Overview of 73
Hello and Welcome!
Welcome to SP2273: Working on Interdisciplinary Science, Pythonically. Thank you for joining 731. My name is Chammika Udalagama, and I am the course coordinator for this course. On this page, I will quickly run through many of the important features of the course. I have greatly elaborated on these in other chapters of this part.
If you are wondering, inspired by one of my favourite authors2, for several years now, I have been writing all my course notes in the first person.
Learning Objectives
If there is one thing expected of you as a university graduate, it is to be an effective problem-solver. So, as an educator, it is my duty to prepare you to be effective and useful problem solvers. For this, I need to provide opportunities for you to hone your problem-solving skills in authentic settings like those you would encounter in a workplace (in science or otherwise). We must also cultivate and practice skills related to self-regulated learning, collaboration, and communication, as these are necessary ingredients for your success. Therefore, my broad, overarching goal for this module is for you to use the experience and skills you learn to supercharge your life as a problem solver.
Reflecting all these and my belief that learning is not a spectator sport, 73 is an intensively hands-on course. It has a balanced mix of individual and group components, focusing on both basics and applications. Furthermore, since computational skills and computation are essential (like mathematics and writing), I see this course as a service course that develops knowledge and skills that are more important and useful outside of it.
Lastly, I want to point out that 73 is not the best place to learn Python comprehensively. As much as I love playing with Python, I see Python as just one of many problem-solving tools. Instead, 73, is about figuring out ways to use a computational tool (Python in our case) in science, intelligently.
The structure of 73
Highlights of 73
The course is broadly split into two portions. The first(Weeks 1 - 6) focuses on developing and practicing fundamental skills individually. The second(Weeks 7 - 13) focuses on working in a group to apply your fundamental skills to solve problems and broaden your learning. Let me first share some of the highlights of 73. I will explain these in greater depth later.
- The module is extensively hands-on in design.
- Ample support, scaffolding, and feedback will help your learning journey.
- In weeks 1 to 6, you have leeway to pick a pace and form of learning that suits you.
- This dedicated website for the content.
- We use many industry-standard platforms and tools (Git, GitHub, Jupyter).
- Differentiated learning (see below).
- Mastery learning (see below).
- Webcasting in Weeks 1 to 6.
Course content (Week 1 to 6)
Differentiated instruction
One of the exciting features of our class is diversity. Not only does the cohort come from different disciplines, but you also have varied interests and backgrounds. This means each of you will begin this course from different starting points. Moreover, some of you will naturally like the module’s content and have higher levels of motivation. These individuals will want to go through the module at lightning speed (let’s call these the racers), while the rest (let’s call them the hikers) will prefer a more leisurely pace. So, to make this module work for everyone, we need to have some form of differentiated instruction (Tomlinson (2017)) that will allow each of you to consume the course content comfortably. To facilitate this, I have designed the course in a modular fashion with some optional content.
I must comment that there is nothing good or bad about being a racer or a hiker. Each of us learns different things at different rates. What is important is that you focus on the quality of your learning journey (i.e., the learning process) rather than on getting quickly to the destination (i.e., the product).
Course material
I have split most of the learning units of the first half into three categories:
- Need-to-Know,
- Good-to-Know, and
- Nice-to-Know.
You must ensure you know everything that is Need-to-Know and are comfortable with what is Good-to-Know. The Nice-to-Know bits are the icing on the cake and are optional. These are meant for those of you with a greater appetite for content.
Nice-to-Know is optional.
In each unit, I follow a conversational style where I walk you through topics and relevant code. These ‘walk-throughs’ will guide you into new concepts and skills and showcase how these ideas can be used. Finally, the exercises will allow you to use these new ideas to solve problems. The Learning Portfolio assessment involves working out all this material into a record of your learning.
Workflow
My plan is to go through all the (Need and Good) content of the material during the lectures together with you. You will have to work on the exercises during the tutorials. You must engage with the materials at a pace that suits you best. The pace at which I cover the material may not be ideal for everyone. So you are free to set this pace yourself by chosing to follow along with me or to do your own thing in or out of the lecture/tutorial. However, it’s crucial to ensure you have completed all the work by the end of Week 6.
People
Lots of individuals are involved in running this module. The most useful of them are the SPS mentors. Mentors are senior students who take on teaching responsibilities. The SPS mentor programme has been in place for over 27 years and is taken very seriously by the students and the University. It is unquestionably SPS’s most unique and valuable resource. Mentorship is not only a way to give back to the community but also an opportunity to hone one’s leadership skills. Further, the students (i.e., you) also benefit from the advice and experience of the mentors as they have trodden the path you are presently on.
SPS mentors
| Alefiya | Ang Siaw Wei | Bei Yi Yang |
| Bharath | Channe Chwa | Chen Mingyi |
| Clement Tan | Derek Ong | Ervin Chia (H) |
| Guan Xin | Hillson Hung | Jensie Low |
| Lee Kai Xiang | Lim Ting Wei | Michael Lim |
| Nandi Shao | Nemo Chen | Ng Jing Ting |
| Ryan Seow | Tee Kai Ze | Trina Tan |
| Yeow Xuek Qee | Kelissa Goh (H) | Kellie Wong |
Teaching assistants
- Lee Yuan Zhe
- Chee Onn
- Kellisa Goh
Graduate Teaching Assistants
- Ervin Chia Sheng Hin
- Timothy Yee Bing Lun
SPS mentors
| Alefiya | Ang Siaw Wei | Bei Yi Yang |
| Bharath | Channe Chwa | Chen Mingyi |
| Clement Tan | Derek Ong | Ervin Chia (H) |
| Guan Xin | Hillson Hung | Jensie Low |
| Lee Kai Xiang | Lim Ting Wei | Michael Lim |
| Nandi Shao | Nemo Chen | Ng Jing Ting |
| Ryan Seow | Tee Kai Ze | Trina Tan |
| Yeow Xuek Qee | Kelissa Goh (H) | Kellie Wong |
Teaching assistants
- Genevieve Tang and
- Lee Yuan Zhe
Acknowledgments
73 is based on an old SPS module called SP2171. It has had many reincarnations. I owe thanks for many changes to the present course to advice from Dr. Kiruthika Ragupathi of the NUS, Centre for Teaching and Learning (CDTL).
73 is based on an old SPS module called SP2171. Kelissa Goh, Darren Teo, Hillson Hung, Mingyi Chen (all from SPS batch 2019), and Yuan Zhe were heavily involved in re-inventing this old content into the first version of 73. The present version is also inspired by many ideas discussed in this process. So, I would like to take this opportunity to thank them.
The following individuals kindly helped proofread this version of the notes. I am thankful for their input.
Yuan Zhe, Bei Yi Yang, Darren Teo, Channe Chwa, Derek Ong, Hillson Hung, Genevieve Tang, Ang Siaw Wei, Bharath.
Genevieve Tang and Linda Sellou were also instrumental in putting together some of the rubrics used for assessment, or which I am immensely grateful.
References
Footnotes
We usually refer to our SPS modules with just the last two digits.↩︎