The Data Science Lifecycle is a systematic approach that helps data scientists transform unprocessed data into insights. It can be used, and choices are informed by data. Usually, it consists of many stages:
Source: Data Science Life Cycle
Data Discovery: This first stage entails determining the business issue, comprehending the goals, and obtaining pertinent data. This process involves gathering data from many sources, such as web scraping, APIs, databases, and sensors.
Data Preparation: This step aims to prepare the raw data for analysis by cleaning and altering it. It includes three steps: data integration (combining data from many sources), data transformation (normalization, scaling, and encoding), and data cleaning (managing missing values, outliers, and duplicates).
Data Visualization and Exploration: In this case, patterns are found, anomalies are detected, and the properties of the data are understood using exploratory data analysis (EDA). This stage involves exploring the data using statistical techniques, summary statistics, and visualizations (such heatmaps, scatter plots, and histograms).
Source: 6 Stages of Data Science Life Cycle
Model Building: Using the prepared data as a training set, a variety of statistical and machine-learning models are created during this stage. Regression, classification, clustering, and deep learning are some of the techniques used to create models that are predictive or insightful.
Model Evaluation: To guarantee the performance, correctness, and dependability of the developed models, they undergo testing and validation. Evaluation measures are used to assess several models and select the best one, including accuracy, recall, mean absolute error, F1-score, and cross-validation.
Deployment: The finished model is put into use in a real-world setting, where it may be integrated into apps or utilized for decision-making. In this phase, pipelines for testing, continuous integration, and performance monitoring of the model are put up.
Monitoring and Maintenance: The model’s performance is tracked after deployment to make sure it keeps producing correct results. Based on fresh information or evolving circumstances, regular updates, retraining, or modifications are done.
Source: The Life Cycle of Data Science
Communication and Reporting: Data scientists use dashboards, reports, or presentations to communicate their results, insights, and practical suggestions to stakeholders. To make sure the outcomes are understood and can be used successfully, this phase is essential.
Iterative feedback loops are used at every level of the Data Science Lifecycle to improve the procedure. It guarantees that the analysis complies with the needs of the business and data quality. This lifecycle facilitates the effective management of data science initiatives and their successful completion.