Data Cleaning and Preprocessing: Raw data often contains errors, missing values, inconsistencies, and noise. Data scientists preprocess and clean the data to make it suitable for analysis. This involves tasks like removing duplicates, handling missing values, normalization, and feature scaling.
Exploratory Data Analysis (EDA): EDA involves exploring the data to understand its underlying patterns, distributions, and relationships. Data visualization techniques like histograms, scatter plots, and heatmaps are commonly used to gain insights into the data.
Statistical Analysis: Statistical methods are applied to analyze the data and infer conclusions. This includes hypothesis testing, regression analysis, clustering, classification, and more.
Machine Learning: Machine learning algorithms are used to build predictive models and make data-driven decisions. These algorithms include supervised learning (classification and regression), unsupervised learning (clustering and dimensionality reduction), and reinforcement learning.
