What are the best ways to demonstrate critical thinking skills as a data scientist?

Make sure you understand your data before throwing code at it for charts, tables or models. Sometimes traditional data exploration techniques work fine but other times you need to expand on those. I'm currently working with time series data from vehicles. Traditional data exploration tools just didn't give me a good gut level insight I needed so I created a 3D animation of the time series data and discussed it with subject matter experts. That did the trick! Armed with a much better intuitive understanding of what the data means, I can proceed to more traditional analyses and ask and answer more relevant questions.

Be clear on what business problems you are trying to solve. Define what success metrics that you are trying to maximize/minimize. Identify the most appropriate data source and how to calculate the metrics. Pick the appropriate problem formulation (classification/regression) based on how the model is going to be used e.g. instead of formulating risk score as regression problem (score: 0-99), it might be wise to formulate as classification problem (high/medium/low risk) if downstream task does not differentiate the treatment of two different scores but fall under the same bucket of risk score e.g. both risk score 90 and 95 will receive the same treatment.

Before application, you must decide what you are trying to solve. Ask yourself, ?? ?What will be the primary sources of data? ?? ?How will it simplify the job for your customers and their customers? ?? ?What data is redundant and what is relevant? Once you are done with proper research, the application of your critical thinking skills can become more efficient.

??????As a Data Science professional, critical thinking is an essential skill. ?? The ability to think critically and logically helps analyze data, identify patterns, and develop solutions to problems. ?? One crucial aspect of data science is clearly defining the problem. For example, let's consider a scenario where a retail company wants to increase its sales. ??? The company may start by identifying the problem as low sales, but without defining the problem further, they may not be able to determine the root cause. ?? Is it because of low customer traffic or ineffective marketing strategies..? ?? Each of these problems requires a different approach, and without a clear definition, we may end up implementing the wrong solution. ?

Data scientists must articulate the problem in a way that is specific, measurable, and aligned with the broader organizational goals. Clear problem definition sets the foundation for effective analysis and solution development. It involves asking the right questions, and ensuring that the problem statement is not only well-understood but also framed in a manner that guides subsequent analytical processes.

Essentials : 1. Select data methods and tools based on problem context, ensuring alignment with objectives and constraints. 2. Justify choices with logical reasoning and evidence, emphasizing how selected methods address specific questions. 3. Clearly articulate how chosen tools facilitate data collection, processing, analysis, visualization, and modeling for effective problem-solving.

Strategic selection of methods and tools is the compass guiding data science endeavors. Opt for approaches aligned with the problem's nuances, ensuring a harmonious fusion of precision and efficiency.

Demonstrating critical thinking skills as a data scientist extends to the careful selection of appropriate methods and tools for analysis. This involves a good understanding of various statistical techniques, machine learning algorithms, and data processing tools. A skilled data scientist assesses the unique characteristics of the data at hand and strategically chooses methods that align with the problem statement.

In choosing the most appropriate methods and tools, you have to justify why such methods and tools are most appropriate, how closely those tools suit the type of your data collection and if you are choosing any method or tool, what is the rationale behind?If any method and tool is given priority over other available and more approprate tools, what is the reasoning behind that selection.

Keeping data quality and accuracy is paramount for any data science project since small error in data may lead to a wrong interpretation or affect an important decision. A variety of data quality tools and frameworks like Soda Core, Great Expectations, dbt core etc. can be use to monitor and evaluate your data, inconsistencies, defects in an automated way to avoid negatively impacting your data analysis, reporting and decision-making.

Evaluate data thoroughly for accuracy, completeness, consistency, relevance, timeliness, source reliability, and potential biases. Document the process for transparency, ensuring a reliable foundation for analysis.

Critical thinking in data science begins with an evaluation of the quality and reliability of the data. A proficient data scientist recognizes the significance of data sources, identifies potential biases, and assesses the completeness and accuracy of the dataset. Rigorous data validation ensures that any insights derived are trustworthy. This involves a critical examination of data collection methods, understanding potential sources of error, and implementing appropriate preprocessing steps to enhance data integrity.

Essentials 1. Scrutinize data sources for errors, inconsistencies, outliers, and biases to ensure high-quality and reliable datasets. 2. Implement necessary corrections and transformations, addressing missing values, and understand the limitations and assumptions of the data. 3. Clearly communicate uncertainties and risks associated with the data, showcasing transparency in your analysis and decision-making.

In evaluating the quality and reliability of data, the most important thing is the source of data, how credible the data source is? Another aspect to that is, the clarity regarding selection of data, stating clearly any bias in data selection while providing reasoning and justification for that bias and relating that type of data selection with the objectives of data analysis or research. If the bias in data selection is by purpose, that purpose shall be stated clearly and in that situation, there is no need of data corrections or transformations, however the rationale behind the bias in data selection shall be justified with valid reasoning.

Critical thinking is the process of purposeful reflective judgment in order to decide what to believe and/ or what to do. It is therefore imperative that your analysis and interpretation are free from any agenda or bias. You must therefore have the intent and the courage to question yourself, be aware of your mental processes, evaluate evidence and think independently when you are analysing or interpreting data. Your analysis has to demonstrate truth-seeking behaviour, open-mindedness and perseverance. The critical questions you need to answer are: do I trust the data, do I trust my team, and do I trust myself? And finally, am I queuing up for reassuring fiction or for inconvenient truths?

It does not work. Why? It works too well. Why? Always check that your model is actually doing what is reasonable with the data.

Engage in a discerning analysis of data, going beyond surface-level insights. Question assumptions, identify patterns, and draw meaningful conclusions, ensuring a nuanced and accurate interpretation of the dataset.

I’ve found that the simple task of coming up with a ballpark estimate for your result before doing any analytics can go a really long way. Many people are tempted to run analyses, get an answer, and assume the answer is right just because an answer was produced. But having that gut check can help you quickly determine if your answer is likely correct or not. It’s the same concept as the common interview question “how many piano tuners are there in Chicago?” It might take a bit of figuring to get that estimate but it’s worth it. Should your metric be in the thousands? Tens of thousands? Millions? Negative?

Exhibiting critical thinking skills involves the thorough analysis and interpretation of data. Beyond mere computation, you have to critically evaluate the patterns, trends, and anomalies within the dataset. This entails questioning assumptions, considering alternative explanations, and recognizing potential pitfalls in the analytical process. This critical approach not only ensures the accuracy of results but also reflects the capacity to extract meaningful and actionable information from complex datasets, an essential aspect of critical thinking in the realm of data science.

Effectively communicating findings and recommendations is a key facet of demonstrating critical thinking skills as a data scientist. Beyond the analysis, you must convey complex insights in a clear, concise, and comprehensible manner. This involves tailoring the presentation to the target audience, emphasizing the most relevant information, and addressing potential uncertainties transparently. Effective communication ensures that decision-makers can understand, trust, and act upon the results, underlining the data scientist's capacity to think critically about how their analyses contribute to informed decision-making within the broader context of the organization.

Convey insights clearly and persuasively. Craft a compelling narrative around your findings, ensuring stakeholders grasp the significance. Provide actionable recommendations, fostering informed decision-making based on your data-driven insights.

1. Craft clear and concise messages: Convey complex findings using simple language and structure to ensure easy comprehension. 2. Choose the right communication medium: Opt for formats that suit your audience, such as visualizations for technical peers and executive summaries for non-technical stakeholders. 3. Tailor messages to the audience: Understand your audience's knowledge level and tailor your communication to meet their needs, emphasizing key points and practical implications.

1. Actively seek feedback to enhance your skills. 2. Embrace continuous learning to stay ahead in the dynamic data science landscape. 3. Explore new methods and tools, adapting to the evolving field.

Embrace a culture of improvement. Actively seek feedback on your work, be it from peers or outcomes. Iteratively refine your approaches, leveraging insights gained to enhance the quality and impact of your data science endeavors.

I agree with the experts' views above, particularly about the need for effective communication and narratives. In other words, the 'explainability' part of data science is important - this requires domain expertise. For example, for a data science project for a leading hospital about donor behavior, our data science approach turned out to be quite accurate in terms of predictions, but the narrative didn't appear sufficiently clear, requiring strong conceptual logic. So, we also developed sound economic theory-based models complementing our data science work. This gave us a lot of insights that could be related to the forecasts and predictions that we got from the data science part. This helped us communicate effectively with the client.

Critical thinking by definition means to criticize your analytical approach. An ability to critically think that will help identify your own bias, or bias in the data or the analysis.

An outside-the-box method to demonstrate critical thinking as a data scientist is to challenge conventional assumptions and explore alternative perspectives through "Counterfactual Analysis." This approach: Asks "What-if?": Encourages questioning the status quo by considering hypothetical scenarios. Identifies the Scenario: Defines baseline and alternative scenarios to compare outcomes. Enhances Understanding of Causality: Distinguishes between correlation and causation by analyzing different possible events. Encourages Exploration: Promotes the examination of multiple and alternative scenarios to uncover potential impacts and insights.