Discussion Road to AIAP: An Odyssey from Finance to AI

Hi everyone! Today, I'm excited to share my personal journey of transitioning from the finance industry into the exciting field of data science. Before diving into my story, I want to mention that the purpose of this forum discussion is to share my experiences, insights, and the challenges I faced while preparing for the AIAP and transitioning from a career in finance to data science. I hope my journey can inspire and encourage others who are considering a similar change. I'm also eager to learn about the unique backgrounds of everyone here and how you navigated your own transitions. Please don't hesitate to share, ask questions or seek advice in this thread, as I'd be more than happy to help and share my experiences with you all.

A little about me: I hold a degree in finance and have worked in risk management at an investment management firm. My main responsibilities included calculating credit and derivative risks, along with managing various operational risks. Over time, I started feeling unchallenged in my role, as if there was no room for growth or skill development. I longed for an opportunity to create something and expand my expertise. Furthermore, the hierarchical reporting structure in my previous role left me feeling limited in terms of creative problem-solving. These factors led me to explore new avenues, and that's when I came across the dynamic field of data science.

Now that you have a glimpse of my motivation to transition from finance to data science, let's take a closer look at the starting point of my journey. There are definitely some aspects of my finance background that have proven beneficial in my journey towards becoming a data science practitioner. My finance background provided me with several valuable skills that have been advantageous in my journey.

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.

Now that I've shared the transferable skills from my finance background, let's dive into the next phase of my journey: preparing for the actual transition into data science. I'll discuss the resources and strategies I used to build my skills and knowledge, setting the stage for my future success in data science and AIAP.

To create a structured learning plan, I began with the AI Apprentice Field Guide as a comprehensive starting point. My advice is to follow the guide while allowing your natural curiosity to lead you in exploring various sub-topics. Don't feel constrained by just following a guide; remember that the field of data science is rapidly evolving, and being open to exploration is a valuable skill in itself.

To build my data science foundation, I utilised a wide range of resources. Some of my go-to resources included AI Singapore’s LearnAI courses, Machine Learning Mastery and Analytics Vidhya blogs, Andrew Ng's machine learning specialization and range of courses, and Josh Starmer's StatQuest YouTube channel. Additionally, I frequently turned to the Stack Overflow forum for guidance and followed the book "An Introduction to Statistical Learning in R." Although the book’s code snippets were in R, I could easily find Python translations. I focused on Python and SQL, with some R on the side. I frequently used libraries such as Seaborn for visualizations, Scikit-learn for classical machine learning, and TensorFlow for deep learning. I also experimented with other tools like GitHub, Docker, Tableau, Pandas Profiler, and Streamlit to expand my skillset further.

One of the most effective ways I gained practical experience was by working on hands-on projects, primarily through Kaggle. I learned by emulating others and analysing how they approached data science problems. It's essential to dive into projects as early as possible and avoid getting stuck in a loop of watching tutorial videos. While I faced numerous challenges in my projects, grappling with these difficulties provided me with the most valuable insights and growth to hone my technical skills. Cultivating resourcefulness in finding solutions to problems is a crucial skill for data science practitioners, so it's essential to develop this ability as early as possible in your journey.

Reflecting back on my own journey, here are some additional insights and pieces of advice I'd like to share with those considering a similar transition.

• 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.

Embarking on a transition to data science can be an incredibly rewarding experience. My journey has been full of challenges and growth opportunities, and I'm excited about the future in this dynamic field. I hope my story and advice can inspire others who are considering a similar change. Remember, with determination, curiosity, and a willingness to learn, you too can successfully navigate this transition and unlock your hidden potential. Good luck!