I believe for many people like may encounter this problem:
I'm an aspiring Data Scientist who learned from Excel, SQL, Tableau, R, and Python. And when I finally landing a Data Scientist job. But things are not exactly as what I expected. I'm not building software or application.
For me, personally, I'm obsessed with data science for the cool, so-called AI, application like recommendation system, GPT-3, and so on. Then I realized the data science field is so big that on the other side, it required different skillsets! The distance of me being a Machine Learning Engineer is learning more software engineering knowledge and experience. And thanks to Jen Y. from Agoda sharing her study plan which gave me some ideas.
I'll switch from and back to MOOCs, and here to learn and practice. And below is my plan:
- Data Structure and Algorithms
- There are plenty and well-organized content for this field
- Then practice on Leetcode for algorithms part (Easy to Medium)
- Object-Oriented Programming
- Dynamic Programming
- Algorithms on LeetCode (Medium to Hard)
- Shell and Linux command
- Shell on LeetCode
- SQL Database syntax and analysis
- Database on LeetCode
- Finally, Interview Preparation
- For those who are targeting FAANG companies, LeetCode has company-specific interview preparation for premium account.
After all this fundamental consolidation, I got a final project to put everything together. To echo back the point of building software of machine learning. I will try to build a recommendation system Web Application using streamlit and Python as a skill evaluation for myself
If you feel like there can be some modification or other recommend resources, feel free to let me know here!