
edXPython is the lingua franca of data science, but learning it wrongly wastes time. This course goes straight to what matters: reading and writing working code, understanding functions and loops, and then applying that foundation to real data problems with Pandas, NumPy, matplotlib, and SKLearn.
You'll also learn statistical thinking—probability, recognizing statistical fallacies, and avoiding the ways data gets misused. That context is crucial. Python is just the vehicle; the real skill is thinking clearly about data and asking questions that data can answer.
Best for: Career-changers entering data science, analysts wanting to code, and anyone building machine learning models.
How to read and write Python code. ,Functions, arguments, and return values; variables and types; conditionals and Boolean expressions; and loops. ,Basic problem-solving mechanism using probability and statistics. ,An ability to recognize common fallacies in probability, as well as some of the ways in which statistics are abused or simply misunderstood. ,Applications of Python programming for data science, using popular libraries such as Pandas, numPy, matplotlib, and SKLearn. ,How Python serves as a foundation for machine learning and artificial intelligence.
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