You want to become a data analyst. How can you get started on the right foot?

Anyone who decided to become a data analyst. First they ask to them why we need this role i.e. what is our intention.Then only we start. So before enrolling data analyst first clear in objective. For data analyst the objective is to solve the problem not to make colourful dashboard. So first in data analyst is to know the business acumen and know the objective. Then move forward

Entiende tu pasión y motivación, ?Te apasiona descubrir patrones en los datos? ?Quieres contribuir al éxito de una organización? Identifica tus fortalezas y debilidades, evalúa habilidades actuales y áreas de mejora en el análisis de datos. ?Tienes experiencia en matemáticas, programación, estadística? Establece objetivos específicos y medibles, define objetivos claros y específicos que te gustaría lograr como analista de datos. Investiga el campo del análisis de datos, explora áreas y aplicaciones del análisis de datos, como análisis de negocios, marketing, salud, etc. Considera tus circunstancias personales, nivel de educación, experiencia laboral y responsabilidades personales. Define objetivos realistas que sean alcanzables.

Para iniciar una carrera como analista de datos, primero adquiere una educación relevante y aprende herramientas clave como SQL, Python, y R. Entiende los fundamentos estadísticos y aplica tus conocimientos en proyectos reales para construir un portafolio. Desarrolla habilidades en visualización y comunicación de datos. Amplía tu red profesional, busca mentoría, y mantente actualizado con las últimas tendencias en análisis de datos. Considera pasantías o roles de entrada para ganar experiencia práctica. Obtén certificaciones relevantes y busca constantemente mejorar tus habilidades y conocimientos.

Objetivos de Carreira: Você precisa ter uma ideia clara de onde quer chegar em sua carreira. Quer trabalhar em uma grande empresa de tecnologia, ou prefere o ambiente de uma startup? Você está interessado em um setor específico, como finan?as ou saúde?

Determina qué aspecto del análisis de datos te apasiona más y qué problemas específicos quieres resolver. Esto guiará tu aprendizaje y te mantendrá enfocado.

Now before going to analyse the data. First you have to understand what is data. Data is simply a collection of information. Information is nothing but text,numbers,images,etc. And understood the information i.e basic criteria of what information it is. And how we get this information, who submitted the image and what is the purpose of this image. Then only you start analysis. Think about it twice.

Algunos pasos a aprender siendo analista de datos: Aprender los fundamentos, conceptos básicos del análisis de datos, estadística, bases de datos, programación (Python o R), herramientas como Excel, SQL, y bibliotecas de análisis de datos. Obtener experiencia práctica. trabajar con datos reales para practicar habilidades y desarrollar portafolio. Participa en Cursos y Certificaciones, unirse a comunidades y redes , conectarse con otros profesionales del análisis de datos. Desarrollar habilidades de comunicación, capacidad de comunicar hallazgos de manera clara y efectiva, mejorar habilidades de presentación y visualización de datos. Busca Oportunidades de Trabajo o Pasantías para ganar experiencia práctica en el mundo real.

Objetivos de Carreira: Você precisa ter uma ideia clara de onde quer chegar em sua carreira. Quer trabalhar em uma grande empresa de tecnologia, ou prefere o ambiente de uma startup? Você está interessado em um setor específico, como finan?as ou saúde? - Manipula??o e Visualiza??o de Dados; - Aprendizado de Máquina; Projetos práticos melhor maneira de aprender é fazendo. Tente trabalhar em projetos práticos que permitam aplicar o que você aprendeu. Lembre-se, a chave é come?ar pequeno e expandir suas habilidades ao longo do tempo. N?o se preocupe em aprender tudo de uma vez. Boa sorte! ??

One thing I found very helpful was connecting with other data Analysts especially on LinkedIn, they have actually helped me to go for the right materials, the rights online tutorials, what is trending in the Data society and to know more about what I'm getting into in the Data world. Also learning excel has built my data analysis experience so much. From my own perspective, I would say learn excel first before opting in for other softwares, it helps a lot.

Familiarízate con estadísticas, probabilidad, y programación en lenguajes como Python o R. Las certificaciones, como la que obtuve de Google, pueden proporcionar una base sólida.

Companies use different data analysis tools, and these tools change as new technology develops. Choosing tools that are commonly used in industry is a good place to start. Today, these are:? SQL, Excel and Tableau or Power BI - Nearly 55% of companies require SQL as a core skill for their data analysis positions since most data lives in structured databases.? - Excel is the most accessible and easiest tool to learn for business analytics. A 2019 Robert Half study found that 54% of businesses use Excel as their primary analysis tool. - According to Gartner Research, there are more than 220 data visualization tools in the market, but 30% of companies use Tableau or Power BI. Start with either one.

Une des choses après avoir défini ce que c’est qu’un ??data analyst?? consiste à identifier les outils qu’il utilise habituellement. Cela passe par une veille sur le marché et une observation des tendances dans la profession. Il faudra également déterminer ce dont on a réellement besoin en tant que data analyst cela sera déterminant pour la suite. Il est à noter qu’il ne faut pas se munir d’un outil uniquement par effet de mode mais de le choisir en fonction de nos réels besoins. C’est un détail non négligeable.

Evalúa y practica con herramientas esenciales como SQL para manejo de bases de datos, y software como Tableau para visualización de datos. Mantente al día con las tendencias actuales de la tecnología.

Here is a list to make your life easier: Excel Python SQL Google Meets and Microsoft Teams Looker, Power BI or Tableau Don't bother learning more than that, these few alone will enable you to do 90% of all analysis needed nowadays.

For analysing the data there are more tools available. But you have to check the limitations of each tools i.e. in excel itself we will analyse using pivot table and pivot chart then why we go to other tools. So excel has some limitations compare to other tools like that each tools has some pros and cons. So based upon industry and current trend use the wise tools. So as per my Market research while start the career in data analyst i recommend power BI, Tableau. So this tools we give more options and some ideas of Charts. Then advance go to python.

Working on projects cannot be overemphasized, it improves your ability to think analytically, it helps you to know various ways to solve problems, it helps you in increase your knowledge on different functions and so on. There are useful sites where you can get project from e.g github.com and so many more.

Aplica lo que aprendes en proyectos reales, aunque sean hipotéticos o voluntarios. Esto te ayudará a entender los desafíos prácticos y a construir un portafolio.

So You know the tools. Then next what you want to showcase you skills via projects only. So in online there is a plenty of projects available. Don't copy and paste. First see how they are doing.Then with that search some common problem and collect data. Then start building project. It will impress to Recruiters

únete a comunidades, participa en foros y busca mentores. La retroalimentación de otros analistas es vital para mejorar tus habilidades.

Working in a team is much easier than doing all alone. I was the one who tried everything on my own and nobody helpful me - this takes way more time than getting trained by experienced co-worker. So if you have a chance, join some teams who need data analysts and offer your help in exchange for experience - its worth it!

Feedback is everywhere. Without feedback non of the product won't sale. Like that without feedback we won't improve. So as much ask feedback to your senior and also in modern era there is a Chatgpt, Perplexity and online tools there. Ask there it will be more usefull and that also will helpful.

El campo del análisis de datos está en constante evolución, por lo que es importante seguir aprendiendo sobre nuevas técnicas, herramientas y mejores prácticas.

This one never stops - you will have to keep learning. Don't think high - not complicated statistics and IT, but the simple things like Pandas, Excel, maybe even PowerBI and Looker - just get more knowledge that will save your time and enable you to achieve more with such tools.