates will be presented with working examples of mostly open source technologies. Short lectures will be followed by presentation and simple exercises by the participants Content and Software used All software used is updated each time the course is run, so we check the newest versions possible. It covers the process from obtaining, formatting, processing and analysing the data, to explain how to automate decision making process with machine learning.

Objective: Delegates be able to analyse big data sets, extract patterns, choose the right variable impacting the results so that a new model is forecasted with predictive results.

Course can be provided with any tools, including free open-source data mining software and applications

This instructor-led, live training (online or onsite) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python. By the end of this training, participants will be able to:

• Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection.
• Compare and implement various strategies for solving real-world data mining problems.
• Understand and interpret the results. 

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Data Vault Modeling is a database modeling technique that provides long-term historical storage of data that originates from multiple sources. A data vault stores a single version of the facts, or "all the data, all the time". Its flexible, scalable, consistent and adaptable design encompasses the best aspects of 3rd normal form (3NF) and star schema. In this instructor-led, live training, participants will learn how to build a Data Vault. By the end of this training, participants will be able to:

• Understand the architecture and design concepts behind Data Vault 2.0, and its interaction with Big Data, NoSQL and AI.
• Use data vaulting techniques to enable auditing, tracing, and inspection of historical data in a data warehouse.
• Develop a consistent and repeatable ETL (Extract, Transform, Load) process.
• Build and deploy highly scalable and repeatable warehouses.

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

This course is intended for engineers and decision makers working in data mining and knoweldge discovery. You will learn how to create effective plots and ways to present and represent your data in a way that will appeal to the decision makers and help them to understand hidden information.

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.

Big data is data sets that are so voluminous and complex that traditional data processing application software are inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.

The objective of the course is to enable participants to gain a mastery of the fundamentals of R and how to work with data.

KNIME Analytics Platform is a leading open source option for data-driven innovation, helping you discover the potential hidden in your data, mine for fresh insights, or predict new futures. With more than 1000 modules, hundreds of ready-to-run examples, a comprehensive range of integrated tools, and the widest choice of advanced algorithms available, KNIME Analytics Platform is the perfect toolbox for any data scientist and business analyst. This course for KNIME Analytics Platform is an ideal opportunity for beginners, advanced users and KNIME experts to be introduced to KNIME, to learn how to use it more effectively, and how to create clear, comprehensive reports based on KNIME workflows

KNIME is a free and open-source data analytics, reporting and integration platform. KNIME integrates various components for machine learning and data mining through its modular data pipelining concept. A graphical user interface and use of JDBC allows assembly of nodes blending different data sources, including preprocessing (ETL: Extraction, Transformation, Loading), for modeling, data analysis and visualization without, or with only minimal, programming. To some extent as advanced analytics tool KNIME can be considered as a SAS alternative. Since 2006, KNIME has been used in pharmaceutical research, it also used in other areas like CRM customer data analysis, business intelligence and financial data analysis.

MATLAB adalah lingkungan komputasi numerik dan bahasa pemrograman yang dikembangkan oleh MathWorks.

MonetDB is an open-source database that pioneered the column-store technology approach. In this instructor-led, live training, participants will learn how to use MonetDB and how to get the most value out of it. By the end of this training, participants will be able to:

• Understand MonetDB and its features
• Install and get started with MonetDB
• Explore and perform different functions and tasks in MonetDB
• Accelerate the delivery of their project by maximizing MonetDB capabilities

Audience

• Developers
• Technical experts

Format of the course

• Part lecture, part discussion, exercises and heavy hands-on practice

R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has also found followers among statisticians, engineers and scientists without computer programming skills who find it easy to use. Its popularity is due to the increasing use of data mining for various goals such as set ad prices, find new drugs more quickly or fine-tune financial models. R has a wide variety of packages for data mining.

Goal: Learning to work with SPSS at the level of independence The addressees: Analysts, researchers, scientists, students and all those who want to acquire the ability to use SPSS package and learn popular data mining techniques.

Process mining, or Automated Business Process Discovery (ABPD), is a technique that applies algorithms to event logs for the purpose of analyzing business processes. Process mining goes beyond data storage and data analysis; it bridges data with processes and provides insights into the trends and patterns that affect process efficiency.  Format of the Course

• The course starts with an overview of the most commonly used techniques for process mining. We discuss the various process discovery algorithms and tools used for discovering and modeling processes based on raw event data. Real-life case studies are examined and data sets are analyzed using the ProM open-source framework.