Description
Introduction to Data Science
Data Science involves analyzing large datasets to uncover patterns and insights. It is used in various fields like healthcare, finance, and marketing. The process includes data collection, cleaning, analysis, and model interpretation.
Basic Concepts
Data Types: Structured (numbers, categories) vs Unstructured (text, images).
Data Collection: Methods include surveys, APIs, and web scraping.
Data Cleaning: Handling missing data, outliers, and applying transformations like normalization.
Exploratory Data Analysis (EDA)
Data Visualization: Tools like Matplotlib and Seaborn create graphs to identify patterns.
Statistical Summaries: Calculating mean, median, variance, etc.
Correlation: Analyzing relationships between variables.
Basic Machine Learning Concepts
Supervised Learning: Algorithms like regression and classification (e.g., decision trees, random forests).
Unsupervised Learning: Techniques like K-means clustering and PCA for data grouping.
Model Evaluation: Methods like accuracy, precision, recall, and cross-validation.
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