As a student at the Institute for Advanced Analytics, the use of AI is encouraged where allowed. It can be used to help study or understand a topic, summarize an article, help fix code errors, and much more.
AI is not here to replace data scientists, but to help them.
Here are three examples of how I have used AI in my studies and personal endeavors while at the Institute:
Studying for a Test
There are a lot of topics covered in a very short amount of time. At the IAA, you have to balance tests, homework, and practicum. It can get quite busy at times. Notebook LM was one of the first things I tried to aggregate all the materials I had from class to make studying for tests easier. The result is a beautiful study guide that has formulas, concepts, and examples.
Taking it one step further, I am able to ask Gemini or ChatGPT to quiz me on those concepts using open response answers, multiple choice, and True/False. It will give feedback based on your answers, and all you have to do afterwards is understand the feedback and ask another set of questions. Though this is great, you always have friends and classmates who are going through the same program to help you as well.
Research and Summarization
Though a lot of topics are covered in this program, it’s still on me to explore more options and techniques to assist me in practicum, homework reports, and future endeavors. AI is a powerful tool that can help streamline the research and comprehension of scholarly data science related articles.
For example: Following class, I wanted to ask my teacher a question about an idea I had about what we learned in that lecture. I got an answer, but it was on me to further investigate what was said during that conversation. Using AI to help research the topic was great, but it was even better for summarization or simplifying some of the more complicated sections.
The idea was on Bayesian Model Averaging, a method that combines predictions from multiple models by weighting each model’s contribution according to how likely it is given the data. Additionally, while researching, AI was able to give me an alternative called Model Stacking. Both were really cool techniques that might just be useful later on. Again, this was an amazing example and experience with AI. Keep in mind that you are always able to sit down with your knowledgeable professors and ask questions outside of class time.
Fix Code Errors or Writing Code
Fixing code errors is a pain. There were many times where I was writing C++ back in the day and just had to tough it out and look back to see if there was a semicolon missing or look on Stack Overflow for hours. Not anymore. AI is really great for stuff like this. It notices things like this easily and can give a variety of solutions extremely quickly, and from my experience, they work a lot of the time.
Additionally, it can help write really niche code portions that may require an even nicher solution. While fixing errors is a great use of AI here, you have to be careful with writing code. AI will write code that is optimized for efficiency. One example of this is regular expression patterns. Regular expression patterns are powerful tools used in code for pattern matching and manipulation of text, but by no means easy to read to those unversed in the language. I actually only know about this because AI wrote code for me using these regular expression patterns. I had to do my own research to understand what it had just written for me. Part of being a good programmer and data scientist is to make your code readable for other people. If things like those regular expression patterns are really needed, be sure to comment in your code about it. This is extremely important, as code comparison and collaboration with others is extremely common and helpful at the IAA.
In the end, AI has been a helpful companion throughout my time at the Institute.
AI doesn’t replace the effort required here, but it definitely makes the workload more manageable and the learning experience that much better.
Whether I’m studying for a test, diving into a complex research topic, or debugging a stubborn piece of code, AI has been there to lighten the load a bit and point me in the right direction. It’s also pushed me to learn more deeply by giving me new angles and ideas I wouldn’t have considered on my own. But even with all these advantages, the real strength of the IAA still comes from the classmates and professors around you. Using AI responsibly just adds another tool to that onto that already great support system.
Columnist: Seamus Flaherty