You’re a Data Scientist and you need feedback on your work. How can you get it?

It is imperative to get feedback on your work if you want to grow, learn, and make sure that your work is quality. Mentorship, online communication, code reviews, talking with a team member about your project, and other strategies can be used to help.

Here are 5 actionable ways that I always leverage to define my goals for seeking feedback on data science work: 1. Identify who will be giving feedback (technical expert, business stakeholder). This tailors your goal. 2. Instead of a generic "how's my work?", craft a focused question based on your desired feedback. For example, "Is my model performing as expected on unseen data?" 3 Knowing the stage of your work helps tailor the request. For early exploration, feedback on methods or data selection might be most relevant. For finalization, focus on accuracy and clarity. 4. Ensure your goals are SMART (Specific, Measurable, Attainable, Relevant, and Time-bound) 5. Before seeking feedback, jot down any assumptions made during your analysis

What kind of feedback are you looking for? Is it a technical review of your code or analysis? Do you want feedback on the clarity of your visualizations or the overall direction of your project? Knowing your goals helps you choose the right audience and frame your questions effectively.

Clearly define what kind of feedback you need. Is it technical accuracy? Domain knowledge? Clarity and communication? Choose sources with expertise that aligns with your specific needs. If your work involves a specific machine learning technique or statistical analysis, seek feedback from someone familiar with that methodology. Also, feel free to join online communities dedicated to Data Science like Kaggle or relevant subreddits. Share your work (following community guidelines) and get feedback from a broader pool of data enthusiasts.

Leverage both internal and external resources. Discuss your work with colleagues familiar with the project or domain. Consider mentorship programs within your company or explore online communities like Kaggle or Reddit's r/datascience. Attending data science conferences or meetups and presenting your work during poster sessions or talks can also be a great way to get feedback from a wider audience.

Before soliciting feedback, clearly articulate your goals and questions. Organize your work in a clear and concise manner, whether it's a presentation, code snippets and data visualizations, or a document outlining your approach and findings.

Frame your questions in a way that's easy to understand and allows for specific feedback. Be open to different perspectives and remember, feedback is meant to be constructive. Express your appreciation to those who provide their time and insights.

Present it once. and do not ask for feedback. Then go to the canteen/cafeteria wearing a cap to be completely unrecognizable, then buckle yourself up for the most unfiltered feedback while people are gulping their black coffee. People are more unfiltered when there is no accountability, leverage it, as it is going to happen anyways. Peace ??

Listen actively and pay close attention to the feedback you receive, even if it's not what you were expecting. Don't hesitate to ask clarifying questions if something is unclear.

As a Data Scientist, I will use several strategies to get an understanding of my work. Firstly, I can collaborate closely with stakeholders and team members throughout the project to gather continuous feedback. Additionally, I can present my findings in clear and understandable ways to provide valuable input. Utilizing peer reviews and seeking feedback from domain experts can also help refine my work and ensure its quality.