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Short Course

Python Programming

Programme ScheduleDuration (days)Mode of DeliveryProgramme Code
6, 7 and 9 January 2025 | 9 am - 5.30 pm3Classroom LearningTGS-2022011018
Course Duration: 3 Days

Course Overview

Python Programming teaches the fundamentals of Python. It introduces the highly popular programming language with simplicity but brings forth the power and clear syntax of Python. It teaches the core features of Python and participants will be able to create software applications using Python. Besides, it teaches the NumPy and pandas packages that prepare participants to produce Data Analytics software applications. Topics covered include:

  • The Python programming environment: Learn to install and use the Anaconda programming environment to get the best out of Python.
  • The Python variables and types: Learn the fundamental elements that define a programming language.
  • Data structures including list, tuple, set, dictionary and string: Learn the building blocks that make up a Python program.
  • Operators: Learn how to use the components in constructing statements to express yourself in Python.
  • Program flow controls: Learn how to provide dynamism in programming.
  • Functions: Learn how to express yourself more fluently with statements.
  • Inputs and outputs: Learn how to create interactivity in Python.
  • File handling: Learn how to read from and store data to secondary storage.
  • NumPy: Learn how to use the popular package for managing numbers.
  • Pandas: Learn how to use extended data structures, Series and DataFrame for efficient management of data variables.
  • Create data analytics software applications using the data structures and functions in Python.

Course Objectives

This course will equip learners with the following competencies:

  • Learn and apply data structures in Python
  • Learn and apply programming constructs in Python
  • Learn to create and use files and produce outputs in Python
  • Build a strong foundation in the fundamentals in Python programming
  • Learn and use libraries (e.g. NumPy and pandas) in Python API
  • Learn and apply Python programming language to create simple software applications

Job Role Readiness

Data Analysts and Business Analysts.

Prerequisites

At least a polytechnic diploma.

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This programme is part of

  • Professional Certificate in Applied Machine Learning
  • Professional Certificate in Machine Learning Using Python
  • Professional Certificate in Programming using Python and JavaScript

    • Types of Available Fundings

  • Enhanced Training Support
  • SFEC
  • UTAP
  • PSEA

    • Testimonials


    Dr Yeo is experienced and excellent in making Python easy for beginners to understand. Worked on examples which make it easier for students to practise and understand the concepts. Definitely recommended for everyone!

    Great instructor and good course to take for beginners in programming.

    I was very impressed and happy with the course. Materials were very well done. The lecturer was very precise in his delivery.

    I recently completed the Python course and I am happy with the experience! The curriculum was comprehensive, the instructor was very knowledgeable, and the hands-on projects were engaging.

    Walk in with 0. Walked out a Python programmer.

    The pace of the programme was just right for students without Python programming knowledge. The materials provided were also very clear and well condensed. Dr Yeo used real life scenario / analogy to assist us in understanding Python codes which is really helpful. He was also very humorous which greatly livened up the programming class.

    The course enabled me to learn the technicalities of Python and ensure that I am able to apply this knowledge in the working world.

    Prof Yeo is amazing.

    I enjoy my time learning Python Programming a lot.

    Instructors


    Dr Ai Xin

    Dr Ai Xin

    Dr Ai Xin is currently a Lecturer with the School of Computing at the National University of Singapore (NUS). She has many years’ experience teaching Artificial Intelligence and Data Science courses such as machine learning, deep learning and data mining. She graduated from NUS with a PhD degree in Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks. She worked at BHP Billiton Marketing Asia for eight years, gaining extensive industry experience in various functions such as risk management, supply chain management, and sales and marketing planning.

    A/Prof Danny Poo

    A/Prof Danny Poo

    A/Prof Danny Poo brings with him 35 years of Software Engineering and Information Technology and Management experience. A graduate from the University of Manchester Institute of Science and Technology (UMIST), England, Dr Poo is currently an Associate Professor at the Department of Information Systems and Analytics, National University of Singapore. Prior to joining the University, Dr Poo was with the System Operations at DBSBank, Singapore. A Steering Committee member of the Asia-Pacific Software Engineering Conference, Dr Poo is actively involved in Information Management and Healthcare Analytics research. A well-known speaker in seminars, Dr Poo has conducted numerous in-house training and consultancy for organizations, both locally and regionally. Dr Poo is the author of five books on Object-Oriented Software Engineering, Java Programming language and Enterprise JavaBeans.

    Dr Poo notable teaching credentials include:

    • Data Strategy
    • Data StoryTelling
    • Data Visualisation
    • Data Analytics
    • Machine Learning
    • Data Management
    • Data Governance
    • Data Architecture
    • Capstone Projects for Business Analytics
    • Software Engineering
    • Server-side Systems Design and Development
    • Information Technology Project Management
    • Health Informatics
    • Healthcare Analytics
    • Health Informatics Leadership.
    Industry Credentials
    • Deutsche Bank
    • Gemplus
    • Micron
    • NCR
    • PIL
    • PSA
    • Rhode-Schwarz
    • Standard Chartered Bank
    • ST Electronic
    • Monetary Authority of Singapore
    • Infocomm Development Authority
    • National Library Board
    • Ministry of Manpower
    • Nanyang Technological University
    • Nanyang Polytechnic
    • National University Hospital.

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi

    Dr Amirhassan Monajemi is a Senior Lecturer in AI and Machine Learning with the School of Computing (SoC) at the National University of Singapore (NUS). Prior to SoC, he was a Senior Lecturer in NUS School of Continuing and Lifelong Education (SCALE) teaching AI and Data Science to adult learners. Before joining NUS, he was with the Faculty of Computer Engineering, University of Isfahan, Iran, where he was serving as a professor of AI, Machine Learning, and Data Science. He was born 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 the University of Bristol, Bristol, England, in 2005. His research interests include AI, Machine Learning, Machine Vision, IoT, Data Science, and their applications. He has taught the artificial intelligence courses, including AI, Advanced AI, Expert Systems, Decision Support Systems, Neural Networks, and Cognitive Science since 2005 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 has registered a few patents in the fields of AI, Machine Vision, and Signal Processing applications, including an AI and machine vision-based driver drowsiness detection system and a low power consuming spherical robot. He also has 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 Isfahan intelligent traffic system delivery and testing, and red light runners detection. He is experienced in different sub-domains of Artificial Intelligence and Machine Learning, from theory to practice, including Deep Learning, Logic, and Optimisation.

    A/Prof Tan Wee Kek

    A/Prof Tan Wee Kek

    A/Prof Tan Wee Kek is currently an Associate Professor in the Department of Information Systems and Analytics at the School of Computing, National University of Singapore. He is also currently serving as an Assistant Dean (Student Life) in the School of Computing, and a Fellow and EXCO member of the NUS Teaching Academy. He graduated with a Doctor of Philosophy in Information Systems in July 2013 and a Bachelor of Computing in Information Systems (1st Class Honours) in July 2007, both from the National University of Singapore. Prior to this, he attended Singapore Polytechnic and graduated with a Diploma in Computer Information Systems with Merit in July 2001. His current primary research interests focus on consumer-based information technology (e.g., online decision aids, social computing, virtual worlds and consumer cloud services). Most of his research is based on design science, a well-established problem-solving paradigm that has been widely adopted in information systems research. His current secondary research interests focus on information systems education. His work has been published or is forthcoming in journals such as Journal of the American Society for Information Science and Technology (JASIST), Decision Support Systems (DSS), Communications of the Association for Information Systems (CAIS), and Journal of Information Systems Education (JISE). His work has also been presented or is forthcoming in conferences such as ACM SIGMIS Computer Personnel Research Conference (SIGMIS-CPR), IFIP Working Group 8.2 Working Conference (IFIP WG8.2), European Conference on Information Systems (ECIS), Americas Conference on Information Systems (AMCIS), and International Conference on Human-Computer Interaction (ICHCI). He has won the faculty level Research Achievement Award in 2013 and was a nominee for the ACM SIGMIS 2009 Magid Igbaria Outstanding Conference Paper of the Year. His current teaching interests include imparting senior undergraduate students with knowledge and skills to develop enterprise information systems as well as teaching them principles of information security management, and concepts of mobile and ubiquitous commerce. He has won the University level Annual Teaching Excellence Award for AY 2009 to 2012. In addition to this, he has also won the Faculty Teaching Excellence Award for AY 2008 to 2011 as well as the Faculty Best Teaching Assistant Award for AY 2007/2008. For his sustained commitment to teaching excellence, he has been placed on the Faculty Teaching Excellence Award Honour Roll for AY 2010/2011 and University level Annual Teaching Excellence Award Honour Roll for AY 2012/2013. He is presently the lead faculty mentor of the NUS BiZiT Society, a student special interest group on business and information technology.

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang

    Dr Yeo Wee Kiang is a Senior Lecturer at the Department of Information Systems and Analytics in the NUS School of Computing, where he is dedicated to researching the use of technologies like Adaptive Learning and Large Language Models (LLMs) to create personalized and engaging computing education experiences. With a PhD from NUS focused on machine learning and data mining for pharmaceutical drug discovery, Dr Yeo brings practical industry experience as the former Director of Data Science at a food tech startup, where he led a team applying AI and machine learning to enhance nutritional value. His diverse background includes being an Adjunct Lecturer in the Department of Economics at NUS Faculty of Arts and Social Sciences, helming the Big Data Analytics module in the Master of Economics programme from 2019 to 2023, and teaching Python programming and data analytics at Nanyang Business School. Prior to his academic appointments, Dr Yeo was the Lead Instructor for Data Science at a New York-based commercial Continuing Education and Training (CET) provider, teaching aspiring adult learners in Hong Kong, Sydney, and Singapore. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore. As part of the NUS ACE (Advanced Computing for Executives) teaching team, Dr Yeo specializes in technical training and practical computing education focused on data analytics, machine learning, and Python, aiming to equip professionals with in-demand Data Science skills.

    Dr Leong Wai Kay

    Dr Leong Wai Kay

    Dr Leong Wai Kay is a lecturer with the Department of Computer Science at National University of Singapore (NUS). He graduated with Bachelor of Computing and PhD in Computer Science from National University of Singapore. He is currently teaching undergraduate courses in programming methodology, and software engineering on iOS platform.

    Programme Fees


    Singapore Citizens Singapore PRs Enhanced Training Support for SMEs International Participants
    39 years old
    or younger    		 40 years old
    or older
    Full Programme Fee S$2,550.00 S$2,550.00 S$2,550.00 S$2,550.00 S$2,550.00
    Less: SSG Grant Amount S$1,785.00 S$1,785.00 S$1,785.00 S$1,785.00 -
    Nett Programme Fee S$765.00 S$765.00 S$765.00 S$765.00 S$2,550.00
    9% GST on Nett Programme Fee S$68.85 S$68.85 S$68.85 S$68.85 S$229.50
    Total Nett Programme Fee Payable, Including GST S$833.85 S$833.85 S$833.85 S$833.85 S$2,779.50
    Less Additional Funding - S$510.00 - S$510.00 -
    Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes S$833.85 S$323.85 S$833.85 S$323.85 S$2,779.50
    1. Total Nett Programme Fee Payable, including GST, after additional funding from various funding schemes.
    2. Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
    3. Please note that all external funding for courses is limited in duration and subject to eligibility and availability.

    Other Information

    This course is eligible for Union Training Assistance Programme (UTAP). NTUC members can enjoy up to 50% funding (capped at $250 per year) under UTAP. NTUC members aged 40 and above can enjoy higher funding support up to $500 per individual each year, capped at 50% of unfunded course fees, for courses attended between 1 July 2020 to 31 December 2025. Please click here for more information.

    Advanced Computing for Executives

    11 Research Link
    COM 3, #B1-07
    Singapore 119391
    Email: soc-ace@nus.edu.sg

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