You’re struggling to find a way to analyze economic data. How can you make sense of it all?

As one of my valued economic school alumni always says - what is the problem you want to solve? LLMs and ML are making solutions for accessible, thus we can afford to be more intentional in the question

Avant de définir votre question, il est important de comprendre le contexte économique global et les tendances actuelles. Par exemple, en analysant l’impact de la COVID-19 sur le marché du travail, il est judicieux d’intégrer une perspective historique. Comment le marché du travail a-t-il évolué au cours des dernières décennies? Quelles étaient les tendances avant la pandémie? Cette compréhension du contexte vous permettra de mieux interpréter les variations dues à la COVID-19.

Para dar sentido a los datos económicos, primero identifique las variables clave, establezca objetivos claros de análisis, utilice herramientas estadísticas adecuadas y considere el contexto económico y político. Luego, interprete las tendencias, compare con datos históricos y realice proyecciones. La colaboración interdisciplinaria y el seguimiento constante son esenciales para una comprensión más profunda.

A ways to consider: 1) Reorganise 2) Mix other parameters 3) Change some inputs 4) Reframe the data 5) Reassess the process

In my experience as an economics researcher and in industry, I find it useful to think of the theoretical framework of what I am trying to analyze. Example: do you want to look at the impact of COVID-19 on labor demand or labor supply? If you want to think about labor supply you need to think about drivers of supply, like wages, wealth, family considerations, etc. If you want to look at labor demand you need to think about drivers of demand...

É importante ressaltar que a obtenção de dados deve provir de fontes confiáveis, seja através da revisão da literatura a respeito do tópico, devidamente revisada e aprovada por pares, bem como consultas às bases de dados oficiais fornecidos por autoridades governamentais ou banco de dados. Na análise exploratória de dados é importante realizar a organização dos dados com a metodologia de análise apropriada, bem como realizar estudos Cross-section para identificar os impactos de determinadas variáveis em outras. Outra importante ferramenta consiste na elaboração de regressões lineares, evidentemente que deve ser feita em modelos quantitativos, para avaliar o comportamento aproximado da variável no resultado do estudo.

When analyzing economic data, it is essential to have a clear research question or objective in mind. This will guide your data collection and analysis process. Next, identify and select reliable data sources that align with your research question. Once you have gathered the necessary data, organize and clean it, ensuring consistency and quality. Standardize the data to facilitate comparisons and analysis. Conduct exploratory data analysis to identify patterns, trends, and relationships in the data. Use descriptive statistics to summarize key features and visualization techniques to communicate findings effectively.

Use AI tools to automate the collection of relevant economic data. Leverage web scraping, APIs, or existing databases to gather information. Ensure your data is well-organized. Cleanse and preprocess the data to remove errors, missing values, and outliers. Consider using tools like Python's Pandas library for data manipulation.

Once the data is collected, make sure to organise it based on your objectives. For our honey company we focused on how much honey is being consumed on a regular basis, by what age group and geographical area; was that sufficient? of course not. We analysed what uses they make of it; and what substitutes people have. Why do they use sugar instead of honey and vice versa. It is extremely important to understand how the data was collected when organising it. For example, if you collect data through online surveys, you might not cover all age groups. Or you might miss impatient people who don't have time to fill them online.

Once you know what you're looking for, it's time to gather your data. Government websites, international organizations, and market research firms are treasure troves of economic data. Remember, reliable sources and proper organization are key for accurate analysis.

Borrowing a term from data science, conducting an “exploratory data analysis” or EDA is the way to explore & visualize your economic data. When performing an EDA we standardize, normalize, winsorize &/or otherwise transform our data in order to examine relationships between the variables in our data set - rolling correlations over time, seasonality, etc. Often this increases our understanding of the data & environment.

Before deciding on the delivery of your findings, it helps to do some research on how major sources such as Bloomberg and CNBC breakdown and portray them into diagrams that are easy for readers to understand. If this is done well, even a layman will be able to understand complicated data.

Use data visualisation tools and techniques to explore patterns and trends in your data. Tools like Matplotlib, Seaborn, or Tableau can help create insightful visualisations. You can engage with the LinkedIn community to discuss preliminary insights or seek advice on interesting patterns you observe in the data.

To make sense of economic data, think of it like crafting a good recipe. Start with a simple one, tweak it bit by bit, and taste-test along the way. It's all about iterating through models until it fits your taste. But here's the catch: you reach a point where you have to ask, "Is it worth making this dish more complex?" It's a trade-off between spending more time in the kitchen and getting a dish that's just right versus keeping it simple and practical. Finding that sweet spot is the key – a balanced blend of precision without making things overly complicated. It's about getting the recipe just right without spending more time than the meal is worth.

Apply statistical and machine learning models to analyze economic relationships and make predictions. AI algorithms, such as regression models or clustering algorithms, can be powerful tools. Collaborate with LinkedIn connections specialising in data analysis or economics to validate your models and gain additional perspectives.

Des graphiques, des tableaux et des infographies peuvent rendre les données complexes plus digestes. Utiliser des outils de visualisation de données tels que Tableau ou Power BI peut enrichir vos rapports et faciliter la compréhension des tendances et des modèles.

Craft a clear and concise narrative around your findings. Explain the implications of your results in the context of the economic question you posed. Leverage AI-powered language models to generate compelling reports or summaries. Platforms like OpenAI's GPT-3 can assist in creating coherent and well-structured content.

Approach economic data analysis as if you're telling a story. Rather than drowning in spreadsheets and numbers, treat each data point as a character in a narrative. Identify the plot by understanding the trends and relationships between variables. This storytelling approach not only makes the analysis more engaging but also helps you extract the underlying meaning from the data, turning a complex puzzle into a coherent and insightful tale of economic dynamics.

8 Pro Tips: 1. Define Your Question:What are you trying to understand? A clear objective guides your data hunt. 2. Gather Reliable Sources: Use reputable databases, government reports, and industry-specific research. 3. Clean and Organize: Ensure data integrity and structure it for efficient analysis. 4. Visualize the Data: Charts, graphs, and dashboards turn numbers into stories. 5. Choose the Right Tools. 6. Correlate and Analyze: Identify patterns, relationships, and trends within the data. 7. Context is King: Consider external factors influencing economic trends. 8. Don't Overfit: Avoid drawing conclusions from limited or biased data 👉 Remember Oversimplification: Don't reduce complex economic realities to single-factor explanations.

1-Critical Thinking: Always be skeptical of your findings. Are there alternative explanations? Consider potential biases and limitations in your data or analysis. 2-Software Tools: Embrace technology! Numerous user-friendly data analysis software and tools (Excel, R, Python) can simplify your journey. 3-Continuous Learning: The world of data analysis evolves rapidly. Stay updated on new techniques and emerging trends to keep your skills sharp.