22 Nov, 23 Nov, 24 Nov 2023 | 9am-5.30pm | Online
Machine Learning in Python:
24 Oct, 25 Oct, 26 Oct 2023 | 9am-5.30pm | Online
Duration: 6 Days
This PC will equip learners with the following competencies:
- Learn the various Machine Learning methods in data analysis
- Have the necessary knowledge and strong foundation to appreciate the proper application of advanced analytics methods in business applications
- Learn data structures and programming constructs in Python and have a strong foundation in Python programming
- Learn and apply the Python programming language for creating Machine Learning applications
Job Role Readiness
This programme is designed for mid-career PMETs and to meet the needs of:
Professionals who plan to be a Data Analyst/Business Analyst that is capable of integrating Business, Customer and Technology to produce digital products that the market needs.
It will prepare learners for the following job roles to perform their responsibilities more effectively:
- Business Analyst
- Data Analyst
- Data Engineer
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.
At least a polytechnic diploma (or equivalent) with 3 years working experience, preferably in the IT industry.
(Click their photos to view their short biographies)
Assoc Prof Danny Poo
Assoc 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 5 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.
- Deutsche Bank
- 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 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 on teaching Artificial Intelligence and Data Science courses, e.g. machine learning, deep learning, data mining and etc.
She graduated from NUS with a PhD degree on Electrical and Computer Engineering. Her research focused on Game Theoretical Modelling, Optimization Methods, Algorithm Design and Wireless Networks.
She worked in BHP Billiton Marketing Asia for eight years and gained a lot of industry experience through different functions, e.g. risk management, supply chain management, sales and marketing planning and etc.
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. Previously, Dr Yeo was Adjunct Lecturer at the Department of Economics in the NUS Faculty of Arts and Social Sciences. Since 2019, he has been teaching the ECA5372 Big Data Analytics module as part of the Master of Economics coursework programme. Additionally, he was a part-time lecturer at Nanyang Business School in Nanyang Technological University where he imparted knowledge on Python programming and analytics to undergraduate students pursuing business studies. Prior to these roles, Dr Yeo served as the Lead Instructor (Data Science Immersive) at a New York-based commercial Continuing Education and Training (CET) provider. In that capacity, he taught aspiring adult learners in Hong Kong, Sydney, and Singapore who were eager to upskill or transition into new careers. Dr Yeo received his doctorate from National University of Singapore. For his PhD work, he focused his research on machine learning and data mining within a multinational pharmaceutical company. His area of specialization involved developing computational methods aimed at uncovering potential medicinal drug candidates. He also holds the WSQ Advanced Certificate in Training and Assessment (ACTA) from the Institute for Adult Learning Singapore.
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email firstname.lastname@example.org
To register, click Register
Machine Learning in Python: TGS – 2022011025
Python Programme: TGS – 2022011018 / TGS – 2022011046 (Synchronous e-learning)