Why this matters
An analyst can often get away with a one off spreadsheet or a notebook full of copied cells. A data scientist's work is reused: the same model is retrained next quarter, the same survey analysis runs on the next round, a colleague picks up your project when you go on leave. Adverts reflect this. Python appears in nearly all of them, SQL in most, and the stronger ones mention clean code, Git or software design. Code that others can read, rerun and trust is part of the job, not a bonus.
What changes from analyst code
As an analyst you learned to load a file, filter it, group it and chart it. In this section you move from "it worked once on my machine" to "anyone can run it again and get the same answer". That means:
- Functions instead of copied cells. If you clean the same columns every month, write a function once and call it.
- Reshaping and joining with confidence. Real projects combine several tables: transactions with agent details, survey answers with household rosters, yields with rainfall. Joins that silently duplicate rows are one of the most common sources of wrong answers.
- Doing heavy work in the database. When data lives in a database with millions of rows, you prepare it with SQL before pulling a manageable table into Python.
- Version control and records. Git keeps a history of every change, and a short record of data sources and library versions lets someone repeat your work.