Balancing Expectations and Experiences for Data Science Beginners

"Experience is the teacher of all things." 

- Julius Caesar

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Understanding the Reality: Beyond Algorithms in Data Science

When I first thought about a career in data science, I imagined working only with algorithms, optimizing machine learning models to get the best accuracy. I saw it as a world of technical challenges. However, once I started my job at Tradom  (old name GFIT Inc.), I realized it’s much more than just model performance. After a month, I learned that while technical skills are important, understanding the company’s goals, knowing the industry, and meeting customer needs are just as crucial. Working with my colleagues is key to solving real-world problems.

In these first few weeks, I’ve used Python, Statistics, SQL, and Excel extensively for my everyday task. I also started learning advanced Linux, basic Docker commands and using Confluence, which are making our workflow more efficient as we handle multiple projects both technical and non-technical, at a time. Additionally, I’ve begun to explore  concepts related to the forex field and at the same time learning about  data security, which are crucial in this domain. 

Breaking Through Challenges and Asking for Help
One of the biggest challenges for any newcomer is dealing with the classic thought: “Oh! I’m stuck. Should I ask for help or not?” This question haunted me several times in my first month. But as I learned, the sooner you ask for help, the better your day becomes. When you cross the barrier and ask, you’ll not only save your time and effort but also make your day productive. This was a key lesson for me.
I am incredibly fortunate to ba a part of Tradom and  surrounded by supportive environment, with encouraging coworkers and a compassionate supervisor who recognize the initial hurdles newcomers face. They have not only guided me through technical challenges but have also motivated and encouraged me, whether I’m working remotely or at the office.

What you actually need as a beginner

So far, I’ve realized that being a Data Scientist is not just about technical proficiency in machine learning models. It’s about a well-rounded skill set and, most importantly, a willingness to learn and ask questions. If I were to give advice to anyone starting out, I’d say:

1. Have a solid foundation in Python, Excel, SQL, and Statistics.

2. Don’t hesitate to ask for help—whether you’re stuck on code or a business problem.

3. Be open to learning about domain-specific knowledge and understanding how your work aligns with the company’s broader goals.

In Summary

My first month as a Data Scientist has been eye-opening and rewarding. I’ve learned that success goes beyond model performance—it’s about balancing technical skills, business understanding, and teamwork. 

The key takeaway: Don’t hesitate to ask for help; it saves time and accelerates growth.
I’m excited for the next month of learning and tackling new challenges.