Data Science by Aravind choudhary

Data Science is a practical and comprehensive guide designed to help beginners, students, developers, and aspiring data scientists understand the complete data science lifecycle—from collecting and preparing data to analyzing, visualizing, modeling, and extracting meaningful insights. The book begins with the fundamentals of Data Science, Python programming, statistics, and data handling, and gradually progresses to advanced concepts such as exploratory data analysis, data visualization, feature engineering, machine learning, deep learning, and predictive analytics. Readers will learn how to work with real-world datasets using popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn. The book emphasizes practical implementation, problem-solving, model evaluation, and real-world projects to help readers develop job-ready Data Science skills. What You Will Learn Introduction to Data Science Data Science Lifecycle and Workflow Roles and Responsibilities of a Data Scientist Python for Data Science NumPy and Numerical Computing Pandas and Data Manipulation Data Collection and Data Sources Working with CSV, Excel, JSON, and Database Data Data Cleaning and Preprocessing Handling Missing and Duplicate Data Data Transformation and Encoding Exploratory Data Analysis (EDA) Descriptive Statistics Mean, Median, Mode, Variance, and Standard Deviation Probability Fundamentals Correlation and Covariance Data Visualization Matplotlib and Seaborn Charts, Graphs, and Statistical Visualization Feature Engineering Feature Selection Data Scaling and Normalization Handling Categorical Variables Introduction to Machine Learning Supervised and Unsupervised Learning Linear and Logistic Regression K-Nearest Neighbors Decision Trees Random Forest Support Vector Machines Naive Bayes K-Means Clustering Principal Component Analysis Model Training and Testing Train-Test Split and Cross-Validation Model Evaluation and Performance Metrics Confusion Matrix, Precision, Recall, and F1-Score Overfitting and Underfitting Hyperparameter Tuning Ensemble Learning Predictive Analytics Time Series Fundamentals Introduction to Deep Learning Neural Networks Introduction to Natural Language Processing Introduction to Generative AI Data Science with SQL and Databases Working with APIs and External Data Scikit-learn for Machine Learning Model Saving and Deployment Data Science Project Workflow Real-World Data Science Projects