A continuación, le indicamos cómo puede mejorar sus habilidades de análisis y visualización de datos.

Firstly, deepen your understanding of statistical methods/data manipulation techniques through online courses, textbooks, or workshops. Practice analyzing diverse datasets using statistical software like Python, R, or SQL to gain hands-on experience. Secondly, familiarize yourself with data visualization tools such as Tableau, Power BI, or matplotlib in Python. Learn to create visually appealing and informative charts, graphs, and dashboards that effectively communicate insights from your analysis. Additionally, seek out real-world projects or datasets to work on, either through personal projects or by collaborating with colleagues Most importantly be curious to learn from others. Speak to others in your team. Continuous learning is key.

I just had a coffee chat about data types and structures! He said they are very important in the long run because you do not want to waste space.

Start by understanding the fundamentals of data analysis, including statistical concepts, data types, and common analysis techniques. Familiarize yourself with basic data visualization principles to effectively communicate insights derived from analysis.

Certamente, um analista de dados naturalmente atua como uma luz no fundo de um túnel, no entanto, é imprescindível que tenhamos no mínimo fundamentos dessas métricas estatísticas ora mencionadas. Pois elas atuam como epicentro de uma bao tomada de decisão em função da análise de dados.

Very important. That’s why I advise anyone starting out in data analysis to start with the basics of Microsoft Excel. This foundation helps to build your confidence and prepares you for advanced analysis.

Regarding statistical concepts, if you have worked with Excel on day to day using some formula like SUM, AVG, IF, etc. Then most likely you already get the basic of statistical. Only need to learn a bit deeper such as outlier, hypothesis testing, etc. But for me, most importantly as the basic of data analysis, you have to first understand the business context of the data or project you are working with, since it will really help you to move to the next step easier

The nice thing about data analytics is that most roles either generate or need to use data to become more effective. I was able to get my first data analytics role by pursuing continued education in my free time and by looking for projects in my first role that involved analyzing data. Resources like DataCamp can help you start playing with data in R, Python, Tableau, and even Excel. Getting basics down like pivoting/aggregating data and then maturing into visualizing data is a great way to get started!

Something just as important as understanding the basics is understanding the problem itself. If the question you are trying to answer benefits the problem you are trying to solve, go ahead and analyze. A lot of data analysis feels like operating in the dark, but framing questions like "I want to look into this because I hypothesize that X affects Y, which contributes to my problem Z" can be very helpful to avoid getting lost in the weeds of endless analysis.

Antes de se aventurar em análises de dados complexas, é crucial ter um entendimento sólido dos conceitos básicos. Conheça bem as estatísticas fundamentais, como média, mediana, moda, variância e desvio padrão, pois são a base das análises avançadas. Além disso, tenha uma boa compreensão dos tipos e estruturas de dados, já que esse conhecimento é vital para escolher as ferramentas e métodos corretos para a sua análise.

Mastery of tools like Google Docs, Excel, Tableau, Power BI and SQL goes a long way to enhance your data analysis and visualization skills. There are several ways through which these skills can be mastered. They include bootcamps, online courses, and on YouTube.

Explore and become proficient in popular data analysis and visualization tools such as Excel, Tableau, Power BI, or Python libraries like Pandas and Matplotlib. Utilize online tutorials, courses, and practice exercises to deepen your understanding and mastery of these tools.

Emphasis on practice! Practice!! Don’t just watch Youtube videos and attend online classes. Hands-on projects are more beneficial than just tutorials, as they provide real-world context. An example I've seen is how learning SQL can drastically improve your ability to handle large datasets, making your analysis more efficient and robust.

There are so many data analysis tools available. For me personally I have used Excel, mySQL, python, Tableau, and powerBI. Each of those tools have their own advantage and disadvantage. In order to be 'master' of data analytics tools, I think the key is 'exercise'. We can't be good in any of those tools just by reading article or reading other's analysis. We can only be better and better by keep on using it, keep on practicing. One step at a time, just keep exercising. Appreciate the process.

Mastering tools (use DataCamp, Youtube, etc.) is important BUT a highly underrated skill that I use all the time in my work is knowledge of database structures. Get a fundamental exposure to databases (2 hours) and be able to comfortably say, "This relationship is many-to-one" or "this data is not atomic" and it will serve you greatly every time you need to write a SQL query or even just pivot data in Excel.

Enhancing your data analysis skills requires mastering essential tools, such as advanced Excel functions like pivot tables. Pivot tables are crucial as they allow quick summarization of large datasets, flexible data exploration, interactive dynamic reporting, efficient data filtering and sorting, and seamless integration with visualizations. Additionally, learning VBA for automation can significantly streamline repetitive tasks and enable complex calculations and custom functionality. By mastering these tools, you can make your data analyses more efficient, thorough, and insightful.

When it comes to data viz tools, there are a ton of options out there, each with its own strengths. Regarding what I’ve mostly used, Tableau is amazing for creating interactive dashboards and exploring data visually. Minitab is a powerhouse for specific statistical analysis and process improvement. And PowerBI is a business intelligence tool that integrates well with Microsoft-oriented organizations and has a broad support community. If you're starting out, it's a good idea to get familiar with a few different platforms. That way, you'll be able to choose the right tool for the job

When it comes to data viz tools, there are a ton of options out there, each with its own strengths. Regarding what I’ve mostly used, Tableau is amazing for creating interactive dashboards and exploring data visually. Minitab is a powerhouse for specific statistical analysis and process improvement. And PowerBI is a business intelligence tool that integrates well with Microsoft-oriented organizations and has a broad support community. If you're starting out, it's a good idea to get familiar with a few different platforms. That way, you'll be able to choose the right tool for the job

Não há uma ferramenta ideal de modo geral, mas sim aquela que melhor atende aos requisitos do projeto que você irá desenvolver. Entre as principais, destacam-se o Power BI, Tableau, QuickSight e Looker. Além disso, é possível utilizar Python e R juntamente com bibliotecas populares como matplotlib, seaborn e ggplot2 para visualização de dados mais personalizada.

Dedicate time to learning programming languages commonly used in data analysis, such as Python or R, and practice coding through real-world data analysis projects. Build proficiency in data manipulation, cleaning, and analysis techniques through hands-on coding exercises and projects.

Use online platforms like Kaggle to find datasets to work with or create your own projects to apply what you've learned. I love coding so much. Starting out as a data analyst, I was wondering why I had to code, lol. I thought I’d just create charts and that’s all. I started out with writing SQL codes then Python codes. And trust me, it’s been amazing!

To practice coding skills used in data analytics, I usually practice on online platform such as StrataScratch, HackerRank, DataLemur, or any other platform. On those platform, we could learn and practice from the very basic to the advanced one. Also, nowadays, the most powerful tools we can use is AI such as ChatGPT, where we could ask the syntax of a coding for the job we would like to do. But we still have to understand the whole code given by the AI.

The online resources for learning coding are good and will get you so far but nothing lives up to needing the tool for your job. Unfortunately, many workplaces don't require using R/Python on the daily and like a muscle it will atrophy. Try forcing yourself to accomplish specific work objectives in the programming language of your choice (extra credit if you can make executive worthy visuals too!)

Something just as important as coding is debugging. Learn to to use the scientific method to debug your own errors, and you become an independent programmer that doesn't pester your colleagues every time something goes wrong in the code.

Dominar linguagens como R e Python é essencial para a criação de visualizações de dados eficazes. Essas linguagens oferecem bibliotecas poderosas, como matplotlib, seaborn (Python) e ggplot2 (R), que proporcionam flexibilidade e controle na criação de gráficos personalizados. Com conhecimento prático nessas linguagens, os profissionais podem explorar e manipular os dados de forma mais eficiente, adaptando as visualizações às necessidades específicas do projeto

Analyzing data involves examining and interpreting information to uncover patterns, trends, and insights that can inform decision-making. Data experience refers to the knowledge and skills gained through working with data, such as collecting, cleaning, analyzing, and presenting information. In the context of a business or organization, strong data experience can lead to better strategic planning, more informed decision-making, improved operational efficiency, and enhanced customer experiences. In my opinion, data experience encompasses the skills, knowledge, and expertise required to effectively work with data to derive insights and drive decision-making.

Working on real life problems will go along way in understanding the best way to analyze data. There are several projects online you can use to practice. It will help you get better faster.

Engage in regular data analysis projects to apply your skills and gain practical experience in extracting insights from datasets. Challenge yourself to work with diverse datasets and explore different analysis techniques to broaden your analytical capabilities.

Aha! Analyzing data has to be the sweetest part because, you’re done with the toughest part( data cleaning, lol). I’d like to add, before analyzing, you have to know “why” you’re analyzing this data. Ask yourself questions like: what do I want to achieve with this dataset? Asking these questions guide you on working better instead of moving around and analyzing less important stuff.

As you already have the basic for analysis and the tools to do it, the next step is to find some project to analyze. I usually search for public dataset to analyze from the like of Kaggle or Maven analytics or any other source. In those website, we could search any kind of public dataset, such as marketing dataset, banking dataset, or logistic dataset. If after finding the dataset you are interested, you still have no idea of what to do, there are also a lot of people usually already done the analysis using the same dataset. You could just learn from them, and do it your own version.

Focus on domain-specific learning to understand the context behind the data, stay updated with the latest trends and methodologies, and prioritize ethical data handling by being mindful of biases, data limitations, and the implications of your findings.

If you’re just starting out, it’s crucial to understand the difference between continuous and discrete data. Continuous data can take any value within a range, such as height, temperature, or time. Discrete data, on the other hand, can only take specific values, like the number of injury incidents in a construction environment or the count of defects in a production process. Analyzing these different data types requires distinct statistical techniques, so building a solid foundation in statistics is key. Without the background knowledge on how to treat each data type, you’d be remiss in your recommendations and findings.

If you’re just starting out, it’s crucial to understand the difference between continuous and discrete data. Continuous data can take any value within a range, such as height, temperature, or time. Discrete data, on the other hand, can only take specific values, like the number of injury incidents in a construction environment or the count of defects in a production process. Analyzing these different data types requires distinct statistical techniques, so building a solid foundation in statistics is key. Without the background knowledge on how to treat each data type, you’d be remiss in your recommendations and findings.

Na análise de dados com R e Python, começamos preparando os dados, limpando e organizando-os. Em seguida, exploramos os dados, criando gráficos e estatísticas para entender padrões e relações. Depois, podemos aplicar modelos estatísticos ou de machine learning para extrair insights ou fazer previsões. Por fim, comunicamos nossos resultados de forma clara, seja em relatórios, apresentações ou dashboards interativos, utilizando ferramentas como Jupyter Notebooks ou R Markdown para integrar análises, visualizações e explicações.

Nothing beats hands-on experience, the best way to enhance your data visualization skills is to practice. Test several visualization options, and find out what works and why it does. When testing, ensure you optimize all visualization for simplicity.

Focus on enhancing your data visualization skills by creating clear, informative, and visually appealing charts, graphs, and dashboards. Experiment with different visualization techniques and formats to effectively convey key insights and findings to stakeholders.

Data visualization for me is the 'art' of data analytics. Because we have to find the balance between the functionality of the viz and the visual factor of it. Most importantly, the main goal of every visualization is so that anyone who sees it will get and understand any kind of information that we want to share

Simple is always better. Also, learn to gray out the parts or bars in the graphs that don't matter to bring attention to the component that does. Color psychology and ink ratios are extremely helpful for design decisions that serve to deliver graphs and plots that communicate thoughts effectively, quickly and clearly.

Yes, data visualization involves more than just making graphs and charts—it also involves using your data to tell a story. To effectively communicate insights and tell the story behind the numbers, choose a chart or graph that is appropriate for your data and audience. Every visualization—whether it's a heat map to draw attention to patterns, a line graph to demonstrate trends over time, or a pie chart to show proportions—should be carefully designed to improve comprehension and clarity.

Uma forma de obter insights rápidos é fazer o uso de gráficos e visualizações para examinar os padrões e tendências nos dados de forma intuitiva e acessível. Ferramentas como histogramas, gráficos de dispersão e mapas de calor podem revelar insights rapidamente

- Set aside regular time slots each week for professional development. This could involve dedicating a few hours to reading the latest articles, watching tutorials, or completing segments of online courses related to data analysis and visualization. 🕒 - Subscribe to newsletters from leading data science platforms and forums can provide a steady stream of up-to-date information on emerging technologies and industry best practices. This way, you get curated knowledge delivered straight to your inbox. 📬 - Actively participate in community discussions, whether in online forums or at local meetups. Sharing experiences and solutions with peers can offer practical insights and enhance your learning curve.🤝

You can only get better when you practice what you've learned. Continuous learning helps you upskill and adds more to your knowledge till you get to your desired level.

In the dynamic realm of data analysis and visualisation, adaptability is key. Embrace continuous learning as a compass guiding you through the ever-changing landscape. Engage with industry blogs, immerse yourself in online communities, and actively participate in webinars or workshops. By doing so, you not only stay abreast of emerging tools and techniques but also foster invaluable connections within the professional network. Keep your skills sharp, and let curiosity be your driving force in this exciting journey of perpetual growth and innovation.

Embrace a mindset of continuous learning by staying updated on industry trends, advancements in data analysis tools and techniques, and best practices. Participate in online communities, forums, and networking events to exchange knowledge, seek guidance, and learn from peers and experts in the field.

In addition to mastering tools and practicing coding, collaboration and networking are crucial. Working with others can provide new perspectives and enhance your learning. Join data science communities, attend workshops, and participate in study groups to build your network and gain insights from peers. Effective communication and collaboration ensure your analyses are well-rounded and impactful.

I always prioritize learning basics over learning to work with tools. knowing Statistics helps you see tha datasets something other than tables polulated with meaningless numbers and makes extracting insight easier. Furthermore, For practicing with real-world data sets it is important to have an organized approach, from asking questions that the dataset can answer to find the ways to answer them.

Todos os itens mencionados são de relevância ímpar quando se fala desse assunto, mas eu acrescentaria um bom networking junto com tudo que já foi dito.

Todos os itens mencionados são de relevância ímpar quando se fala desse assunto, mas eu acrescentaria um bom networking junto com tudo que já foi dito.

One thing to keep in mind is whether you’ll be doing one-off analyses or setting up ongoing measurement systems. A one-time analysis might be used to answer a specific question, allowing for a faster and more detailed examination. In contrast, an ongoing system requires more initial development and maintenance but enables continuous data collection and monitoring. Understanding your objectives and the resources available will help you determine the best approach for your scenario.

One thing to keep in mind is whether you’ll be doing one-off analyses or setting up ongoing measurement systems. A one-time analysis might be used to answer a specific question, allowing for a faster and more detailed examination. In contrast, an ongoing system requires more initial development and maintenance but enables continuous data collection and monitoring. Understanding your objectives and the resources available will help you determine the best approach for your scenario.