Here's how you can impress your superiors by translating business problems into data science solutions.

To impress your superiors with data science, identify core business problems through discussions and active listening. Understand their goals and pain points, then propose data-driven solutions aligned with company objectives. Show how data science can provide clarity and drive outcomes. Bridge the gap between business needs and data insights to demonstrate your value effectively.

Identifying the issue in the data science/ software dev POV is important for bridging the gap. Most of the time, clients themselves don't know what they want. Even if they know what they want, they lack the technical expertise. So, you'll be their consultant for some time (planning phase). Also a common pitfall is, clients will have a consultant, but their suggested method is too old for implementation. Keeping oneself updated in the context of changing landscape is not an easy thing. So, identify the issue, propose a solution in a very simple manner (omitting technicalities) and give a demo. If they have some implementation planning, assess it to the current context. They come to you for their trust in you. Make sure that doesn't break.

The first step is identifying whether a business problem can be translated into a "Data Science" problem. Attending meetings with stakeholders and superiors will definitely be beneficial in understanding the business problems. Performing analysis of the data and visualization of the results in the form of a dashboard is always helpful. The same dashboard can present the results to the stakeholders and explain how data science makes data-driven decision-making easy.

Imagine a business challenge of declining customer retention rates in an e-commerce platform. Collaborate with the marketing team to understand the customer journey and identify potential drop-off points. Analyze customer purchase history data to identify frequently abandoned shopping carts. Complement this with website clickstream data to pinpoint specific product pages or checkout steps causing high abandonment rates. Customer purchase data might have missing zip codes or inconsistent product category labels. Cleanse the data by imputing missing values using appropriate techniques and standardizing product category labels for accurate analysis.

Collect relevant data sources that are essential for addressing the identified issues. Ensure data quality, integrity, and compliance with legal and ethical standards. Leverage both internal and external data sources as needed.

Analyzing data is crucial in transforming business problems into data science solutions. Once data is gathered, employ statistical models, machine learning algorithms, or data visualization methods to extract meaningful insights. The objective is to craft a coherent narrative that elucidates what the data indicates regarding the issue. Presenting findings clearly and comprehensively showcases your ability to convert intricate data into actionable strategies. This skill is invaluable in demonstrating your proficiency and impressing your superiors, underscoring your capacity to bridge data science and business objectives effectively.

Impressing superiors with data science solutions hinges on how you present results. Craft a compelling narrative that connects the data insights to business objectives. Use clear visualizations to highlight key findings and their implications. Tailor your presentation to the audience, focusing on the metrics and outcomes that matter most to them. Emphasize the actionable insights derived from the model and how they address specific business problems. Provide scenarios and forecasts to illustrate potential impacts. Effective communication of results not only showcases your analytical skills but also your ability to drive strategic decisions.