Inference and Prediction
Data science can be a bit of a maze when you're just starting out. So, let's break it down together. The heart of data science lies in two main tasks: inference and prediction. These two tasks are the backbone of what you'll be doing in the field.
What is Data Science?
Before diving into specifics, let's start with a helpful definition. The Initiative for Analytics and Data Science Standards defines data science as "using data to achieve specified goals by designing or applying computational methods for inference or prediction." This definition is like a roadmap for your data science journey. It emphasizes two core activities: inference and prediction, which we'll explore in detail.
Inference: Answering Questions with Data
Inference is all about answering questions with data. Imagine your management team comes to you with a question. They want you to use data to provide a solid answer. Often, the data you'll work with isn't perfect. It might be incomplete or unclear, and this is where your statistics skills come into play.
Suppose an executive asks you to determine if an increase in targeted marketing spending last quarter had a measurable impact. To answer this, you’d analyze the available data, looking for patterns and correlations. Your goal is to draw conclusions that help answer the strategic question posed by your management.
Prediction: Driving Decisions with Data
Prediction is a bit different. Here, you're not just answering a question; you're building a model that helps make ongoing decisions. This is where your machine learning skills come into play. You create predictive models that can forecast outcomes based on the data.
Let's say you're asked to identify which mortgages that are currently late would benefit from a proactive refinance program. You're not just answering a one-off question. Instead, you're building a model that predicts the likelihood of each mortgage benefiting from refinancing. This model helps the organization make day-to-day decisions based on your predictions.
The Rapidly Changing Landscape
Technology in data science evolves quickly, and it can feel overwhelming. But remember, at the core, you're usually doing one of two things: inference or prediction. As you grow in your data science career, you'll get better at distinguishing between these tasks and knowing when to apply each approach.
Practical Tips for Working with Executives
When interacting with executives, it’s crucial to understand whether they need inference or prediction. Here are some tips to help you:
- Ask Clarifying Questions: When given a task, ask specific questions to determine if they are seeking a strategic answer (inference) or a predictive model (prediction).
- Understand the Context: Get a clear understanding of the business context and the specific goals behind the request.
- Communicate Clearly: Explain your approach and the type of analysis you plan to conduct. Make sure they understand whether you're providing an inference or a prediction.
Data science might seem complex, but breaking it down into inference and prediction can make it more manageable. By understanding these core tasks, you’ll be better equipped to tackle data science projects and communicate effectively with your team and management. So, embrace the journey, and remember that each project, whether it involves inference or prediction, is a step forward in your data science career.