It can be installed on Windows, Linux, Unix, and Mac OS, and best of all, it’s free. It still requires understanding the "who," "why," and "how.Wireshark is a very popular packet sniffer. Stage 4 challenges data scientists to be good captains and decision-makers. In this stage, figuring out priorities is now the data scientist's responsibility. 🦖 Stage 4: "What?" - Becoming the leader. They actively maneuver their careers away from stakeholders who are holding them back from reaching their potential.ĭata scientists in Stage 3 pick battles wisely based on "who" and "why." Learning to read between the lines is essential for professional growth.
In this stage, data scientists become more discerning about collaborations, picking their battles wisely. Stakeholders and leaders may have reasons to withhold the truth, especially when there's a lack of alignment, so Stage 3 involves reading between the lines and understanding incentives. 🦖 Stage 3: "Who?" - Gathering info about people and their motivations. Moving from Stage 2 to Stage 3 involves not taking answers at face value. Many data professionals think Stage 2 is the final stage, but it's not. Gaining the habit of asking "Why?" before asking "How?" is a huge upgrade. Without perfect management, data scientists often end up sinking their effort into chasing poorly scoped requests. 🦖 Stage 2: "Why?" - Asking about reasons and impact before agreeing to requests. Some data scientists may not progress beyond Stage 1, which is okay if they've got a great manager but dangerous otherwise. In this stage, data scientists tackle every request with enthusiasm and eagerness. 🦖 Stage 1: "How?" - Focusing on problem-solving without questioning the context. 🦖🦖🦖 Stages of data scientist evolution 🦖🦖🦖
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