Data Technology and Management for IT Professionals

Course Reference No:
TGS-2021002740 (Classroom Learning)
TGS-2020504739 (Synchronous e-Learning)


Learn Orange, Python, Apache Hadoop and Apache Spark. Get an introduction to modern data management techniques with this data analytics course in Singapore.

Course Objectives

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.


Day 1
  • 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
Day 2
  • 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

On-campus or Online (Live)


Dr. Amirhassan Monajemi


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.


Dr. Edmund Low


Edmund Low is currently Senior Lecturer with the University Scholars Programme (USP) at the National University of Singapore. He teaches courses on engineering, statistical methods, data science and analytics. He currently heads the quantitative reasoning domain, and was formerly director of the Quantitative Reasoning Centre, at USP. He has organised / co-organised programming workshops and data hackathon for students. As an educator, Edmund is a multiple recipient of both the USP Teaching Excellence Award, as well as the NUS Annual Teaching Excellence Award. He has more than 15 years of academic and professional experience in the use of computational modelling and data-driven tools, applying them to solve problems in public health, water resource management and air quality in buildings. Edmund holds a PhD in Environmental Engineering from Yale University.




2 Days
9.00am to 5.30pm 


National University of Singapore
University Town

International Participants


Incl. GST

Singapore Citizens
(39 yrs old or younger) 


Singapore PRs


Incl. GST

Singapore Citizens
(40 yrs or older)


Incl. GST

Enhanced Training Support for SMEs


Incl. GST

Sign Up Now

Fees & Funding

Singapore Citizen1
39 years old or younger
Singapore Citizen1
40 years or older eligible for MCES2
Singapore PRs Enhanced Training
Support for SMEs3
Full Programme Fee S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00 S$1,900.00
SSG Funding
(Refer to Funding Page for Claim Period)
- (S$1,330.00) (S$1,330.00) (S$1,330.00) (S$1,330.00)
Nett Programme Fee S$1,900.00 S$570.00 S$570.00 S$570.00 S$570.00
7% GST on Nett
Programme Fee
S$133.00 S$39.90 S$39.90 S$39.90 S$39.90
Total Nett Programme
Fee Payable, Incl. GST
S$2,033.00 S$609.90 S$609.90 S$609.90 S$609.90
Less Additional Funding if Eligible Under Various Scheme - - (S$380.00) - (S$380.00)
Total Nett Programme Fee, Incl. GST,
after additional funding from the various funding schemes
S$2,033.00 S$609.90 S$229.90 S$609.90

Learners must fulfill at least 75% attendance and pass all assessment components, to be eligible for SSG funding.

  1. All self-sponsored Singaporeans aged 25 and above can use their $500 SkillsFuture Credit to pay for the programme. Visit to select the programme. 
  2. Mid-Career Enhanced Subsidy (MCES) - Singaporeans aged 40 and above may enjoy subsidies up to 90% of the programme fee. 
  3. Enhanced Training Support for SMEs (ETSS) - SME-sponsored employees (Singaporean Citizens and PRs) may enjoy subsidies up to 90% of the programme fee. Click Here for more information. 
  4. Eligible organisations (excluding government entities) may apply for the absentee payroll funding for Singaporean/permanent resident participants attending the programme during working hours. The absentee payroll funding is computed at 80% of hourly basic salary capped at $4.50 per hour or $7.50 per hour for SME. For more information, visit Enterprise Portal for Jobs & Skills
  5. Eligible individuals may apply for training allowance capped at $6/hr under the WSS scheme, visit- WSG for more information..
  6. NTUC Training Fund (SEPs) – All self-employed (i.e. freelancers and sole-proprietors-with-no-employee) Singaporeans and Permanent Residents are eligible to apply for the NTUC Training Fund (SEPs) from NTUC’s Employment and Employability Institute (e2i). Click here for more information.
NTUC members enjoy 50% of the unfunded course fee support for up to $250 each year for courses supported under UTAP (Union Training Assistance Programme). Terms and Conditions apply. Please visit
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27 July 2021