Here's how you can use logical reasoning to uncover insights in large datasets for business decisions.

One thing I've found helpful is starting with a clear definition of goals before analyzing data. Understanding the specific business objectives behind the data ensures that your logical reasoning is focused and effective. Whether you're aiming to increase sales, reduce costs, or improve customer satisfaction, defining these goals frames your analysis and keeps you on track. This targeted approach prevents distractions by irrelevant data, maintaining a clear line of inquiry and ensuring that your insights are aligned with your business needs. #DataGoals #FocusedAnalysis #BusinessObjectives

I leverage a framework called the "Leaning Tower of Data Analysis" to uncover insights in large datasets for business decisions. On the ground floor, I define clear goals and clean the dataset. On the second floor, I summarize data with statistical measures. Moving up, I analyze data distribution and compare subsets on the third floor, and identify trends on the fourth. On the fifth floor, I detect deviations and explore correlations. The sixth floor introduces machine learning for classification. On the seventh, I tailor the analysis to specific industries. Finally, at the top, I synthesize findings into a compelling narrative. This structured approach ensures focused and actionable insights.

From my experience from working on many projects, defining a proper goal is very crucial before starting any project as it keeps on task and effectively directs the analysis in the right direction without getting lost by any irrelevant data which doesn't align with the goal defined.

In my experience, defining goals is the cornerstone of any successful data analysis project. Without clear objectives, it's easy to become overwhelmed by the sheer volume of data. Specific goals act as a compass, directing your efforts toward meaningful insights that align with business priorities. They help you stay focused and efficient, ensuring that your analysis is purposeful and actionable.

Deriving insights from unclean data can result in inaccurate, misleading, or incomplete conclusions due to the presence of errors, inconsistencies, duplicates, and missing values that skew analysis. To address this, data cleaning is essential. This process involves identifying and correcting errors, fixing inaccuracies and typos, removing duplicates to ensure each record is unique, and handling missing values by filling in or appropriately managing gaps in the data. By cleaning data, we improve the quality and reliability of the dataset, leading to more accurate analysis, better insights, and more reliable decision-making.

Data cleaning is crucial in industry, much like refining raw materials. Unclean data can lead to flawed analyses and poor decisions. In healthcare, for instance, inaccurate patient data can cause misdiagnoses, while in finance, incorrect transaction records can distort risk assessments. Companies like Google and Amazon prioritize data cleaning to ensure their algorithms and models use high-quality data, maintaining their competitive edge. This practice is essential for delivering reliable products and services, making data cleaning a fundamental process that directly impacts outcomes and profitability.

In my experience, the quality of insights hinges on clean data. Removing errors, duplicates, and irrelevant entries is essential. This step ensures you're working with accurate information, providing a reliable foundation for your analysis. Think of it like preparing a clean canvas before painting; it's crucial for achieving clear and interpretable results. Cleaning data may seem tedious, but it's a vital part of logical reasoning, enabling you to draw trustworthy conclusions that inform sound business decisions. #DataCleaning #AccurateInsights #FoundationForAnalysis

The principle of "garbage in, garbage out" is fundamental in data science. The quality of your insights is only as good as the data you analyze. An example of this is when Amazon developed a model to analyze resumes for hiring employees. The model was trained on an imbalanced dataset that had a bias against female candidates, leading it to reject female applications disproportionately. This underscores the importance of cleaning data to ensure there are no biases or errors. Data cleaning involves removing errors, duplicates, and irrelevant entries, and it’s a critical part of logical reasoning because it lays a trustworthy foundation for your analysis. Ensuring the data is free of biases helps produce fair and actionable insights.

Data cleaning completely influences any insights you receive out of data-assets. > Data transformation and feature engineering are the 2 major aspects involved in data cleaning > Feature analysis, feature correlations and predictive modelling are all great functions of clean data. A clear canvas always delivers concise and highly interpretable details whereas a bad canvas doesn't provide interpretability.

One time at work, I found unexpected correlations by analyzing patterns in clean data. Asking targeted questions like why sales spiked or what drives customer churn can reveal hidden relationships. Using techniques like regression analysis or clustering helps uncover these insights. This phase showcases your logical reasoning as you piece together the data puzzle, transforming raw data into actionable business intelligence. The patterns you identify can guide strategic decisions and uncover new opportunities. #PatternAnalysis #DataCorrelations #AnalyticalTechniques

Analyzing patterns in data is where the real magic happens. Once your data is clean, you can uncover hidden relationships and correlations that drive valuable insights. Popular methods for pattern analysis include time series analysis, decision trees, and association rule learning.

The most underrated skills in data science is domain understanding. I've noticed that data scientists often rely too heavily on the direction provided by clients. While clients may know what they want, they don't always know what they can get or what they truly need. I believe in future, the most sought after and impactful data scientists would rarely be industry-agnostic. When data scientists really understand the industry they're working in, it creates a synergy that can greatly enhance the development, impact, and overall usefulness of the model by identifying opportunities that others might miss, communicate more effectively with stakeholders, and build more robust, impactful models that align closely with business objectives.

One thing I've found helpful is incorporating a structured approach to pattern recognition and logical reasoning. For instance, when analyzing sales data, I dig deep into temporal trends and seasonality. Asking the right questions makes all the difference: Why did sales spike last quarter? What external factors were at play, like holidays or campaigns? Utilizing regression analysis, I differentiate between correlation and causation, ensuring we focus on actionable insights. In another instance of analyzing customer churn, clustering techniques have allowed us to segment customers based on behavior, revealing that specific demographics are more prone to churn.

En este punto, puede que con datos limpios sea confuso saber por donde empezar, por espacio, me limitaré a un problema de clasificación binaria. 1. Correlación Biserial (acompañado de transformaciones apropiadas). 2. Utilización de valores de información y pesos de evidencia. 3. Pruebas no paramétricas para comparar distribuciones. 4. MrMr es un excelente algoritmo para entender poder predictivo de las variables. 5. Probar algoritmos que no requieren de mucho tratamiento de datos (XGBoost) acompañado de Análisis con valores Shapley puede generar valor para entender dinámica entre variables.

In my experience, testing hypotheses is where logical reasoning shines. Setting up controlled experiments or performing statistical tests validates your findings. This step is crucial to ensure that your conclusions are not due to chance or external factors. Rigorous testing builds confidence in your insights, enabling you to make informed business decisions. By confirming hypotheses, you can trust that your data-driven decisions are based on solid evidence, minimizing risks and maximizing benefits. #HypothesisTesting #DataValidation #InformedDecisions

Testing hypotheses is crucial for validating insights drawn from data. Some ways to get started include designing controlled experiments and performing statistical tests to ensure your findings are reliable. One thing I've found helpful is using A/B testing to compare outcomes between different groups. Actually, I disagree with relying solely on observational data; controlled experiments can more effectively isolate variables and confirm causality. For example, an e-commerce company improved their conversion rates by testing different website designs through A/B testing, ensuring their changes led to actual improvements rather than random variations.

What I think is one should Select appropriate statistical tests to evaluate your hypotheses. There are many different statistical tests available, so it's important to choose one that is suited to your data and research question.

One thing I've found helpful when testing hypotheses in large datasets is to approach the data with a clear and methodical mindset. Establishing controlled experiments or performing statistical tests is essential to validate any insights we think we've uncovered. Logical reasoning plays a key role here—it's not just about running numbers but about ensuring that the signals we identify aren't artifacts of random noise. I've often leveraged A/B testing along with more complex statistical methodologies like regression analysis to confirm hypotheses. Rigorous testing like this not only bolsters our confidence in the conclusions but also provides a solid foundation for making informed business decisions.

Decision making should be proportional to these things: 1. The problem statement 2. The objectives 3. The outcomes While delivering your analytic insights, you need to ensure you never rely on intuitions and always adhere to precise data driven outcomes. Anything not aligning or worsening the outcomes or objectives should be scraped of well in advance.

Decision time is here and Logical Reasoning would help us deduce and make the choices, of course strengthened by the fundamental statistical maths that we have used. All the effort was put in for this stage, laborious hours put to structuring, and cleaning of data and then forming patterns, building and testing hypothesis statistically, now the right conclusion both in a single sentence and more often a more nuanced and detailed explanation would emerge and help us allocate resources or evaluate the claims of a HIPPO/ or the blue eyed manager, calm logic and technically sound analysis can literally save the organisation from opportunity costs all the time, and real dollars most of the time.

Making decisions based on validated insights is the culmination of data analysis. Some ways to get started include weighing potential outcomes and risks using decision matrices and considering both quantitative data and qualitative business context. One thing I've found helpful is holding stakeholder meetings to discuss insights and gather diverse perspectives. Actually, I disagree with relying solely on data; understanding the broader business environment is equally important. For example, a retail company successfully expanded into new markets by combining data-driven insights on customer preferences with strategic discussions about market trends and competitive landscape.

After drawing insights from the data, we can make informed decisions which either help in customer satisfaction or can generate revenues for the company. The business decisions should be validated by the insights which are drawn from the data after doing rigorous analysis and testing.

Decision making depends on : 1. Which issue we are concerned upon 2. The efficient way to understand those results 3. Finding the right combination of business needs and analytical directives

Remember that data analysis is ongoing. Regularly update your analysis as new data emerges. For example, continuously refine customer satisfaction strategies by incorporating new feedback. This iterative approach ensures that your business remains agile, responsive, and informed by the latest data.

Iterate and Refine: Business landscapes are dynamic. Regularly re-evaluate your insights against new data and evolving market conditions. This iterative process ensures your decisions remain grounded in the latest information, maximizing their effectiveness.

From a technical perspective, data science/analytics code must be designed to support iterative analysis seamlessly. This involves creating modular, reusable, and maintainable code that can easily integrate new datasets to existing ones, and adapt to changing business requirements. Automated workflows and version control systems play a crucial role in managing updates. By incorporating these practices from the start of a project, data analysts can efficiently re-evaluate and refine their models, keeping business insights accurate and up-to-date.

Iteration ensures that the solution remains effective and adapts to any new issues or changes in customer expectations, improving long-term satisfaction.

Hay un frase muy importante en el análisis de datos, "esta vez será diferente." Cada años vemos nuevos fenómenos en el mercado, y eventos pequeños pero de grande repercusión pueden cambiar la proyección de los eventos. Se busca tener un análisis acertado del futuro, pero para tenerlo no podemos buscar un forecast estático ni rígido; ya que el mercado no se comporta de esta manera. Lo más importante es deja espacio para los cambios, que siempre son la constante