Pillars of data science

The four key pillars of data science are often described as:

Data Collection and Acquisition This is the foundational step of data science. It involves gathering data from various sources, which could include databases, APIs, sensors, surveys, or public datasets. The quality and accuracy of data collection are crucial to ensure that any analysis or models built later on are reliable.

Data Cleaning and Preprocessing Raw data is often messy and unstructured. Data cleaning involves handling missing values, removing duplicates, dealing with outliers, and transforming data into a more usable form. Preprocessing might also include normalization, encoding categorical variables, and feature extraction.

Exploratory Data Analysis (EDA) EDA is the process of analyzing data sets visually and statistically to uncover patterns, trends, and insights. It includes generating summaries of data, plotting graphs, and identifying relationships between variables. This stage is crucial for understanding the data and informing the next steps in analysis or model building.

Modeling and Algorithm Development In this stage, data scientists apply machine learning or statistical models to the data. This could involve supervised or unsupervised learning, regression analysis, clustering, classification, etc. It's important to choose the right algorithms based on the problem at hand and to iteratively tune parameters to improve model performance.

Together, these four pillars form the core structure of the data science workflow. They work in tandem to enable data scientists to extract meaningful insights and predictions from raw data.