¿Cuál es su proceso para comunicar la resolución de problemas en Ciencia de Datos?

Consider a healthcare analytics project aiming to optimize patient care. Beyond traditional steps, collaboration involves engaging not just data scientists but also clinicians and ethicists. Through interdisciplinary dialogue, diverse insights emerge, refining the problem definition. Ethical considerations come to the forefront when dealing with sensitive patient data. Discussing potential biases ensures fair algorithmic decisions. This collaborative and ethical approach fortifies the analysis, aligning it with the broader goal of improving healthcare outcomes responsibly.

Here's a breakdown of what it entails: 1. Understanding the business's or stakeholders' core objectives and needs. 2. Type of Problem: Identifying whether the problem is a prediction, classification, optimization, exploration, or other type. 3. Relevance and Impact: Considering how solving this problem will affect the business or process. 4. Engaging with stakeholders to align expectations and understanding. 5. Evaluating the availability of technical resources, tools, and team capabilities. 6.Creating a problem statement that is specific, measurable, achievable, timely, and relevant

I follow these steps: 1 - Know what is the question you need to answer. For instance, is it knowing which clients to visit before they churn? or measuring the impact of the visit? or understanding which characteristics are related to churn? 2 - Learn what would help solve the problem - usually it's one of the following: categorizing or grouping data; identifying patterns or anomalies; isolating cause/effect; showing correlations or predicting outcomes. It will lead to the methods to be used (clusterization, experimentation, etc.) 3 - Learn the data-sets, time ranges and unit of observations to be studied. 4 - Know what output you need to deliver and how it would be used (dashboard, ML for production, etc.)

1. Define Problem Clearly: • Clearly articulate the problem's scope and objectives. 2. Audience Understanding: • Tailor communication to the audience's technical level. 3. Step-by-Step Explanation: • Break down problem-solving into clear steps. 4. Visual Aids: • Use charts or diagrams to illustrate complex concepts. 5. Storytelling Approach: • Frame explanations as a narrative for clarity. 6. Avoid Jargon: • Minimize technical jargon and explain terms. 7. Highlight Key Insights: • Emphasize key findings and their impact. 8. Provide Context: • Offer context around data sources and methodologies. 9. Interactive Communication: • Encourage questions and foster a collaborative environment. 10. Quantify Results: • Use metrics to demonstrate the i

This is a key step in the data science journey within every organization. 1. Always engage the business stakeholders to discuss the business problems. Never make any assumptions (you are the data expert but they are the domain experts) 2. After narrowing down on the problem, next step is to discuss the key success criteria (in terms of business KPIs etc.) 3. Conduct extensive literature research to understand industry benchmarks around the identified business problems. (helps to see if the key success criteria is above or below this benchmark)

Defining the problem is the cornerstone of effective problem-solving in Data Science. Clearly articulating the context, objectives, and constraints sets the foundation for a successful project. It's essential to engage stakeholders, identify key questions, and establish a roadmap. In my experience, a well-defined problem not only guides the analysis but also streamlines communication, ensuring everyone involved understands the goals and expectations upfront.

Deep dive in the domain knowledge understanding is the first step we should make ourselves acquainted with, i.e. we have to do our own extensive research about the facts and concepts revolving around the problem. It helps a lot in formulation of accurate Hypotheses and assumptions.

This is the most important step in the whole process. Problems sometimes are not defined properly, are unstructured and it is the Data Scientist who should collaborate with the stakeholder on understanding scope of the problem.

We dive into the depths of datasets, armed with tools and curiosity, to discover patterns, anomalies, and relationships that lie beneath the surface during this stage of the data science process. It's a fusion of art and science, with statistics colliding with visualizations to reveal stories that numbers alone cannot tell. This exploratory phase is about more than just understanding what the data contains; it is also about challenging assumptions, challenging preconceived notions, and sparking insights that will serve as the foundation for all subsequent analysis. It is here that data begins to speak, whispering clues that guide the journey from raw data to actionable knowledge, transforming the unknown into the known.

After defining the problem and ideal solutions, you can study your data limitations. First, assess the data quality: without this, any method becomes a "garbage in, garbage out" one. Start by checking for missing, invalid, or outlier data, and decide how to manage it (data imputation, exclusion, winsorization, transformations such as logarithmic, IHS, etc.). Post-cleansing, use exploratory data analysis (EDA) to give you some sense of answers in terms of the original problem that you defined. EDA helps identify potential features and prepping the data for deeper analysis. It also offers a transparent approach, essential before employing complex methods that might seem black box-ish to stakeholders.

Exploring data is one of the top time-taking steps during whole problem solving process. Based on the acquired domain knowledge and hypothesis, we play with the data to get its ins and outs. This can even help us formulation of new features, which can make the task easy for the model to understand patterns

The selection of methods depend on the initial steps of (a) understanding the problem and (b) the data limitations. Restricted by the problem and the limitations, the next step is to explore which tools can provide solid evidence to test your hypothesis. Consider combining multiple methods, be creative. For example, doubly robust methods for estimating treatment effects merge the benefits of both propensity score methods and outcome regression models. At last, remember that each method depends on assumptions and that when these are not met, your results could be biased. Therefore, verify your assumptions' validity before trusting your findings. Whenever possible, use multiple methods to cross-check the consistency of your results.

Applying the right methods is a pivotal phase where the chosen tools, whether statistical tests or machine learning models, transform data into actionable insights. In my experience, selecting methods aligning with data complexity ensures accurate outcomes. It's crucial to balance sophistication with simplicity, choosing methods that effectively address the defined problem. This step lays the groundwork for deriving meaningful results and validating initial assumptions, enhancing the overall effectiveness of the problem-solving process.

1 - Methods rely on assumptions; their violation can lead to biased results. Start by verifying these assumptions. Even experiments (gold standard), can fall short if issues like insufficient take-up rate or SUTVA violations (spillover) arise. 2 - Benchmark your results against existing literature, behavioral or economic theories, etc., to see if they align with industry standards or to uncover anomalies that deserve further investigation. 3 - Finally, focus on the implications of your findings to formulate new hypotheses, and suggest actionable steps. E.g. if you find that a significant predictor of client churn is a more than 15% decrease in early usage, a recommendation could be an early warning system to address these cases.

Model Interpretation is one of the crucial step during whole process. We get to understand more about the "black box". Say e.g. if we had two features with significantly high correlation (<0.8) and in feature importance plot, one of them has extremely small contribution, we can remove that and retrain. That can improve the model's performance. The performance of the model on the test set defines its robustness and generalizing ability. If the original dataset itself is splitted into train-test-validation, try using different chunks everytime and check the model's performance on those test sets. Multiple performance interpretations helps a lot.

This critical stage goes beyond raw data analysis to turn complex data findings into meaningful insights and narratives. We navigate the complexities of statistical outputs, graphs, and models, extracting their essence to tell a compelling story. The interpretation of results is more than just understanding what the data says; it is also about understanding what it means in the context of the problem, which frequently challenges our hypotheses and shifts our perspective. Data transcends its numerical nature in this moment of clarity, becoming a powerful lens through which we can see the world, make informed decisions, and drive impactful change, making this phase not only analytical, but profoundly enlightening.

Data visualization is the art of transforming the complex language of data into a visual symphony that speaks to the soul. It's where numbers transform into vibrant graphs, charts, and maps that tell a more compelling story than words ever could. These visual masterpieces not only captivate the eye, but also simplify the complex, rendering the abstract tangible and the incomprehensible clear. Visualization is more than just data analysis; it is a link between data and decision-makers, a tool that can sway opinions, start conversations, and spark change. Every pixel and plot in this world is a verse in the epic poem of data, inviting the audience on a journey of discovery, understanding, and insight.

Looking to illustrate your problem? E.g., “Have sales of our premier product declined in the past six months?” You can illustrate this with plots showing sales over the last six months, paired with a similar graph from the previous year, to rule out seasonality as the cause. Need to explain the estimated impact of an ad campaign? Present a time series plot of daily clicks, alongside clicks in unaffected regions. This helps in visualizing how the metric would have evolved after the intervention if the intervention had never occurred (Brodersen et al., 2015). Graphical representations can clarify and enhance understanding, effectively communicating key points of your analysis to executives that do not have a data science background.

This is where your storytelling skills shine! Communicating a solution effectively involves considering your audience. Are they technical experts or non-tech-savvy managers? Knowing this sets the tone for your presentation and report. Another crucial technique is anticipating questions based on the results. Be prepared for queries and proactively address them with well-thought-out responses and supporting materials. 🌟

Sometimes the models don't perform well to that extent as expected. Apart from the popular techniques at hand ,e.g. hyperparameter optimisation, cross-validation, etc., we should follow the ongoing researches in that specific field which are giving state-of-the-art performance. Also there are plenty of resources on platforms like Kaggle. Try exploring all possibilities that can help model understand the data better.

- Audience-Specific Messaging: Adapt to your audience's understanding, focusing on business impact or technical details as needed. - Start with 'Why': Frame data insights within the context of the business problem. - Storytelling with Data: Use clear visuals to explain complex data. - Simplicity and Relatability: Avoid jargon, using plain language and real-world examples. - Engage the Audience: Encourage interaction and address questions with data-driven evidence. - Actionable Insights: Translate findings into clear, practical recommendations. - Compelling Data Narratives: Highlight the story behind the data.

Often colleagues and others asking advice and ideas to optimize their experience effectiveness and environment when assuming brand new data science leadership roles. I always suggest dedicating the inital days to weeks to architecting a collaborative roadmap that builds to further clear okr kpi corporate deliverable capabilities and is constructed leveraging and dependent on data science methods implementations etc that all deliver value in themselves. All these things in reality do often connect cross catalyze differentiate in multi pronged and synergistic interpotentiating ways. Also - this mapping naturally leads to greater vigilance in harvesting value from efforts and methods to the biggest deliveries. The power of we