People say data science is difficult, which it is, but even harder is explaining it to other people!
Data Science itself is to blame for this, mostly because we don’t have a concrete definition of it either, which has created a few problem. There are companies promoting ‘Data Science’ tools as ways to enable all your analysts to become “Data Scientists”. The job market is full of people who took a course on Python calling themselves “Data Scientists”. And businesses so focused on reporting that they think all Analytics, Data Science included, is just getting data faster and prettier.
But the tools we use are just that, tools. The code we use requires specialized knowledge to apply it effectively. The data pipelines we create are to monitor the success and failure of our models, it’s an added bonus it helps with reporting. To mitigate these challenges we have to come up with clever phrases such as:
- “Buying a hammer does not make you a carpenter”
- “Knowing how to drive does not make you a mechanic”
- “Following the recipe on the back of cake mix does not make you a baker”
- “Owning Quickbooks does not make you an accountant”
- “Wearing a FitBit doesn’t make you a Doctor