How can you continue to develop your skills after being promoted in Data Science?
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Getting promoted in data science is a great achievement, but it also comes with new challenges and expectations. You might have more responsibilities, more complex projects, more stakeholders, and more pressure to deliver results. How can you continue to develop your skills and stay on top of your game in this competitive and dynamic field? Here are some tips to help you grow as a data scientist after getting promoted.
Data science is constantly evolving, and new tools and techniques are emerging every day. To keep up with the latest developments and best practices, you need to invest time and effort in learning new skills and updating your existing ones. You can do this by taking online courses, reading blogs and books, attending webinars and workshops, joining communities and forums, and following experts and influencers. You can also use platforms like Kaggle and DataCamp to practice your skills and learn from others.
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Paresh Patil
💡Top Data Science Voice | ML, Deep Learning & Python Expert, Data Scientist | Data Visualization & Storytelling | Actively Seeking Opportunities
Advancing in data science necessitates an unwavering commitment to lifelong learning. As you ascend the professional ladder, it’s crucial to stay conversant with the vanguard of data science tools and techniques. Immersing oneself in emerging technologies, such as autoML frameworks, or delving into sophisticated algorithms enhances your analytical prowess. Additionally, mastering new programming paradigms or data orchestration platforms not only ensures a broadened skillset but also equips you to tackle more complex, high-dimensional data challenges with agility. This proactive learning trajectory is indispensable for maintaining a competitive edge in the ever-evolving data science arena.
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Megan Bloemsma
Data Scientist turned Software Engineer working at Microsoft
Also don't be afraid to learn skills that are 'outside of the field', like software engineering skills. These can help greatly in getting models into production, and collaboration with different departments within your place of work.
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Feedback and mentorship are essential for your professional development, especially after getting promoted. You can seek feedback from your peers, managers, clients, and users on your performance, your strengths, your weaknesses, and your areas of improvement. You can also find a mentor who can guide you, inspire you, challenge you, and support you in your career journey. A mentor can be someone from your organization, your network, or your industry who has more experience and expertise than you and who can offer you valuable insights and advice.
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Paresh Patil
💡Top Data Science Voice | ML, Deep Learning & Python Expert, Data Scientist | Data Visualization & Storytelling | Actively Seeking Opportunities
Ascending the data science career ladder is a testament to your expertise, yet the journey doesn’t end with promotion—it transforms. To further hone your craft, actively seek feedback on your analytical strategies and model deployments. Engage with mentors; their seasoned insights can reveal nuances in data stratification or algorithm optimization that are not immediately apparent. Peer review is equally invaluable; it cultivates a rigorous analytical acumen. Embrace this collaborative critique not as censure but as a crucible for refining your technical skills and fostering a granular understanding of intricate data narratives. This commitment to excellence through mentorship and feedback is the hallmark of a data science virtuoso.
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Bhartendu Dubey
Analyst @Sanofi | Ex-ZS | Winner-ETCS 2019-20 | CSE'21 @JIIT | IBM certified in Data Science, ML & AI
Rightly said, Having a mentor helps you in getting the right direction for your journey. This also allows you to stay focused and find a way in case you are distracted. They appreciate you for your good work and at the same time keep a note if in case there's a scope of improvement for you, which surely is the case, as getting better is a never ending journey.
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One of the best ways to develop your skills and showcase your value as a data scientist is to work on diverse and impactful projects. You can look for opportunities to apply your skills to different domains, problems, and datasets, and to use different methods, models, and frameworks. You can also seek projects that have a clear and measurable impact on your organization, your customers, or your society. Working on diverse and impactful projects will help you expand your knowledge, sharpen your creativity, enhance your communication, and demonstrate your leadership.
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Paresh Patil
💡Top Data Science Voice | ML, Deep Learning & Python Expert, Data Scientist | Data Visualization & Storytelling | Actively Seeking Opportunities
Post-promotion in data science, to further skill enhancement, pivot towards projects with diverse data ecologies and high-impact potential. Tackling varied datasets, from unstructured text to complex time-series, sharpens your proficiency in data wrangling and feature engineering. Moreover, engaging in projects with substantial business or societal impact will challenge you to refine predictive models and deploy advanced machine learning techniques like ensemble learning and hyperparameter optimization. This not only solidifies your technical acumen but also enhances your strategic thinking by aligning data insights with overarching goals, thereby cementing your status as an eminent data scientist within the community.
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Md Hasan Shahriar
Data Science | Machine Learning | MS @University of Potsdam
Working on diverse and impactful data science projects is crucial for professional growth and success. It broadens your skill set and enhances problem-solving abilities, builds domain knowledge. This enriches your portfolio with continuous learning opportunities and promotes adaptability on critical problems. This combination of diversity and impact is key to thriving in the dynamic field of data science.
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Data science is not a solo activity, but a team effort. You can learn a lot from collaborating with other data scientists and professionals from different backgrounds, disciplines, and perspectives. You can exchange ideas, share knowledge, solve problems, and create synergies. You can also leverage the skills and expertise of others to complement your own and to deliver better solutions. Collaborating with others will help you improve your teamwork, your communication, your critical thinking, and your emotional intelligence.
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Md Hasan Shahriar
Data Science | Machine Learning | MS @University of Potsdam
Collaboration is a fundamental aspect of success in data science. It enhances your learning, problem-solving, and project execution abilities, while also providing a broader perspective on the field. Collaborating with a diverse group of data scientists and professionals can help you stay competitive and ensure that your projects are well-rounded and effective. It provides diverse perspectives, fosters knowledge sharing, enhances teamwork, facilitates peer review, boosts productivity, expands your network, offers mentorship, and promotes innovation. Collaboration also enables cross-disciplinary solutions and accountability.
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David Stambrook
Retired - Seeking Adventure
Agreed. I have some private lessons too. I am now retired and would welcome being an occassional 'mentor'. Learning to navigate the 'politics' of company/enterprise dynamics and power structures adds a whole different dimension to data science leadership. I found this out the hard way. To my peril. Mind you, I was in the 'apolitical' bureaucracy of the Canadian public service. With more than a wee bit of 'bureaucratic politics'.
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Finally, you need to keep track of your achievements and goals as a data scientist. You need to document your projects, your results, your learnings, and your feedback. You need to celebrate your successes, acknowledge your failures, and identify your lessons. You also need to set realistic and specific goals for your short-term and long-term development, and to monitor your progress and adjust your plans accordingly. Keeping track of your achievements and goals will help you stay motivated, focused, and confident in your data science career.
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Md Hasan Shahriar
Data Science | Machine Learning | MS @University of Potsdam
You can maintain a portfolio of projects, datasets, and results, and document the impact of your work. It's also important to set clear objectives for your career, such as learning specific machine learning techniques, gaining expertise in certain domains, or contributing to open-source projects. Regularly reviewing your accomplishments and refining your goals is a powerful strategy for continuous growth in this dynamic field.
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Jose Alonso Caballero Márquez
PhD (s) en Ciencias de la Administración UNAM - Alfabetización Financiera / Financial Literary
Como Ingeniero Industrial e investigador, puedo dar fe de que el seguimiento permite el aprendizaje y el aprendizaje permite logros, y que esto de la mano de una adecuada planeación impulsa un proceso más organizado. Si bien, hay que ser consientes de nuestras caídas, hay que saber celebrar los triunfos. La prueba y error como estrategia de desarrollo permite formar un carácter basado en la experiencia, dando una mayor satisfacción cuando logramos objetivos.
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Saeid Aliakbar
Data Team Lead at Namafar.ir
The most effective way for me to cultivate my data scientist skills is by immersing myself in the practical challenges of everyday life, applying a data-driven methodology to address real-world problems. This hands-on approach not only transforms theoretical knowledge into practical expertise but also sharpens critical skills essential for a successful career in data science. By actively solving daily issues, I develop a keen sense of problem-solving, learn to make informed decisions based on data insights, and continually adapt to evolving challenges.
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Julian Elliott
VP Data Science, GfK | Championing Data-Driven Business Excellence | AI & Machine Learning at Scale | Transformational Leadership Advocate | Board Advisor
I advise any Data Scientist, and all those in Data Science teams that I lead, to always have a current and regularly used "individual development plan" (IDP). Sometimes called other things such as a "personal development plan" (PDP). “Invest in as much of yourself as you can. You are your own biggest asset by far.” – Warren Buffett My advice on an IDP, especially after a promotion is to revisit it entirely, every year, and to update every quarter. The IDP should be balanced across two key dimensions: 1) technical skills 2) soft skills and also have a balance between the activities you need to do your current role better, and the capabilities you need to develop to be ready for your next role. This simple framework is well proven.
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