What do you do if you want to analyze economic data but don't know which tools to use?

To analyze economic data, consider using statistical software, spreadsheet software, econometric software, data visualization tools, online platforms, database management systems (DBMS), text analysis tools, and machine learning libraries. Statistical software like R, Python, and MATLAB offer customizable functions, while spreadsheet software like Microsoft Excel and Google Sheets are user-friendly. Econometric software like Stata and SAS offer specialized features. Experiment with different tools to find the best fit for your project.

In my experience of analyzing economic variables to gain insights for decision making as well as forecast, some of the things I considered when selecting the tools were considering my specific needs/ objectives and available budget. I also sought advice from Economists who provided some guidance.

If you're unsure how to choose the right economic forecasting tool, here are some key steps: 1. Define your forecasting needs - identify the economic indicators, timeframe, and required accuracy. 2. Research and evaluate different forecasting tools, comparing features, capabilities, and fit with your requirements. 3. Consult experts and experienced users for recommendations on the most suitable tools. 4. Conduct test runs to assess the accuracy, reliability, and usability of promising tools. 5. Consider ongoing support and maintenance when selecting a forecasting tool. 6. Continuously review and refine your approach as needs and economic conditions evolve.

When we intend to analyze economic or any type of data, the first thing we have to determine are the indicators or ratios that will help us know the health of the organization under study. The next step should be to know where the data necessary to calculate these ratios is housed. Data mining work is a complex job that sometimes has to be developed together with the ERP provider that generates it.

I have to identify the sources of data's first. If my data primarily consists of survey or market research results, statistical analysis tools such as SPSS or SAS may be the best choice. However, I use Microsoft Excel for data analysis and visualization. It’s great for organizing data, performing basic calculations, and creating simple charts and graphs.

Los datos viajan en maleta, de origen a destino. Y no en el formato que deseamos, sino en el que permitan las herramientas, origen y destino. Y si ha de ser así, porque las herramientas no se entienden, es decir, si necesitan un intermediario o mediador, que en este caso es la extensión del archivo, pues elijamos el más liviano, estándar, etc. En definitiva, aquel que acomode mejor origen y destino. En mi opinión, la herramienta que muestre esos valores en el formato más apropiado (Pandas, Excel, PBI, etc), debería conectarse directamente a la herramienta proveedora, sea vía api, vía query, o de la forma que mejor acomode origen y destino. De esta forma, ganaríamos eficiencia, rapidez y todo estaría en "rabioso directo" para el usuario

API stands for application programming interface. In the context of APIs, the word application refers to any software with a distinct function. Interface can be thought of as a contract of service between two applications, this contract Defines how the two communicate with each other using requests and responses. For example, The weather bureau's software system contains daily weather data, the weather application on your phone " talks " to this this system via APIs and shows you daily weather updates on your phone.

Companies have a great variety and amount of data in their possession, so in order to manage, work and analyze all the information, it is essential to use Business Intelligence tools. This allows us to automate data extraction, transformation and presentation. It is important that the information is accessible in real time, minimizing time lags that can make decision-making difficult.

Zapatero a tus zapatos, o dicho de otra forma: "no enredes". Antes de ponerte a crear informes para la Dirección aprende, estudia, prueba, juega, fórmate, y cuando estés al tanto de cómo se hacen bien las cosas y sólo entonces ¡hazlas!. Da igual la herramienta o la tecnología que uses, aunque, por favor, aprende antes de tirarte a la piscina, para no hacer el ridículo. Si vas a usar Python, procura estar a la última en Pandas, por ejemplo. Si vas a usar herramientas de inteligencia de negocio más orientadas a mostrar que a programar, adelante, aunque por favor, siempre siempre ¡fórmate antes!

Start by exploring popular statistical software like R, Python with libraries such as pandas and numpy, or specialized economic software packages like Stata or SAS. Each tool has its strengths and weaknesses, so experimenting with them will give you a better understanding of their capabilities and which ones align with your preferences and requirements. Additionally, consider taking online courses, attending workshops, or seeking guidance from experienced professionals to deepen your understanding of different tools and refine your analytical skills. As you gain more experience and expertise, continuously evaluate and adapt your approach to ensure you're using the most effective tools for your economic data analysis.

Y además de lo expuesto ya, debes conocer a quién o quienes va destinado el producto. Es decir, no es el mismo gráfico el que espera ver una persona de la alta dirección, que un responsable de departamento. Incluso tú te sentirás confortable con algo sencillito, o quizá te gusten más los funnels con gráficos complejos y auto explicativos...o no. Si conoces a la audiencia y a los receptores, tienes mucho terreno ganado para obtener tus objetivos. Al final, te darás cuenta de que no a todo el mundo le gusta la tortilla cocinada igual. A algunas personas les gusta con cebolla, a otras sin, a algunas más hecha y a otras menos hecha. Y al final es la misma tortilla, aunque según la cocinemos, llegaremos más y mejor a nuestra audiencia.

To develop a reliable and effective analysis system we must take into account: - Start with reliable data - Avoid manual data entry - Ask the audience how they need to see the data - Provide only the necessary information - Simplify the reporting model - Carry out specific checks to detect possible errors in the model