Vous recevez des commentaires sur votre travail de science des données. Commettez-vous ces erreurs courantes ?

When receiving feedback on Data Science work, avoid defensiveness, consider context, focus on actionable points, refrain from taking it personally, and follow up with proactive steps for improvement. This approach fosters a constructive feedback loop, leading to continuous growth and refinement in your Data Science practice.

To address this issue, I should have included either a fill value or a method parameter in the fillna() method call. Since you requested to fill the missing values with the mean of each column, the appropriate fix would be to use X.fillna(X.mean()).

In data science, common feedback often points to several typical mistakes: failing to fully understand the problem before diving into analysis, neglecting data cleaning and preprocessing which can lead to misleading results, overfitting models to training data without ensuring they generalize well to unseen data, ignoring the importance of feature selection and engineering, overlooking the need for thorough documentation and explanation of findings, and underestimating the importance of clear communication with stakeholders about the assumptions, limitations, and implications of the work. Avoiding these pitfalls can significantly enhance the quality and impact of data science projects.

* 🤔 Seeking feedback is crucial for personal and professional growth. It offers valuable insights and perspectives that can enhance one's skills and performance.

After receiving input from end users, I focused on problem formulation, data cleaning, exploratory data analysis, feature engineering, and model evaluation in order to avoid common data science errors and enhance their procedures.

Instead of asking for general opinions, be precise about the aspects of your work you'd like feedback on. This could be problem formulation, data cleaning choices, model selection methodology, results visualization, or code structure. Targeted questions yield more helpful and actionable feedback.

Being specific when soliciting feedback is crucial in Data Science. Vague requests yield superficial insights. Specify areas like code quality, model accuracy, or presentation clarity to elicit focused feedback. This ensures relevance and depth in the feedback received, addressing specific needs and goals. Clear guidance facilitates meaningful improvement and advancement in Data Science endeavors.

The most important aspect to collect feedback is to act upon it. And to act upon the feedback you need to listen and note down things you wish to improve/change. Here are some best practices while you seek feedback: 1. For any kind of feedback you receive, you will mark the main 2/3 pointers in a notepad 2. On weekly basis you will refer to all the collected pointers and look for any changes you would like to make. 3. At times, the feedback might be so critical that it loosens your confidence, so you should try to analyze it from every possible aspect and try to take a holistic approach. 4. Feedback sometimes can also lead you to directions not suitable/applicable for you, so here without hesitation feel free to take a second opinion.

🎧 Actively listening to feedback is essential for improvement. It shows humility and a willingness to learn from others' perspectives, ultimately leading to better outcomes.

Adopt an open-minded attitude when receiving feedback. Carefully consider the provided criticisms, aiming to understand the rationale behind the suggestions. Ask clarifying questions if needed. Even if you disagree with a certain point, actively listening shows respect for the feedback provider, encouraging a productive dialog.

Neglecting to listen to feedback is detrimental in Data Science. Embrace feedback as a catalyst for growth, despite initial emotional responses. Understand the rationale and evidence behind feedback to extract valuable insights. Respecting and appreciating feedback, even if not fully agreed upon, fosters a culture of continuous improvement. Utilize feedback to refine skills and approaches, advancing in the field of Data Science with humility and adaptability.

One common mistake in receiving feedback on Data Science work is not listening to feedback. Actively listening to feedback allows you to understand different perspectives, identify areas for improvement, and adapt your approach accordingly. By dismissing or ignoring feedback, you miss valuable insights that could help enhance the quality and effectiveness of your work. Therefore, it's essential to cultivate a receptive attitude towards feedback and prioritize incorporating constructive criticism into your Data Science practice.

The purpose of feedback is to facilitate improvement. Reflect on the feedback, identify actionable ways to improve, and demonstrate the integration of those insights into your subsequent work. This shows a commitment to self-development and responsiveness to feedback.

Failing to apply feedback hinders growth in Data Science. Utilize feedback as a tool for improvement by reviewing work and identifying areas needing refinement. Apply feedback consistently and thoroughly, testing changes and assessing their impact. Embrace feedback as a valuable resource for honing skills and advancing professionally in the dynamic landscape of Data Science.

Neglecting to give feedback deprives others of valuable insights in Data Science. Sharing constructive feedback not only aids others' growth but also refines your own critical thinking and communication skills. Offer honest, respectful feedback with specific, actionable suggestions, contributing to a collaborative learning environment. Engaging in feedback exchanges fosters professional development and strengthens the Data Science community as a whole.

🌍 Seek Diverse Feedback: Reach out across disciplines to get a wide range of insights. This includes other data scientists, domain experts, and end-users. 🔄 Avoid Confirmation Bias: Actively seek opinions that challenge your conclusions to ensure your analysis is robust. 🤝 Broaden Perspectives: Embrace feedback from different sectors and experience levels to enrich your understanding and approach. ✋ Prevent Groupthink: Encourage open and honest feedback to avoid the pitfalls of echo chambers.

Overlooking feedback from diverse sources is a misstep in Data Science. Embrace input from various stakeholders like Data Scientists, domain experts, and end users. Diverse feedback broadens understanding and exposes different perspectives, mitigating confirmation bias and groupthink. Engaging with varied sources fosters innovation and ensures solutions meet diverse needs effectively. Embrace feedback from a range of sources to enhance insights and advance in the dynamic field of Data Science.

If you get feedback from one type of source, you get a very narrow look at the process that you are using. Learn from how advanced degrees are granted. Advanced degrees are granted not by a singular thesis advisor, but an advisory committee. That committee provides different perspectives that improve one's writing and theoretical chops. Data scientists need that feedback because they cannot possibly be experts in data science across the board nor to the multiple types of disciplines they will apply their work to.