What do you do if you want to start a career in data analysis but don't know where to begin?

Just start from MS excel...and build into any business intelligence tool. MS excel help you understand basic syntax and what you will be encountering in future while you explore other tools

Seguem alguns passos que podem ser adotados: 1 - Aprender os conceitos básicos, como tipos de dados e análise estatística; 2 - Escolher uma linguagem de programa??o, como Python ou R; 3 - Praticar bastante com projectos de análise de dados; 4 - Explorar ferramentas como Excel, Power BI e Tableau; 5 - Conectar-se com profissionais e participar de comunidades de análise voltadas para esse ramo de actua??o; 6 - E por último, manter-se atualizado com as últimas tendências e técnicas do mercado. Seguindo os passos acima, pode-se iniciar uma carreira bem-sucedida em análise de dados.

Before delving into the intricate landscape of data analysis, it's essential to establish a strong foundation. Data analysis involves a series of processes, including examination, cleansing, transformation, and modeling of data to derive meaningful insights, inform decisions, and drive actions. Begin by acquainting yourself with fundamental statistical concepts and analytical reasoning. Online courses, textbooks, and tutorials serve as valuable resources to gain the foundational knowledge necessary for understanding data sets and extracting insights from them. By mastering these basics, you'll be well-prepared to navigate the complexities of data analysis effectively and make informed decisions based on data-driven insights.

As a data analyst proficient in SQL, basic Python, and Excel, understanding both the data and the business context is key. Start by mastering Excel for data manipulation and visualization, then advance to SQL for managing larger datasets efficiently. Finally, leverage Python for more complex analyses. Remember, the goal is not just mastering tools but deriving meaningful insights that drive business decisions. Combining technical skills with a deep understanding of data and business objectives will enhance your effectiveness as a data analyst.

Once you start regularly using excel you will naturally get your own style of working with it. Just keep the end user of your analysis in mind and try to keep an organized system in your table construction, naming conventions, titles, use of color, and formatting. These are all important aspects of communicating your results effectively.

To begin with first step, excel must be your entrance to data analysis world. Understanding Excel is the first step. Other tools can be understod easily later if you understand basics.

Well, Im agree with you on side of expend our knowledge wield but do not go deeper with each of them, i recommend to learn the basics just. Because each company have specific system and tools depend on the company we will in it. after you Get your offer job and knowing their systems then go deeper .But when it comes to learn these tools. We have to put some consideration firstly implementing what we learn, secondly do not enroll many course in same time or consuming many learing resources stack with one or two just and go along with it.

Amplie seu leque de ferramentas úteis para análise de dados e o conhecimento técnico nessa área de atua??o, isso ajudará você a desenvolver bem mais rápido os seus projetos.

Find something you are passionate about in your own life and data may change the way people interact or use it. Form a hypothesis around what you think and then go out and find as much data as you can (in the public and private sector) to prove or disprove this hypothesis. Once you have your data - figure out what it means! And then present it back to the world.

This is very important! If you don't try your hand at projects, you'll find yourself forgetting. Data analytics is just like playing a musical instrument, once you stop practicing, you start forgetting. It's a technical field, so you can't afford to slow down. There are plenty of opportunities to get your hands dirty with free real-life data on platforms like YouTube and Kaggle

They always say, 'let your work speak for you'! Gaining hands-on experience is very crucial to kickstart your career in data analysis, therefore, build projects especially solving real world problems. There are so many real world projects you can find on platforms like Kaggle and Zindi.

Apply your knowledge by working on real-world projects. Use datasets from Kaggle or other open data sources to practice and help in build a good portfolio.

Hands-on projects allow you to apply your learnings, develop a portfolio, and demonstrate your skills to potential employers. ? ?????????????? ???????????? ????????????????: Look for interesting public datasets (e.g., from government websites or platforms like Kaggle) and practice data cleaning, analysis, and visualization techniques. ? ???????????????????? ???? ????????-????????????: Participate in open-source data analysis projects on platforms like GitHub, collaborating with others and showcasing your coding abilities. ? ?????????? ??????-???????? ????????????????: Reach out to local businesses, non-profits, or community organizations and offer to analyze their data, providing valuable insights while building your portfolio.

Building a network of like-minded people is crucial in this era. A strong network can help you launch your career successfully. There are two approaches to networking - 1. Common approach - Reach out to people when you need something. 2. My approach - Reach out to people even when you don't need anything. This builds genuine connections because everyone gets approached for favors. By being honest, respecting their time, and accepting their values, you establish trust and long-lasting relationships.

Networking is quite important. It's difficult to retain and grasp everything unless you proactively work on data analysis in a live project. Another great approach could be to occasionally connect with peers who are working on data analysis in different industries, with different tools or methodologies. A knowledge-sharing session, perhaps 30 minutes or so, where you discuss the objectives, methodology, and challenges from the perspective of a different project, helps expand horizons and inculcate more confidence in your work. This is well proven in my line of work.

Ao participar de uma comunidade de conhecimento, você permite criar uma gama de perspectivas diferentes, além de ampliar sua cosmovis?o na área de atua??o.

Connect with professionals in the field, join relevant LinkedIn groups, and seek mentorship to guide you through your journey.

Getting certified can be a good starting point to prepare you especially for the fundamentals of data analysis, also, they can increase your credibility as well. Be mindful of the institutions you certify with because some of them are not recognised. Before you start getting certifications, do your thorough research on the institution first to see if their certifications are valid or not (that's if you want your certificates to get you somewhere) You can try institutions like LinkedIn learning, Microsoft, Google, Datacamp, IBM etc

It’s a good option to get certified as it can help you start your learning process with ease.The certification courses have well organised learning structure which can help if you have time boundaries.This can help pace up your learning.

Remember, don't give up. It's okay to receive rejection emails but just keep applying, and do so wisely. Meanwhile, continue to practice and enhance your skills. That way, when the right opportunity comes along, you'll be fully prepared.

Build a Portfolio: Start building a portfolio of data analysis projects to showcase your skills and experience to potential employers. Work on personal projects, participate in online competitions like Kaggle or contribute to open-source projects to demonstrate your abilities.

N?o se adquire sabedoria se n?o aplicar o conhecimento. Aplique e aprenda com os erros. A sabedoria vem quando superamos desafios. Tenha atitude.

Start now. Start where you are. Start with fear. Start with pain. Start with doubt. Start with what you have. Start and don't stop. You can do it

Before stepping into the realms of Data Analysis, first reflect on your reason for wanting to step in. Define your goal, then work towards it. Do some necessary and relevant research about Data Analysis and what you are about to delve into. If you would be going for physical or virtual classes, consider your most convenient and accessible mode of learning. Apply to relevant places of learning and resume learning. It would get boring at some point, so you would need to be sure of your goals, so as to derive a good deal of motivation for yourself while on the journey.

Understand what it’s really about. Don’t think you’re going to be lauded as a genius or given a seat at the top table, know that analysis is a respectable trade and skill but you will always be providing services TO decision makers rather than BEING the decision maker.

First, you need to self realise whether you are good at Calculations, Data, Formula's, Presentation and Story telling. Then, go for formal learning... the subject is not as simple as people present. Believe me, I had worked with Data Analysts though they had don't their certifications and had work experience however they fails while making presentation/story telling because they miss the domain knowledge... they were good at their subject but lack domain knowledge.