ICT&SS ProfessionalProject Manager (Agile)Digital Service ManagerDigital Business AnalystData EngineerAI Engineer
Overview
Explore modern data management technologies, 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 programme 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.
Key Takeaways
At the end of this programme, you will be able to:
Understand the basic terms of DTM, along with the characteristics and applications of different 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
Who Should Attend
Please refer to the job roles section.
Suitable for IT Engineers, IT Managers, IT Executives and IT Consultants.
Prerequisites
Minimum two years of working experience in the field of IT.
Programme Structure
The programme covers:
Programme 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
Fees
Full Fee
Full programme fee
S$1900
9% GST on nett programme fee
S$171
Total nett programme fee payable, incl. GST
S$2071
With effect from 1 Jan 2024
NOTE Funding is available for this programme. Please visit the Learning Parner's website to find out about the updated programme fee funding breakdown, eligibility, terms and conditions.
Apply through your organisation's training request system.
Step 2
Your organisation's training request system (or relevant HR staff) confirms your organisation's approval for you to take the programme.
Your organisation will send registration information to the academy.
Organisation's HR L&D or equivalent staff can click here to register through Learning Partner's registration portal.
The HR L&D will need to generate a URL link and send it to the participant to register for the programme under Corporate-Sponsored. The participant must first log in to L3AP using Singpass before clicking on the URL link to complete their registration and declaration. Failure to do so will result in registration under Self-Sponsored.
Please refer to NUS Lifelong Learning Portal (L3AP) guide here.
Step 3
The Learning Partner will inform you whether you have been successful in enrolment.
Comprehensive presentation slides with latest information on data technology and management. Learnt about serverless computing, cloud and edge computing, NoSQL, containerisation, data warehouse/lakes, Hadoop parallel storage and processing, etc.
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Exploring how these facets impact big data and analytics not only reinforced their significance but also allowed me to articulate their importance more clearly.
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The facilitator gave a very clear explanation of data technology management systems and big data. He also provided many examples to help better my understanding. I gained a better understanding and appreciation of big data technology after completing this programme.
