Python for Data Engineering
Dates: To be advised
Course Overview
This course teaches the essential programming skills in Python needed to become a successful data engineer. Participants will learn fundamental python concepts including data structures and data analysis with a special focus on data engineering skills. It will cover different data engineering elements such as data ingestion, data acquisition and data manipulation. It will cover some additional advanced data engineering topics with Python such as working with Representational State Transfer (REST) Application Programming Interfaces (API) to pull data. By the end of this course, participants will be trained in writing basic programmes involving data engineering skills.
This course is part of Professional Certificate in Data Engineering Foundations.
Course Objectives
This course will equip learners with the following competencies:
- Foundations in Python
- Programming data engineering problems with Python
- Using Python for data ingestion, data acquisition and data manipulation
- Understanding different libraries and packages in Python which are useful for data engineers
Minimum Entry Requirement
Diploma Holder
Course Convener
(Click photo to view biography)
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 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.
Course Fees
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 soc-ace@nus.edu.sg
To register, click Register
Course Codes
TGS-2023017999 / TGS-2023018000 (Synchronous e-learning)
Singapore Citizens
39 years old or youngerSingapore Citizen
40 years old or older