Do You Want To Supercharge Your Career ?
January 31, 2022
The digital era is here to stay, and it created an important commodity – data. Supercharge your career by building deep skills to work with data.
In collaboration with AI Singapore (AISG), SP offers a Minor in Data & Artificial Intelligence. This Minor is open to all students except for students from Data & AI disciplines.
Students will take five (5) prescribed elective modules to build relevant data skills and be able to apply skills learnt in a relevant industry project, curated by SP Data Science and Analytics Centre with support from AISG, for their internship. Students who successfully complete both the Minor and internship will be awarded an additional e-certificate in AISG’s AI Data Apprenticeship Programme (AIDP).
Guidance on how the five elective modules should be taken:
Students will need to successfully complete the first block of elective modules (Fundamentals and Automation of Data Analysis using Excel, Programming for Data Science) in any sequence. Students will then progress to undertake the second block of elective modules (Data Visualisation for Business, Thinking with Data for Decision Making) in any sequence. Upon successful completion of the electives in the second block students will undertake the last elective module (Machine learning in Python).
Students who successfully complete all five elective modules will earn a Minor in Data & AI.
The synopses of the five elective modules are as follows:
| Module Name | Synopses |
| 1. Fundamentals and Automation of Data Analysis using Excel | This elective equips students with skills in data analysis and its automation, that AISG’s AIDP program trains its participants in. This practical elective is designed to equip students with the essential skills to perform data analysis using Excel. It further aims to equip students with the necessary skills to automate repetitive data analysis tasks in Excel, using Robotic Process Automation (RPA). In particular, this elective provides a much needed platform to train students in the integration of RPA with Excel to automate tedious, monotonous and repetitive tasks with the important aim of making the data analysis pipeline efficient. |
| 2. Programming for Data Science | This module provides students with the fundamental skills to code applications to retrieve, clean and visualize data using the Python programming language. Students will learn how to apply Python packages to describe data, explore data and to create visualizations that can help them gain useful insights from it. |
| 3. Data Visualization for Business | This module is designed to equip students with practical skills needed in processing, summarising and understanding data through visual analytics, in particular, via exposure to the Power BI platform and Python programming. Students will develop an appreciation of the visualization workflow and an understanding of the types of visuals that may be created for representing data, based on the complexity of the problem. The students will also acquire the skill of developing data dashboards progressively, via the integration of Power BI and Python, to facilitate effective monitoring of data. Students will apply skills learnt in the course, in creative ways to develop dashboards with real world data. |
| 4. Thinking with Data for Decision Making | This module aims to equip students with sound statistical concepts and techniques to conduct exploratory data analysis for various types of datasets, to draw insights and make inferences. Students will be trained to use Python and its libraries to represent, analyse and interpret data. |
| 5. Machine Learning in Python | This module introduces participants to the fundamentals machine learning (ML) techniques. Students will be introduced to various Low-code tools for Machine Learning (e.g., Microsoft Azure Machine Learning Studio, Orange Data Mining, and PyCaret/Scikit-Learn Python ML Library). Students will apply the knowledge gained to build a functional ML model. This model can be for improved data analysis beyond classic statistical techniques |