The data science lifecycle is a structured approach used to solve complex problems and make data-driven decisions. It encompasses five stages: problem definition, data collection, data preparation, model building, and model deployment.

Problem Definition: This first stage is crucial as it sets the foundation for the entire data science project. Before diving into any analysis, it's important to identify and formulate a clear problem statement. For example, let's say our problem is to predict customer churn for a telecommunications company. The goal is to develop a model that can accurately predict which customers are likely to leave the company, enabling the business to take proactive measures to retain them.

Data Collection: Once the problem is defined, the next step is to gather relevant data. There are various methods and sources for data collection, such as surveys, interviews, web scraping, or accessing pre-existing datasets. In our customer churn example, we could collect data on customer demographics, usage patterns, and customer service interactions.

Data Preparation: Raw data often requires significant cleaning and preprocessing before it can be used for analysis. This involves handling missing values, removing outliers, and dealing with noisy data. Moreover, feature selection and engineering techniques may be applied to transform the data into a suitable format. For instance, we could engineer new features like the average length of customer service calls or the number of times a customer has contacted support in the past month.

By following the five stages of the data science lifecycle, we can ensure a systematic and reliable approach to problem-solving. Remember, data science is all about making informed decisions based on data. So get ready to embark on an exciting journey in the world of data science!