What are the most important data metrics to track in your career?

The true pedigree for a career in Data Science is the depth and breadth of Skills possesed by a person. It is not about writing many tools in your resume but practical hands on usage and application of tools to demonstrate skills is absolutely necessary. It is always advisable to have a portfolio of projects(on github or others) readily available to showcase the richness of your skills.

Learn the Big Picture Successful Data Scientists deeply understand the core of the business, the task at hand, and anticipate potential complications in a project before writing a single line of code. A Scientist’s career usually follows these steps: 1. Learns the tools: coding, stats, data wrangling, and becoming proficient in their use, advantages and scope. 2. Learns workflow organization: Scientist understands project, desired outcomes or benchmarks and learns efficient ways to achieve them. 3. Learns the core of the business: Scientist identifies business needs, defines projects, anticipates outcomes and delegates tasks. At this level, they easily migrate workflows and easily adapts to challenges.

The best metric for assessing your skill level in a particular domain is if you're able practice "Feynman's technique" effectively, i.e, are you able to explain to explain the most complex topic in your domain to a non-technical audience or better yet, to a fifth grader. The other measure of success is "Interleaving" - are you able to interleave a subject you're 95% proficient in with another complementary subject, in which you're 70% proficient in - and able to create new avenues of value for yourself and for the organization you're working for. examples of this skill would be - "95% proficient in Data Science and 70% proficient in Product Management" or "95% proficient in coding + 70% in Analytics"

In terms of skills level, the most important data metrics to track in your career include: Skill Proficiency: Assessing the level of expertise in specific skills relevant to your role and industry. Skill Diversity: Monitoring the range of skills acquired, ensuring a balance between specialized and generalist abilities. Skill Application: Measuring the practical application and effectiveness of your skills in real-world projects or tasks. Learning Progression: Tracking the progression in learning new skills, including the completion of courses, certifications, or training programs.

Skill level can directly impact your ability to come up with solutions or know how to solve problems in your business. But there is a trade-off between learning, gaining experience and actually creating value. Often data scientists can get caught in trying to improve a model by less than 1%, however business impact should always be your first priority. As well as helping the business track and understand the outcomes. I personally believe that your performance criteria in your career should be directly measured by the changes and improvements created in the business by projects you drove or participated in. It doesn’t help that you’re the best modeller and have never implemented a project in the real world. Focus on creating business value.

A few thoughts about project outcomes for mechanical engineers: 1. How are the outcomes, particularly, quantifiable metrics compared to the trend and standard in your field? 2. What professional development have you got from the projects? 3. Public accessible outcomes, e.g., publications, patents, etc.

When focusing on project outcomes in your career, the most important data metrics to track are: Project Completion Rate: The percentage of projects completed on time and within scope. Budget Adherence: Monitoring how closely the actual spending aligns with the allocated budget. Quality of Deliverables: Measuring the quality of project outputs against predefined standards or client expectations. Stakeholder Satisfaction: Gathering feedback from stakeholders, including clients, team members, and management, to assess satisfaction with the project outcomes. Return on Investment (ROI): Calculating the financial or value-added return generated by the project.

Apart from traditional KPIs, you can use more specific quantitative metrics like model accuracy, recall, precision, F1 score, or ROC-AUC for classification problems. For regression tasks, metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), or Mean Absolute Error (MAE) are relevant. Tailor these metrics to the specifics of each projrct Align your project outcomes with business goals. If your project is related to customer retention, track metrics like churn rate reduction or customer lifetime value increase Conduct a cost-benefit analysis for your projects. Calculate the return on investment (ROI) by comparing the benefits (increased revenue, efficiency improvements) against the costs (com resources, labor hours)

If possible, I would recommend creating a GitHub repository for each project. It serves as a great platform to present your work and maintain organization over time. A good GitHub profile often holds more significance than a good CV. Adding a Graphical User Interface to your best projects extends accessibility to both technical and non-technical people. To do so, numerous user interface libraries and frameworks offer free hosting plans for personal projects.

Es fundamental rastrear el impacto de tus proyectos. Define KPIs que reflejen los objetivos específicos de cada proyecto, como mejoras en la eficiencia operativa, incremento en la retención de clientes, o aumento en la tasa de conversión. Monitorea estos KPIs para evaluar el éxito y el valor que aportas. Además, recolecta feedback, testimonios y reseñas de clientes o usuarios para obtener una perspectiva cualitativa de tus proyectos. Esta combinación de análisis cuantitativo y cualitativo te brindará una comprensión integral del impacto de tu trabajo y te ayudará a identificar áreas de mejora y oportunidades de crecimiento profesional.

In the context of your professional network, the most important data metrics to track are: Number of Connections: The total count of individuals in your network, including colleagues, industry peers, mentors, and professionals from diverse fields. Diversity of Network: The variety of industries, roles, and expertise levels represented in your network, ensuring a broad range of perspectives and opportunities. Engagement Levels: Frequency and quality of interactions with your network, including meetings, calls, and meaningful conversations. Referrals and Introductions: Tracking referrals received and given, as well as introductions to new connections, which can be indicators of the trust and value you hold within your network.

LinkedIn has been the #1 medium followed by Meta, twitter (X), Tiktok for networking for me. However, building network happens best when you help people without any other expectation that helping them be unblocked in their career or just by talking to them when they need someone to talk to. One thing you can ask for is to pay that advise or help forward . The community you build with that model has a network effect of trust that not only helps each other grow in careers, lives but also does that by creating a safe and happy space .

One of the key qualities that all Data Scientists need to develop while networking is “Give before you take”. What it means is, you need to engage and understand from the experts what they’re doing, where they are facing issues, before jumping ahead with your problems. This helps to make lifetime meaningful connections.

Check 1. Linkedin 2. Twitter 3. Medium/Towardsdatascience 5. Quora 6. Data Science community meetups 7. Github All these websites will help you create connections in datascience.

La red profesional es una métrica crucial. Utiliza LinkedIn para exhibir tu experiencia y proyectos, interactuando activamente con otros profesionales. Twitter es excelente para seguir tendencias y unirte a discusiones relevantes. GitHub sirve para demostrar tus habilidades técnicas y colaborar en proyectos. Además, asistir a conferencias y webinars te permite expandir tu red y mantenerte actualizado. Mide el crecimiento de tu red no solo en números, sino también en la calidad de las conexiones y la relevancia para tu carrera. Estas interacciones pueden ofrecer oportunidades de aprendizaje, colaboración y crecimiento profesional.

Crafting and nurturing your personal brand is akin to curating your professional identity. I've found that maintaining a strong online presence through a portfolio or blog not only showcases my projects and skills but also establishes credibility. Actively contributing to open source projects and engaging with data science communities enhances visibility and fosters meaningful connections. Building a reputable personal brand not only opens doors to exciting opportunities but also reflects authenticity and passion, contributing significantly to a fulfilling and impactful data science career.

A fine-tuned LinkedIn profile set as 'Open to Work' may attract recruiters, but showcasing your expertise with a GitHub page or personal website is essential. It’s a dynamic portfolio of your skills, projects, and contributions. Networking with professionals in your domain is vital, fostering connections and staying up-to-date with industry trends (curate your network). Finally, sharing your knowledge through blogs (e.g. Medium) or other online content (e.g. LinkedIn posts) not only strengthens your brand but also establishes you as a knowledgeable member of the data science community. Together, these elements not only enhance your visibility to potential employers but also highlights your credibility and commitment to the field.

There are many ways how to build your personal brand. If you chooses a skill-based way, then your outlook is less important than your pitch.

Para fortalecer la marca personal hay que enfócate en crear y mantener un portafolio en línea que resalte tus proyectos, habilidades y logros. Un blog o sitio web personal puede servir como plataforma para compartir tus conocimientos e ideas. Contribuir en proyectos de código abierto y participar en comunidades de ciencia de datos, como foros o grupos en línea, aumenta tu visibilidad y establece tu reputación en el campo. Publicar artículos o estudios en plataformas reconocidas también es una manera efectiva de mejorar tu marca personal. Estas acciones no solo reflejan tus competencias técnicas, sino que también demuestran tu compromiso y pasión por la ciencia de datos, elementos claves para construir una marca personal sólida y respetada.

Your brand gets you noticed: recruiters will reach out to you, people may ask you to speak at an engagement, some may be interested in your opinions. The number one thing you can do here is engage. Go to events, give your (polite and professional) opinion on posts and events, link to your website. While it helps to take a data-driven approach here, I personally don’t worry too much about that and I just keep my engagement high with my community.

Another key metric in your data scientist journey is career satisfaction. It's about how much joy, fulfillment, and growth you get from your work. Check in by thinking about what you love and want as a professional. To boost satisfaction, go for tasks and rewards that match your interests and goals.

Here are some key reasons why tracking career satisfaction is important: 1. Data science success depends on sustained motivation and enjoyment of work over the long run. Metrics help identify factors that enhance or detract from satisfaction. 2. Insights into satisfaction levels guide smart career decisions. 3. Tracking over time shows the progression of satisfaction that may result from promotions, new leadership, and other initiatives.

From my perspective, I feel career satisfaction is one of the most important key metrics to measure our success in the field of data science. Enjoying the work we do and having fun while looking at a problem from different perspectives takes us a step closer to the so-called success, which people run behind. I feel you should be curious and interested in various ways to solve a problem even if the problem statement domain does not belong to you; that's where you actually try to look to find the solution from a different perspective rather than a conventional one.

I would argue this should be ranked higher. One critical error many make is that they are only thinking about what their next job should be. Instead, think what your ideal last job should be and work backwards from there. Do you want to be an esteemed SME, or maybe a leader like a CEO? Both are going to require different decisions that you have to make today.

Simpler metrics for data scientist career progression could include: 1. Skills Acquired: Keep track of new technical and soft skills learned over time. 2. Project Growth: Note the complexity of projects you handle, indicating your ability to take on more challenging tasks. 3. Responsibility Increase: Monitor your level of responsibility in projects or teams, showcasing your growing contributions. 4. Collaboration: Assess your collaboration with different teams or departments, demonstrating your ability to integrate data science into broader business functions. 5. Educational Achievements: Document any certifications or additional training completed, highlighting continuous learning and expertise development.

What are the most important data indicators in your career? Job description and POSITION. The job description is a good starting point for your career. And position is just a process to step up in your career development. The title of your job will change as you change jobs or get promoted.

The revolutionary breakthrough idea which drives a great leap of technology but not just incremental add-ons, and it is enough to change the landscape of existing technologies.

As a data scientist, tracking your progress involves quantifying your contributions, skill advancements, and career goals. Key metrics include: project impact (measured by efficiency improvements or revenue generated), skill development (e.g., mastering new tools or techniques), publication/citation counts for research work, and professional network growth. Regularly reviewing these metrics helps identify areas for growth, ensuring continuous professional development and alignment with industry trends.

I think all of these come in the top spot at one point or the other. There is not one definitive metric as all of these are dependent and could be independent of the other. Depending on your level and professional experience, you need to have the skill to be identified by your lead, then that lead needs to place you in a spot where you can be seen and have your outcomes actually count and be heard.

Innovation Index: Measure the introduction of novel techniques, methodologies, or approaches in your data projects. User Engagement Metrics: If applicable, assess the impact of your data-driven solutions on user engagement, satisfaction, or behavior. These creative metrics can provide a more holistic view of your contributions as a data scientist, emphasizing innovation, and user impact.