/What_We_Learnt_From_Kaggle_Survey_2020

Kaggle Teaches Data Scientists How to Grow and Some Important Things to Note

Primary LanguageJupyter Notebook

What_We_Learnt_From_Kaggle_Survey_2020

This notebook focuses on two questions:

  1. What should data scientists think important in 2020?
  2. How to set up growth as data scientists?

Key take-aways and actions:

  1. Growth πŸš€πŸš€πŸš€
  • Focus on learning new things in the first 4 years of career. Find your job needs, build up and stick to 4-5 skills as your core 🀘🏼🀘🏼🀘🏼 to make yourself unique and standout. But don't forget to be open-minded and have some knowledge on other things.
  • Engage with the community and interact with others by sharing your work and learning from others, it is all-time important!
  1. Important Things To Note ⭐⭐⭐
  • Having at least a Master's degree is important for data-related jobs, but it's becoming less important.
  • Know Python 🐍, learn 🐍, master 🐍, and integrate 🐍 with the other languages you regularly use to build a network πŸ•ΈοΈ in your brain 🧠.
  • Free services like Google Colab and Kaggle Notebook are the must to know, but don't completely ignore pay services like Amazon Sagemaker because pay πŸ’° means better experiences and better quality, and it might be closer to the industry needs.
  • Visualization is intended to be used to deliver people's thoughts, which is beyond being fancy. It could be simple or complicate and it could be built using any tool/packages (Tableau|Matplotlib|Seaborn|Ggplot|...). However, it must be easy to understand and interactive ✨.
  1. When You Consider A Higher Annual Compensation ❗❗❗
  • Career experience is the most important aspect
    • One needs to know how to drive large business impacts using data and their skills.
    • One needs to know how to build things in large scale by knowing what different scales of companies or data teams are doing πŸ‘Ά-πŸ§’-πŸ§‘πŸΌ.
  • Skillsets and abilities is the 2nd important aspect
    • Plotly is strongly recommended! Or any other interactive package also works. And don't forget to know how to put it in production πŸ“±.
    • Get to know and apply more ides, languages, visualizations, and machine learning frameworks whenever needed. p.s. If you are having a data job in the United States, you've been on the pirate boat βš“-πŸ’°.

Interested? Check out the notebook! πŸ‘ŒπŸΌπŸ‘ŒπŸΌπŸ‘ŒπŸΌ