Discussion Road to AIAP: An Odyssey from Finance to AI
Some of these key skills include:
- Technical Skills: My experience with Bloomberg Query Language (BQL) was particularly helpful. BQL allowed me to perform custom calculations, manipulate and retrieve data in the Bloomberg cloud, which was similar in function to SQL. This early exposure to a form of programming language piqued my interest and provided a solid foundation for learning other programming languages commonly used in data science, such as Python and R.
- Analytical Skills: Working in finance, I gained strong analytical skills by constantly examining financial data, identifying trends, and making data informed decisions. These skills have been transferable to data science, where the ability to analyse data, recognise patterns, and draw meaningful insights is essential.
- Soft Skills: My finance background also helped me develop valuable soft skills that have been useful in my transition to data science. For example, I frequently had to present my findings to stakeholders, which honed my communication and presentation skills. Additionally, managing tight deadlines and juggling multiple tasks in a fast-paced environment has strengthened my time management and prioritization abilities. These soft skills have also proven to be invaluable in the data science field, where effective communication and efficient time management are crucial for success.
- Take the first step: Embrace the change and remember that a journey of a thousand miles begins with a single step. It's natural to feel apprehensive, but taking that initial leap is crucial to embarking on your learning journey. Set realistic goals and be patient with yourself, understanding that progress will come with time and consistent effort.
- Don't feel overwhelmed by the vast amount of information available: With countless resources at your disposal, it's easy to feel inundated. To avoid information overload, carefully select a few reliable resources for mastering the basics, and stick to them as you build your foundational knowledge. As you grow more comfortable with core concepts, gradually expand your resource pool to include more advanced topics, ensuring a well-rounded and manageable learning experience.
- While learning data science concepts is crucial, mastering the art of learning is equally important. Consider watching Dr. Barbara Oakley and Dr. Terrence Sejnowski's course, "Learning How to Learn," as efficient learning techniques will help make your journey smoother.
Thanks @meldrick_wee for your detailed sharing.
Python version of Hastie's intro to statistical learning coming in Summer 2023!!!! An Introduction to Statistical Learning (statlearning.com)
Outcompute to outcompete | Growing our own timber
Thank you for sharing your journey with us! It's amazing to see how you leveraged your finance background and tackled the challenges that came your way. I truly believe that there are many transferable skills between the different disciplines. Besides Data Science is a field where having domain knowledge from other fields can really make you shine.
I enjoy reading your sharing, keep up the good work!
Thanks for sharing Meldrick!
Being schooled in Accounting, I can empathise that the road to AI is challenging for anyone without some sort of technical background. Personally it took me 1.5 years to do a Master degree in Analytics followed by another 2-3 years of MOOC courses to get some semblance of a mathematical / statistical / computing foundation to unravel the complexities of AI concepts and apply them with a degree of understanding.
Similar to Meldrick, my biggest takeaways to AI aspirants are to:
- Count the cost: Know that you are in for a learning journey where you may take months to years to get a foundation, and extends indefinitely into the future.
- Know where to look: It is essential you curate different learning sources to learn different topics (shoutout to all of Prof Andrew Ng's courses, RitvikMath, and StatQuest).
- Organisation: Plan and prioritise your learning tasks in a queue / to-do system.
- Know when to stop: There are many rabbit holes, but not every one leads to Wonderland. It may be better to grasp the intuition of the math behind an algorithm instead of rigorously proving it because rabbit holes easily detract you from your main focus.
- Get your hands dirty: Learning by doing often gives a different perspective. Additionally, this gives you a tangible output which is superior to head-knowledge.
- It is okay to not know: You got to not know before you know, you know? Embrace continuous learning. Say "No" to Imposter Syndrome and learn at your own pace.
With the right amount of curiosity to explore new concepts, organisation to prioritise your approach to learning, and grit to scale the many steep learning curves, the world is your "AIster"!
@laurenceliew Hahaha finally the Python version!
I write and I code. Sometimes I pun.