Data Science. Part 1
Data science involves various key concepts and techniques that are applied to extract valuable insights from large datasets. Here are some of the essential aspects and their practical applications: Data Cleaning and Preprocessing : Data cleaning involves handling missing values, dealing with outliers, and ensuring data consistency. Preprocessing techniques include normalization, standardization, and feature scaling. These steps ensure the data is in a suitable format for analysis and modeling. Example : In a retail setting, data scientists clean and preprocess transactional data to remove duplicate entries, handle missing values, and standardize product names. This enables accurate analysis of customer purchasing patterns and inventory management. Exploratory Data Analysis (EDA) : EDA involves analyzing and visualizing the data to gain insights, understand patterns, and identify relationships between variables. It helps in formulating hypotheses and guiding further analysis. Example : ...