A continuación, le indicamos cómo puede superar los desafíos comunes al buscar un ascenso en Ciencia de Datos.

I was involved in a DS project in our company where we had to do the sentiment analysis of reviews of our clients. I'll explain it in the following steps: 1. The data given by sales & marketing team was in excel. 2. There were 70k entries. It had nothing like +ve, or -ve review. 3. There were duplicate entries & weird reviews. 4. First task was to make the data valuable by cleaning it. 5. Next, cleaned data should be tagged properly for sentiments analysis. This data formation required 75% time & no technical knowledge. Finally, analysis, plots, applying algorithms were just a matter of time. Only knowing the basics was required. Suggestion : Pick a dataset -}Problem Statement and work on it. And you will build the required skills.

Never let a day pass without learning something new. Small, consistent steps add up over time, gradually enhancing your skill set. What truly matters is the continuous improvement, even if the progress seems slow. The key is to apply what you've learned and showcase it through new projects. Identify challenges you face in your work and think of projects that address these issues using the skills you are acquiring. This not only reinforces your learning but also highlights your practical problem-solving abilities. Stay updated by regularly reading new research papers and articles to keep abreast of the latest advancements in the field. This ensures you're always at the forefront of innovation, ready to implement the latest techniques.

As someone who values thorough preparation, I used to believe in mastering every detail before diving into any project or task. However, as I ventured into tackling ongoing projects and collaborating with others, I discovered the power of learning on the go. Embracing this approach not only accelerated my learning curve but also allowed me to glean unexpected insights and skills that enriched my experience beyond initial expectations.

Si eres un implementador de proyectos de analítica avanzada, es importante tener en cuenta que la ciencia de datos tiene algunas dependencias técnicas tanto para el acceso a los datos que usaremos en nuestro proyecto, como para disponibilizar los resultados que se van a generar. Muchos proyectos de implementación de machine learning se enfrentan a muchas dificultades para su puesta en producción, porque el equipo técnico no domina la parte de ingeniería de datos y ciberseguridad que son requeridas para intervenir los sistemas informáticos y/o no están alineados a la normativa de gobernanza de datos de la compañía en donde se está implementando. Es importante desarrollar estas competencias para implementar con éxito tus proyectos.

I wholeheartedly agree with this perspective. In data science, your technical proficiency is your currency. To boost your chances of a promotion, it's vital to continuously expand your skill set. Master the latest programming languages like Python or R, and stay updated on advancements in machine learning and artificial intelligence. By demonstrating your commitment to personal growth and showcasing your value as a forward-thinking problem solver, you position yourself as a valuable asset to the organization. Invest in cutting-edge tools and techniques to effectively tackle complex data challenges and deliver impactful results.

I never get involved in data science projects for any company whose results cannot be estimated and measured in before and after gains. Working with analytical methods almost always has 4 affinities with the business: 1. Leverage revenue by making the most of the market or the value we deliver to it; 2. Reduce costs by making the production machine more efficient; 3. Increase productivity, doing more with less; and 4. Support decision-making at the edge to reduce strategic or tactical risks. So, make your deliverables measurable in one of these opportunities to demonstrate your skills.

Demonstrate your contributions to the company by highlighting successful projects and quantifiable results. Prepare case studies and presentations that showcase how your data-driven insights have positively impacted business decisions. Use visualizations and clear narratives to communicate your achievements to stakeholders. Consistently align your work with the company’s goals to emphasize your strategic value.

The underlying principle here is that Data Science by itself has limited value for businesses (and therefore your bosses) until you harness it for the specific domain you work (or seek to work) in. So the main thing is that while you strengthen your technical skills, you also seek to understand the domain you are working in intimately as well. Whether you work in Finance, Marketing, etc., domain expertise will almost always be as valuable as the technical knowhow. Using this approach will naturally lead you down the path of leadership in any organization you join.

As a data science expert, it's crucial to quantify your impact on the organization, Here is how I do this : 1. Predictive Models and Efficiency: For example, if a predictive maintenance model reduced downtime by 20%, highlight this improvement. Explain how this efficiency translates to cost savings or increased production capacity. 2. Data Visualizations and Decision-Making: For instance, if a dashboard you created helped executives identify a new market opportunity that increased revenue by 15%, emphasize this outcome. 3. Language of Business Benefits: Frame your achievements in terms that resonate with business leaders. 4. Direct Correlation: Draw a clear line between your work and the company's success.

Acredito que essa é uma das coisas mais importantes para os profissionais da area, uma vez que temos a tendência em ter uma personalidade mais discreta, e por vezes, podemos passar desapercebidos pelos colegas de trabalho e até por superiores. Por isso é importante que saibamos “vender nosso peixe”, deixando claro, através da nossa expertise em estatística, de mostrar os impactos provenientes do nosso trabalho, não somente na parte financeira, como também na produtividade da organização.

Use performance reviews as an opportunity to gather constructive feedback, identify areas for development, and work on them conscientiously. Seek out mentors within your organization or industry who can offer useful advice and assistance with your professional growth. By leveraging feedback and mentorship, you can continuously enhance your skills and advance your career effectively.

Continuous improvement is essential in Data Science. Regularly seeking feedback from peers and supervisors fosters professional development and signals a commitment to growth. Constructive criticism not only guides enhancement but also allows you to see things from another perspective, providing a broader understanding of your work. Demonstrating a willingness to take feedback and use it to improve shows dedication to excellence and leadership potential in the field.

Feedback is a matter of trust for both the giver and the asker. So, the first thing that needs to be done is to establish a relationship of trust. You may even be competitive to the point of resisting these moments, but you know how to read a professional who can contribute to development, so try to strengthen the relationship to the point of having mutual trust. Furthermore, it is always a challenge to read the business to accurately apply Data Science resources, therefore, your best feedback is from those who understand the business and breathe its activities and operations. Be with them.

Regularly ask for feedback from peers, managers, and mentors to identify areas for improvement. Act on constructive criticism to refine your skills and approaches. Use performance reviews as opportunities to discuss your career aspirations and seek guidance on how to achieve them. Implementing feedback shows your commitment to personal growth and professional development.

Regularly seeking feedback from peers and supervisors is one of the most effective ways to facilitate continuous improvement. Feedback provides insights into our strengths and areas for improvement that we might not have recognized ourselves. It can help us identify gaps in our knowledge or skills and provide direction on how to address them. Moreover, feedback from colleagues who have different perspectives or expertise can introduce us to new ideas and approaches that can enhance our work.

And, in this matter, it is not enough to connect on social media. If you create a real interest in the other person's trajectory and interact seeking to learn how they got there, your network will be effective for you. This attitude and disposition will put the other person in a position that what they did is important to someone, in this case, to you. This sowing comes back to you over time and will certainly bring joy to your harvest. So, your network is your seedbed. Take care of it like a good farmer. Connect with people, be interested in their achievements, ask for time to talk online or in person and look for affinities and complements in your skills.

Build and maintain a strong professional network within and outside your organization. Attend industry conferences, webinars, and networking events to connect with other data science professionals. Engage in online communities and forums. Networking can provide mentorship opportunities, expose you to new job openings, and offer insights into industry best practices.

Networking is an essential skill for any professional, but it holds particular significance in the field of data science. The dynamic nature of this industry, with its rapid technological advancements and constant influx of new methodologies, means that staying connected with other professionals can provide invaluable opportunities for learning, collaboration, and career advancement. Effective networking can open doors to new job opportunities, provide access to cutting-edge knowledge, and foster relationships that can lead to significant professional growth.

In addition to the necessary skills, having a strong professional network is also important for getting a promotion. You cannot obtain a new job or a promotion without networking. Many people nowadays, in my observation, possess talents, but networking—a crucial component of career advancement—is lacking. Thus, start honing your networking skills as soon as feasible in your professional life.

Voltamos a importância do tópico sobre desenvolvimento de habilidades, que junto ao networking são ferramentas cruciais no crescimento profissional, pois é através delas que se descobrem, e muitas vezes conseguem, grandes oportunidades, tanto acadêmicas quanto profissionais. Por isso é muito importante manter perfis e redes atualizadas, e ser engajado, num mundo conectado como o que vivemos hoje em dia, é preciso participar para “existir”.

Leading a data science project successfully requires a combination of strategic vision, effective communication, strong leadership, and a focus on delivering business value. By assembling a diverse and collaborative team, managing challenges proactively, and continuously seeking improvement, you can drive significant impact and achieve your project goals. Embrace the complexities and opportunities of data science leadership, and inspire your team to reach new heights of success.

Take the initiative to lead data science projects that align with business objectives. Volunteer for high-visibility projects and cross-functional teams to demonstrate your leadership capabilities. Successfully managing projects showcases your ability to handle responsibility, coordinate with diverse teams, and deliver impactful results. Leadership experience is crucial for career advancement.

If you make good use of the previous tips I gave, this one will be easy to follow. Data Science professionals who are a reference, seek to positively impact the business with their analytical skills, establish good relationships of trust with other colleagues and company leaders and maintain an efficient network of collaboration and exchange of ideas, are the best candidates to lead projects and guide your development in the best direction. The good always find the good! So, do a good job in the previous steps and you will have good opportunities that you will certainly not let pass your hands.

O mais importante para liderar projetos de tecnologia é se preparar para isso. O conhecimento e as habilidades técnicas são importantíssimas, mas também é preciso que sejam desenvolvidos conhecidos nas áreas de gestão de projetos e pessoas, quaisquer faltas nessas áreas podem prejudicar muito o andamento, e os resultados. Por isso faça um planejamento sobre o desenvolvimento de conhecimentos nessas áreas, quanto antes se iniciar, mais preparado você estará quando surgirem as oportunidades.

Additionally, consider contributing articles to LinkedIn, where you can share your knowledge with a broader audience. Regularly posting high-quality content can earn you recognition as a LinkedIn Top Voice, further enhancing your visibility and credibility in the field.

Develop a strong personal brand by sharing your expertise and insights through blogs, social media, and speaking engagements. Publish articles, contribute to industry publications, and present at conferences. Building a personal brand establishes you as a thought leader in the data science community and can attract career opportunities.

Going above and beyond technical abilities is essential in today quickly changing data world. Here's how to set yourself up to become a future leader: Identify Strategic Opportunities: Don't sit around waiting to be given assignments. Seek out opportunities where data science may provide the business a competitive edge. Become a Data Science Advocate: Educate and inspire others within the company about the potential of data science. Embrace New Technologies: Be an early adopter of emerging technologies in the data science field. Stay curious, experiment with new tools and methods, and share your insights with your team. Collaborate with cross-functional teams to ensure your projects support broader organizational initiatives.

Understanding the business impact is critical; focus on projects that directly benefit the organization and demonstrate how your contributions improve the bottom line. Develop strategic thinking by thoroughly understanding the company's goals and connecting your efforts with these aims. Prioritize activities that are consistent with the organization's long-term goal, demonstrating your capacity to contribute to overall success. By focusing on meaningful projects and strategic alignment, you can demonstrate your value and leadership potential, make a convincing argument for your promotion, and position yourself as an invaluable asset to the business.

Understand the company's promotion criteria and tailor your efforts accordingly. Cultivate strong relationships with key decision-makers and mentors who can advocate for your promotion. Maintain a positive attitude and demonstrate reliability and consistency in your work. Keep a portfolio of your accomplishments and regularly update it to reflect your growth and successes.