What do you do if your data science internship supervisor is not impressed with your work?

Seeking detailed feedback from your supervisor is crucial when receiving critiques on your work during an internship. Requesting specific examples of areas needing improvement helps you focus on enhancing those aspects effectively. Constructive criticism provides valuable guidance for your professional development, aligning with the learning goals of your internship. Understanding and acting upon feedback are essential steps in growing as a data scientist and contributing effectively to projects.

* Schedule a meeting with your supervisor to discuss their specific concerns. Actively listen to their feedback and ask clarifying questions. * Once you understand the areas needing improvement, discuss specific steps you can take to enhance your work. This might involve proposing revisions to your current project or suggesting alternative approaches. * Research additional techniques or resources relevant to the project. This demonstrates your proactiveness and willingness to go the extra mile. * Expanding your skillset can be highly beneficial. Explore online courses, tutorials, or books related to data science concepts that could complement your current project. This shows your eagerness to continuously learn and improve.

One thing that I found very useful is communication and being honest about the shortcomings. A good supervisor will know you’re just getting started and mistakes are a way to learn. However in a scenario that has brought a sense of disbelief as a junior data scientist you should ask for feedback with a lens to improve. “If you were me what would you have done better” ? Explain what led to the shortcoming in all honesty. Show improvement with every feedback. Learn and apply. Be mindful, excited and always giving your best. That way even if you still fall short you can rest assured that you have done your best and it’s the only thing that matters even if your supervisor is unhappy about your work.

Response to negative feedback should be viewed as an opportunity for learning and continuous improvement, the two essential elements of a successful career. Seeking a detailed feedback and identifying areas of improvement is crucial. Communication is the key element here. Knowing exactly what needs improvement is the first step of the process of self growth.

It should not be about impressivness. The benefits one can get from the results must have been ignored. Make a result oriented model, do not fail to highlight the possible benefits in terms of direct costs and long term gains. Run the model for past paroject if you can get hold on old stuff and try to make a point !!!.

Reflecting genuinely on the feedback you've received serves as a cornerstone for both personal and professional development. It involves an introspective examination, acknowledging any gaps or areas in need of enhancement, whether they pertain to grasping project intricacies, mastering challenging concepts, or honing specific skills. By delving into the root causes behind the feedback, you pave the way for crafting tailored and effective strategies to overcome deficiencies. This commitment to self-awareness and improvement not only enriches your data science journey during the internship but also sets a powerful precedent for lifelong learning and growth in your career.

Reflecting honestly on the feedback received is crucial for personal and professional growth. Acknowledge any knowledge gaps or areas needing improvement, considering project requirements, challenging concepts, and skill development. Understanding the root causes behind the feedback helps in crafting actionable plans to address shortcomings effectively, fostering continuous learning and improvement in data science skills during the internship.

Reflecting honestly on the feedback is essential. Evaluate if you fully grasped the project requirements and if any data science concepts were challenging. Determine if the issues stemmed from misunderstanding, insufficient effort, or skill development needs. This introspection enables you to address weaknesses effectively and make meaningful progress in your internship.

Take a step back to reflect on your performance and objectively evaluate the quality of your work. Identify any areas where your skills, knowledge, or approach may have fallen short of expectations, and acknowledge any mistakes or shortcomings. Consider seeking input from colleagues or mentors to gain additional perspectives on your work and areas for improvement.

Engage in honest self-reflection regarding the feedback received. Assess whether there were any gaps in understanding project requirements or challenges with data science concepts or tools. Determine if issues stemmed from effort, comprehension, or skill development needs. Recognizing areas for improvement fosters personal growth and enables targeted efforts to address shortcomings in future projects.

After identifying areas for improvement, it's essential to take proactive steps to enhance data science skills. This can include enrolling in online courses, practicing with relevant datasets, and seeking mentorship. Improving technical abilities like programming, data visualization, and statistical analysis not only impresses supervisors but also strengthens long-term career prospects in the field of data science.

Improving your skills is key. Consider online courses, practicing with relevant datasets, or seeking mentorship. Enhance technical skills like Python or R programming, master data visualization tools, and deepen statistical analysis abilities. These efforts not only impress your supervisor but also bolster your future career prospects in data science.

Develop a targeted plan to address the feedback and areas of improvement identified by your supervisor. Invest time and effort in enhancing your technical skills, domain knowledge, and problem-solving abilities through self-study, online courses, or practical projects. Seek opportunities to apply your newly acquired skills and knowledge in real-world scenarios, such as volunteering for challenging tasks or collaborating on cross-functional projects.

Improving one's skills is crucial, but it is essential to ensure that the skill you are learning is relevant and aligned with your career goals. This may involve taking online courses to sharpen your skills, practicing with datasets similar to the ones you are currently working on, or finding a mentor to provide additional guidance. Enhancing your technical skills, such as Python and R, data visualization tools like Tableau, or statistical analysis, is not only about impressing your supervisor but also about building a solid foundation for your future career in data science.

Dedicate time to improving your data science skills, addressing identified areas for enhancement. Consider enrolling in relevant online courses, practicing with datasets akin to those encountered during the internship, or seeking mentorship from experienced professionals. Enhancing proficiency in technical domains like Python or R programming, data visualization tools such as Tableau, or statistical analysis will not only impress your supervisor but also bolster your future career prospects.

In my opinion, people often fail to succeed in internships not because of skills but because of lack of context. An internship is a short period where you need to learn as well as deliver and we often fall short on the learning aspect. What frequent communication can do is bridge that gap. And don’t only connect with uour supervisor but with your peers and others around you as well to get as much context about the company, the project, the deliverable, etc.

Communicating proactively is key. Keep your supervisor informed about areas needing improvement and share your plan for addressing them. This demonstrates initiative and a commitment to learning. Regular check-ins ensure you're on track and provide ongoing guidance in a learning environment.

Maintain open and transparent communication with your supervisor throughout the improvement process. Provide regular updates on your progress and share any insights or learnings gained from your efforts to address their feedback. Proactively seek additional guidance or support from your supervisor or other mentors within the organization to accelerate your growth and development.

retune the internal communication with supervisor with proper explanantion and define how we can move to the forward with improvening the skills

Maintain a weekly objective, plan ahead for the week by writing down a list of goals, identify and communicate your willingness to learn. Have regular check-ins with your supervisor, make sure you have the right mindset. Being proactive rather than reactive is always better!

Continuously monitor your progress and track the impact of your efforts in addressing the supervisor's concerns. Showcase tangible examples of improvement through the quality of your work, the efficiency of your problem-solving process, and the positive outcomes achieved. Actively seek opportunities to demonstrate your commitment to growth and development, and showcase your willingness to learn from feedback and adapt to evolving expectations.

If your data science internship supervisor is not impressed with your work, it's crucial to first understand their feedback and incorporate their suggestions for improvement. ? Focus on implementing specific changes they've suggested and approach your projects with renewed attention to detail. ? Demonstrating your adaptability and ability to respond positively to feedback showcases your commitment to personal growth and professionalism, which are highly valued traits in any professional setting.

What I find helpful is that working on supervisor feedback since attention to detail is needed to understand all intermediate steps in between data ingestion, data processing, output generation and deploying a machine-learning model. The data science internship helps in acquiring a deeper understanding of the entire data pipeline that could be working on building models as one of the interesting aspects of the role & working on iterative processes of prototyping, testing models. It is important to understand that to excel in the data science internship, the intern needs to fully comprehend the data science space in building state-of-the-art neural networks, work on cutting-edge technologies & machine-learning algorithms.

Learning must happen deeply. When you encounter a challenge and take steps to solve it, you will gain new knowledge at the end of the road. This new knowledge should accompany you in your next project and work. This is the meaning of "Apply Changes".

Apply the lessons learned from feedback by implementing suggested changes in your work. Demonstrate adaptability and a commitment to personal growth. By addressing specific areas for improvement, you show your ability to respond positively to feedback, a trait highly valued in any professional setting.

Maintain a detailed record of the feedback received, actions taken for improvement, and resulting outcomes. Documenting progress not only reinforces personal growth but also provides tangible evidence of development. This record serves as a valuable resource for future opportunities, showcasing dedication to continuous learning and improvement in data science.

Se n?o há um costumeiro feedback por parte do líder a falha já come?a nele antes de entrar no mérito do seu trabalho, mas pulando essa parte, procure pelo feedback da "lideran?a" se ele n?o vem até você. Procure sempre saber como seu trabalho está sendo visto e avaliado e como tem ajudado no andamento do projeto. Creio eu que em Ciência de Dados o feedback deve ser continuo. Decis?es s?o tomadas a partir das coletas e Insights. Dependendo da metodologia de projetos adotada esse feedback pode ser diário ou quase nunca.

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? Você n?o é um "sabe-tudo"! Leve essa verdade pra si. Lembre-se, como estagiário, sua fun??o é aprender e você realmente chegou somente com a no??o básica da faculdade (o que equivale a nem 50% da vivência corporativa). Aproveite também e pegue um tempo para aprender com o ambiente a sua volta. Converse e ou?a colegas da área. Muitas das vezes já enfrentaram problemas parecidos e podem te ajudar a ter uma vis?o ampla da situa??o.