Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It integrates several disciplines, including statistics, data analysis, machine learning, and computer science, to analyze and interpret the data. The following provides a more organized and orderly graphical outline of the core methodological steps of data science:

Data collection: which entails data scraping for data from online resources, machine recorded sensors, and surveys. Data cleaning and preparation which may include deletion of duplicates , data type conversion, data normalization and handling of null values .

Statistical analysis: whatever be it hypothesis testing, linear regression or ANOVA analysis. EDA such as correlated analysis, and outlier detection. Machine learning step which comprises of the algorithms utilized to analyze the patterns, and retrieve insights such clustering, and predictive modeling. Model validation to ascertain its performance on unseen data through the use of cross-validation .

Data Visualization: finally the graphical representation of data through chart diagrams, scatter plots, and geospatial maps thus aiding nontechnical consumers in understanding the revealed insights.

Big data tools: SQL and NoSQL databases, Hadoop ecosystem, or Databricks Apache spark tool mostly used for big data descriptive analysis. Advanced analytics that includes predictive analytics and deep learning . Ethics and data privacy, nevertheless these would not fit into the graphical representation due to them being applied before the data collection, during the data preparation, during analysis.