I've started learning Python, but the sheer number of Data Science tools is overwhelming. Should I focus on Scikit-Learn, or is it better to jump straight into PyTorch and TensorFlow for modern industry roles? Looking for advice from working professionals on what is actually used.
3 answers
For anyone starting out, Scikit-learn is the absolute gold standard for traditional machine learning. It covers regression, classification, and clustering with a very consistent API. You should master it before moving to deep learning frameworks like PyTorch. In the industry, many production models are still gradient-boosted trees, so learning XGBoost or LightGBM is also highly beneficial. Don't forget Pandas and NumPy for data manipulation, as you will spend 80% of your time cleaning data rather than actually training the models. High-quality data is always more important than the specific library.
Which of those libraries would you recommend for someone working primarily with time-series forecasting in a retail environment? Are there specific tools for that?
Start with Scikit-learn for the fundamentals. It teaches you the proper workflow of splitting data and evaluating metrics which is vital for any Data Science project.
Totally agree with Karen. Jumping into Deep Learning without knowing how to properly validate a basic model is a recipe for disaster in a real-world project.
Steven, for time-series specifically, look into Prophet or Statsmodels. While Scikit-learn can do it, these libraries are built to handle seasonality and trends much more naturally. They save you a ton of time on manual feature engineering. You might also want to look at Darts, which provides a unified interface for many different forecasting models, making it much easier to compare results.