Here's how you can equip yourself with the necessary tools and software for Data Analytics.

Before you start using software and tools, it's important to have a good understanding of the basics of data analytics. This includes: - Working with numbers and statistics - Creating charts and graphs to help understand data - Understanding how data is stored and organized - Being able to interpret and make sense of data Think of it like building a house - you need a strong foundation before you can start adding walls and a roof. There were plenty of online material to learn these skills and get a solid foundation in data analytics.

To equip yourself with the necessary tools and software for Data Analytics, master programming languages like Python and R for data manipulation and analysis. Learn SQL for database querying and use data visualization tools like Tableau and Power BI. Utilize Jupyter Notebooks or RStudio for interactive analysis, and become proficient in libraries such as Pandas, NumPy, and Scikit-learn for machine learning. For big data, familiarize yourself with Hadoop or Spark. Online courses, tutorials, and hands-on projects, like creating a Tableau dashboard for sales data, can help build these skills. By mastering these tools, you'll be well-prepared for a data analytics career.

Statistics is the base of analytics, as it helps to understand the outcomes of operations performed using programming languages such as Python or R. It is important to know the statistics to clean and analyze data and understand the correlation and significance of the data. Once you get these basics down, it is not hard to learn the language syntax. Practice your skills using different languages and techniques using Python, R, and SQL. Excel is another important tool for understanding and cleaning data. Gradually move towards using software like Tableau and Power BI for amazing visualizations (The calculations are required here too so don't forget "STATS"...)!

before dwelling into skillsets I would like to share one analogy(consider we have different vegetables mixed in a bag along with some cooking ingredients and the first job for chef is to know what all vegetables & ingredients he has for cooking and based on this, he can decide what all dishes he can prepare from that based on customer needs right). similarly, being a data analyst our foremost thing would be understanding what data we have, type of data, how organized data is, where data is stored, what information data has. for this STATISTICS is key skill to master so that we can get basic insights about data by preparing box plots, charts, summarized data tables and interpret them to know about data before proceeding further with analysis

Para se equipar com ferramentas de análise de dados, é essencial começar com a linguagem SQL é uma linguagem para acesso a bancos de dados SQL e NoSQL. Após estabelecer uma base sólida em SQL, o próximo passo é explorar Python e suas bibliotecas para manipulação, processamento e ingestão de dados, como Pandas, Numpy, DuckDb, entre outras. Alternativamente, ferramentas de ETL como Alteryx, Pentaho e Knime facilitam o processo de ingestão de dados, oferecendo uma curva de aprendizado mais suave com suas funcionalidades no-code. Posteriormente, é importante aprender a usar ferramentas de visualização de dados, como Power BI, Qlik, Looker, além da biblioteca "Streamlit", que permite desenvolver aplicativos de dados na web.

Programming is crucial in data analytics, particularly languages like Python or R, which are staples in the industry. Python offers libraries such as pandas for data manipulation and matplotlib for plotting charts, making it a powerful tool for data analysts. Begin with the basics of these languages, and progress by practicing simple data manipulation tasks. This will build your confidence in handling larger datasets and performing more complex analyses, enhancing your skills progressively.

Before you start using software and tools for data analysis, it's important to learn some basic skills first. You should understand statistics, like averages and how data is spread out. It's also helpful to know how to make sense of charts and graphs. Learning about databases, where data is stored and how it's managed, is another key step. Online courses and tutorials are great ways to build these skills before diving into specific tools.

As a Data Analyst, knowing a programming language is essential for working with datasets and deriving insights. Languages like Python and R are particularly useful in our field. I prefer Python for its easy-to-understand syntax and a wide array of libraries like pandas and NumPy, which simplify working with large and complex datasets. Python is also great for writing ML algorithms. Start by learning the basics and practice writing simple scripts to manipulate and analyze data. 🫡Mastering these skills will significantly enhance your ability to play with data and uncover valuable insights.

Según mi experiencia, la elección de la herramienta de BI depende de muchos factores (necesidades de las personas a la que va dirigido el dashboard, características, precio, limitaciones de la herramienta, etc.) que dependen en gran medida del planeamiento estratégico de la organización, así como de sus recursos disponibles. Sin embargo, considero que un punto trascendental para dicha elección es el tipo de reportes que queremos compartir con los usuarios siendo posibles: tableros estratégicos, dirigidos a los cargos más altos en las organizaciones donde se muestra información general y a alto nivel; y tableros operativos, dirigidos a apoyar a la operación, gran cantidad de usuarios y se muestre información a detalle

I find that choosing the right Data software is a huge challenge for newbies, here's how I started; I started with Python, then to Power BI, Excel and the (MySQL) SQL, it's not linear, but these tool and softwares have helped me. Among them is a database tool(MySQL), a data visualization tool(Power BI), a programming tool(Python), and Excel. I started with these, and it helped me, perhaps it can do same for you.

Choosing the right software is essential for effective data analysis. Start with familiar tools like spreadsheets, which are great for basic tasks and data organization. As you gain confidence, progress to more sophisticated data analytics platforms that can handle complex analyses. This step-by-step approach helps you build on your skills gradually, ensuring you don’t get overwhelmed while also expanding your capabilities to include more powerful and specialized tools.

Understanding and implementing effective data management practices is crucial for any data analyst. Start by learning the basics of database management systems (DBMS) and how to use Structured Query Language (SQL) to perform essential operations. Properly collecting, storing, and organizing data not only makes it more accessible but also ensures its integrity, which is vital for reliable analysis. Mastery of these fundamentals will significantly enhance your efficiency and accuracy in data analytics.

Effective data management is vital for accurate and meaningful insights in data analytics. It encompasses proper collection, storage, and maintenance of data. Proficiency in database management systems (DBMS) and the use of Structured Query Language (SQL) for fundamental operations are essential skills. Efficiently organizing data not only saves time but also preserves the integrity of your analyses😏. By mastering these principles, you enhance your ability to manage data effectively, ultimately leading to more precise and insightful outcomes.

Mas allá de la herramienta que se use para la creación de tableros, lo importante es la información que nosotros mostremos en ellos para que cumplan con su objetivo principal "facilitar la toma de decisiones". En BI el fin no justifica los medios, es decir, que nuestros tableros deben cumplir si o si con ciertos estándares generales de desarrollo y visuales para que en su despliegue no se generen problemas a largo plazo.

There are two primary BI visualization tools that provide the most amount of overlap with any other tool: Tableau and Power Bi. Getting a Udemy training course for both and completing it paves the way for greater depth in either as well as doing visualization work anywhere. Most companies only have one or a tool they migrated to after using one. Avoid niche tools until you work at a place that is switching to them.

Data analysts must excel in using visualization tools. It's not just about crunching numbers; it's about presenting data clearly in reports that reveal valuable insights. Picking software that offers various chart styles and lets you customize them is crucial. Creating dashboards that suit different audiences—like managers, executives, and operations teams—makes it easier for everyone to grasp important insights from the data. This skill doesn't just improve understanding but also helps in making smart business decisions.

Equipping yourself for data analytics means staying current with the latest tools and software. Dive into platforms like Python, R, and SQL for the basics, then explore advanced platforms for data insights, visualizations, and analytics. Don't just stick to what you know—be curious. Attend webinars, take online courses, and join data science communities. Experiment with new software and techniques. Stay updated with industry trends and continuously hone your skills. In this fast-paced field, staying current is your best bet to stay ahead.

El día a día, muchas veces nos limite a realizar actividades recurrentes que, si bien es importante, no aporta valor significativo para la mejora de procesos. Es muy recomendable, siempre estar pendiente de las nuevas actualizaciones en las herramientas visuales, metodologías de trabajo, gestión de proyectos, etc. que nos sirven para optimizar tiempos en lo que actualmente tenemos dentro de nuestro quehacer diario. Debido a la naturaleza sociable del ser humano todo lo anterior se puede realizar en las comunidades (tanto desde dentro o por fuera de ellas) que te permite conocer otros paradigmas sobre lo que tu conoces

In the ever-evolving world of data analytics, keeping up with the latest trends and tools is vital. Engage in online communities, attend webinars, and subscribe to industry newsletters to stay current on new software and techniques. This not only boosts your skills but also connects you with peers in the field. By embracing lifelong learning, you'll stay ahead of industry changes and maintain expertise in making influential, data-driven decisions.

One underrated skill is Data modeling. Understanding this will help you not only write better queries but also make it much easier to use the million tools out there.

One has to be aware of SQL, Power BI / Tableau for getting into Data analytics. Power BI and Tableau are data visualization tools. R and Python language are vastly used for performing complex calculations.