#Python is merely a toolbox 🧰
It's a magical bottomless toolbox but a toolbox nonetheless.
Don't get me wrong. Tools are essential, but no tool should define the data science discipline. Tools come and go, but the fundamentals of our discipline don't.
For instance, 𝗰𝗮𝗿𝗽𝗲𝗻𝘁𝗿𝘆 didn't always involve power tools like circular saws, but it ALWAYS has involved 𝘄𝗼𝗼𝗱. So carpenters must first and foremost understand wood. It's many properties such as varieties, strengths, malleability, moisture, and grain. It's limitations and applications. Not to mention the language, diagrams and math used to discuss wood.
Likewise, the skill every 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 should have is understanding 𝗱𝗮𝘁𝗮. It's properties, limitations, and applications. Also how to effectively communicate findings to all audiences.
Articles tend to confound data science tools with skills, and data science books are mostly tool-centric, not 𝗱𝗮𝘁𝗮-𝗰𝗲𝗻𝘁𝗿𝗶𝗰 nor 𝗺𝗶𝘀𝘀𝗶𝗼𝗻-𝗰𝗲𝗻𝘁𝗿𝗶𝗰. And it's no wonder why I get messages from aspiring data scientists asking me what machine learning library they should learn first — tantamount to a novice carpenter playing with a circular saw on their first day! 🤷🏻♂️