This introductory course has been developed to introduce modern data management technologies to participants. Among the topics to be analysed are the definition of basic data management techniques and technologies, the background of such technologies, and the future trends of Data Technology and Management (DTM).
This course will also explain the differences between various Data Management approaches, environments, and tools, as well as the advantages and disadvantages associated with their diverse applications. Emerging trends in the field of DTM will be reviewed as well.
At the end of the course, participants will be able to:
- Understand the basic terms of DTM, along with the characteristics and applications of different applicable DTM techniques.
- Articulate the future trends of DTM.
- Compare the advantages and disadvantages of the new DTM platforms and tools in different applications.
- Recommend a set of tools for a given problem.
- Course Introduction, description of the goals, contents, and scheme
- Data management technology: Definitions and basic terms
- Big Data and Big Data Technology: Definitions and basic terms
- A review of the DBMS/RDBMS technology and trend
- Technical details of Big data, Cloud and Edge techniques
- Hardware and software requirements
- Differences between ordinary DBMS/RDBMS and Big data
- Skills and resources needed for big data management
- Data quality for big data solutions: data quality assurance for big data systems
- Data quality for big data solutions: new methodologies
- Cloud and edge technologies introduction
- Moving to big data: architecture, infrastructure, application, metadata
- Future trends in big data technology
- Tools review: Airflow, Kubernetes, Docker, ML Flow, Google Cloud, Mongo DB, Graph Databases, No SQL, and Server-less technologies
- Tools Comparison: Airflow, Kubernetes, Docker, ML Flow, Google Cloud, Mongo DB, Graph Databases, No SQL, and Server-less technologies
- Security considerations in modern DM
- Versatility considerations: Technical and implementation
- Volume and Velocity considerations: Technical and implementation
- IoT and multi-resources considerations: technical and implementation
- Big data implementation: requirements, roadmaps, strategies
- Big data implementation: challenges, integration, and validation
- Big data analysis challenges and methodologies
- Big data scaling challenges, necessities, and the application side
- Data warehousing for big data and its challenges
- Describing future DTM trends
Orange, Python, Apache Hadoop, Apache Spark
Minimum two years of working experience in the field of IT
Who Should Attend
IT Engineers, IT Managers, IT Executives, IT Consultants
Mode of Training
Online Workshop using Zoom
Dr. Amirhassan Monajemi
Dr. Amirhassan Monajemi is a Senior Lecturer in AI and Data Science with the School of Continuing and Lifelong Education (SCALE) at the National University of Singapore (NUS). Before joining the NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI and Machine Learning. He was born in 1968 in Isfahan, Iran. He studied towards BSc and MSc in Computer Engineering at Isfahan University of Technology (IUT), and Shiraz University respectively. He got his PhD in computer engineering, pattern recognition and image processing, from University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Vision, Data Science, and IoT.
He has taught the Data Science courses, including Data Mining, Advanced Data Mining, AI, and Neural Networks since 2008 at both undergraduate and postgraduate levels. He was awarded the best university teacher of the province in 2012. He also has studied Learning Management Systems, E-Learning, and E-Learning for workplaces since 2007.
Dr. Monajemi was the data science advisor of MSC (Mobarakeh Steel Complex, the largest in the country). Moreover, he has registered three patents and published more than a hundred research papers in peer-reviewed, indexed journals and international conferences (IEEE, Elsevier, Springer, and so on), and supervised several Data Science, IoT, and AI industrial projects in various scales, including analysis of the effectiveness of research projects in enterprises, and data mining to extract the common patterns among the unsuccessful BSc students. He is experienced in different sub-domains of data science from theory to practice, including data mining and pattern recognition.