A continuación, te explicamos cómo puedes abordar eficazmente las críticas de tu jefe sobre tu trabajo en la ciencia de datos.

Listen actively, seek clarification on specifics, and acknowledge valid points. Develop an action plan for improvement, demonstrate willingness to adapt, and follow up with progress updates to show commitment to excellence.

To effectively address criticism from your boss on your data science work, first, listen carefully and understand their concerns. Ask clarifying questions if needed. Acknowledge the feedback and demonstrate a willingness to improve. Review and analyze your work to identify areas for enhancement. Implement suggested changes and offer solutions or alternatives. Follow up with your boss to show progress and ensure their concerns have been addressed, fostering a collaborative approach.

For me the number one rule is "be open to the possibility that he is right and I need to do differently" Sometimes this is not easy as overloaded manager or too boldisbh colleagues do not see the full picture. But still there could be a space for improvement on my side. I usually also start working to improve relationship with my boss. My goal is either learn how be better, or agree with boss subsequently that the person who criticized me didn't deliver more and was too boldisbh and less experienced than his/her words.

When receiving criticism from your boss on your data science work, listen attentively to understand their specific concerns. Avoid becoming defensive and focus on learning from their feedback. This will help you identify areas in your project that need improvement, such as your analytical approach, algorithms, or data visualization.

When your manager criticizes your data science work, listen carefully to comprehend their precise concerns. Avoid being defensive, instead focusing on learning from their feedback. This will assist you in identifying aspects of your project that require work, such as your analytical strategy, methods, or data visualization.

To effectively seek clarification, follow: 1. Ask for specifics: Request precise examples where the analysis didn't meet expectations. For instance, if criticized for accuracy, ask which specific findings were incorrect. 2. Discuss benchmarks: Clarify what standards or benchmarks are used for evaluating your work. For example, ask if there's a specific model performance metric, like AUC or accuracy, that you should target. 3. Timeline concerns: If time constraints impacted the quality, discuss realistic timelines for projects. 4. Feedback methods: Suggest regular feedback sessions to catch issues early. For example, propose bi-weekly reviews of ongoing projects to adjust approaches swiftly.

Early in my career as a junior data analyst, I encountered a challenge when my boss questioned the validity of my Marketing Mix Model (MMM) due to concerns about multicollinearity. I knew the first step was to clarify any doubts. I took the time to explain to my boss what multicollinearity is, how it can distort the results, and why it’s a common issue in MMM. By walking through the data and showing how certain variables were highly correlated, I ensured that my boss understood the problem, which helped us have a more productive discussion.

The text emphasizes the importance of seeking clarification on any unclear points during feedback sessions. It suggests asking questions about specific aspects of your data science work that your boss is critiquing. This helps ensure that you fully understand the feedback and can address it accurately in your revisions.

Ways to objectively assess criticism: 1. Align with standards: Compare the feedback against industry standards. For instance, if criticized for data cleaning, review whether you followed recommended practices like handling missing values and outliers. 2. Evaluate methods: Assess whether your methodologies are up-to-date. For example, if your model validation is questioned, check if you used cross-validation to prevent overfitting. 3. Check outcomes: Look at the results' impact. If criticized for poor outcomes, analyze whether the predictions significantly diverge from expected trends. 4. Consult peers: Seek a second opinion from colleagues.

The text emphasizes the importance of objectively assessing criticism. It suggests taking a step back, removing personal feelings, and evaluating the validity of the points made in relation to best practices in data science. This objective reflection helps determine which criticisms are constructive and should be acted upon to enhance the quality of your work.

After addressing the initial doubts, I carefully planned my response to the multicollinearity issue in our Marketing Mix Modelling (MMM). From my previous experience, I saw this as a learning opportunity and sought advice from experienced colleagues. MMM often involves multiple marketing channels, and multicollinearity can distort the model’s accuracy. I conducted a Variance Inflation Factor (VIF) analysis to identify problematic variables, such as overlapping media spend. I then explored options like removing or combining these variables or using regularisation techniques like Ridge or Lasso regression. I outlined these steps in a plan and discussed it with my boss to ensure alignment.

Once you have reflected on the feedback, plan your response. Consider how you can address the concerns raised and what specific actions you can take to improve. Formulate a strategy that outlines the steps you will implement moving forward. This plan will demonstrate to your boss that you are taking their feedback seriously and are committed to making positive changes.

Once the plan to address multicollinearity in our Marketing Mix Modelling (MMM) was approved, I moved forward with implementing the necessary changes. MMM often involves various marketing channels, and multicollinearity can obscure the true impact of each channel. Using the insights from the Variance Inflation Factor (VIF) analysis, I carefully removed or combined variables that were contributing to the issue. I also applied regularisation techniques to further minimise multicollinearity’s impact. After re-running the model, I presented the updated results to my boss, highlighting the improvements. This experience reinforced the importance of constructive feedback and my commitment to delivering accurate, reliable results.

Ways to effectively communicate: 1. Summarize understanding: Start by summarizing the feedback to confirm your understanding. For example, say, "I understand the concern is about model overfitting." 2. Detail your approach: Clearly explain the steps you will take to address the issue. For instance, "I plan to implement L2 regularization to reduce overfitting." 3. Use visuals: Support your explanations with charts or data visualizations. For example, show a graph of model performance before and after regularization. 4. Propose timelines: Outline when changes will be made.

Stellen sie sich darauf ein, die Erwartungshaltung des stakeholders zu adressieren und dieser zu entsprechen. Das heißt man das man aus relativ abstrakten Datenanalysen die richtigen Business Rückschlüsse ziehen muss. Vereinfachen Sie die Inhalte und fokussieren sie sich auf die Ergebnisse,und Vorgehensweisen zur possitiven Beeinflussung.

Steps to effectively implement changes: 1. Prioritize adjustments: Identify the most critical areas for improvement first. 2. Document changes: Keep a detailed log of what modifications were made and why. This could include noting the switch from a logistic regression model to a random forest model to improve classification accuracy. 3. Test rigorously: After implementing changes, thoroughly test your new models or data processes. For instance, use a validation set to assess the performance of the new algorithm. 4. Report results: Share the outcomes with your boss. For example, present a comparison report showing improvements in model accuracy before and after tuning hyperparameters.