Developing Windows Azure and Web Services (MOC-20487)
In this course, students will learn how to design and develop services that access local and remote data from various sources. Students will also learn how to develop and deploy services to hybrid environments, including on-premises servers and Microsoft Azure.
Key Outcomes:
Will be able to:
Developing Application With VBA Excel
Microsoft Excel tidak hanya digunakan sebagai penyimpanan data, tapi bisa juga membuat aplikasi. Pelatihan Developing Application With VBA Excel ini mengajarkan anda bagaimana cara membuat aplikasi dengan Database dan Macro.
Jadilah pengguna Excel yang lebih pintar!
Materi Lainnya:
- VBA Fundamental
- Logical Programming With VBA Excel
Data Mining
Data Mining is an activity that includes collecting, using historical data to find order, patterns and relationships in large data sets. The use of data mining is to specify patterns that must be found in data mining tasks. Data mining presence is motivated by data explosion problems that have been experienced lately where many organizations have collected data for many years (purchase data, sales data, customer data, transaction data, etc.). The discussion of material in Data Mining Training focuses on utilizing data mining in the real world.
Business Process Model and Notation (BPMN)
Business Process Model and Notation - BPMN is the de-facto notation standard for business process modeling and provides a common graphical language for end-to-end business process workflows that can be readily adopted and understood in all areas of the business. The objective of BPMN is to support business process management, for both technical users and business users, by providing a notation that is intuitive to business users, yet able to represent complex process semantics.
Analisis Perancangan Sistem Informasi
In designing a system, especially an information-based system, analysis is very necessary. The analysis is useful for minimizing the occurrence of errors when an information system is designed. This section is one part of efforts to develop or design a system, including an information system itself. A good company is one that can make systematic and orderly system analysis and design so that it is easily accepted by all employees.
KEY OUTCOMES
Advanced RPA and Intelligent Automation
This course explores the relationship between artificial intelligence (AI) and RPA and describes how these technologies can be combined to establish intelligence automation (IA) environments. The course covers different types of autonomous decision-making and further extends the usage scenarios from Module 1 by incorporating Artificial Intelligence (AI) systems as part of intelligent automation solutions.
Key Outcomes:
Students will be able to know:
Fundamental RPA
This course establishes the components and models that comprise contemporary robotic process automation (RPA) environments. Different types of RPA bots are explained, along with different RPA architectures and bot utilization models. This course further provides detailed scenarios that demonstrate different deployments of RPA bots and other components in relation to different business automation requirements.
Data Science Governance Lab for Big Data, Machine Learning & AI
This course is the last of the three courses you must take to pursue the Data Science Governance Specialist certification. This lab course incorporates a series of detailed exercises that require participants to solve a wide range of interrelated problems, with the aim of driving a comprehensive understanding of how various data science governance rules and processes can be applied to address common governance issues, as well as having participants apply their knowledge of the course prior to meeting project requirements and solving real-world problems.
Advanced Data Science Governance for Big Data, Machine Learning & AI
This course is the second of three courses you must take to pursue the Data Science Governance Specialist certification.In this course, over 80 additional data science governance precepts and processes are described in relation to analytics platform governance and machine learning and AI pipeline governance stages.
Key Outcomes:
After this course you will know:
Fundamental Data Science Governance for Big Data, Machine Learning & AI
This course is the first of three courses you must take to pursue the Data Science Governance Specialist certification. This course describes data science governance concepts and basics and identifies common risks and challenges, as well as key roles for those involved in governance projects. The course further explores the analytics pipeline governance lifecycle and establishes over 70 data science governance precepts and processes. The course maps how precepts and processes relate to each other and how they relate to governance stages.