These two terms often go hand-in-hand — but they’re not the same. Here’s a quick breakdown to help you understand the difference and choose the right career path.
🔬 Data Science
Focuses on collecting, cleaning, analyzing, and visualizing data. It helps businesses make data-driven decisions. Data scientists use tools like Python, R, SQL, and libraries like Pandas and Matplotlib.
🧠 Machine Learning (ML)
A subfield of AI where computers learn from data to make predictions or decisions. ML engineers build models using algorithms and frameworks like TensorFlow, scikit-learn, or PyTorch.
⚡ Key Difference:
Data science is more about insight, while machine learning is about prediction. Data scientists tell you what happened, ML engineers tell you what will happen.
💡 We offer specialized tracks for both — so whether you want to explore business analytics or build smart AI tools, we’ve got you covered.