Python with Data Science by Aravind choudhary

Python with Data Science is a practical and comprehensive guide designed to help beginners and students learn Data Science using Python. The book starts with Python programming fundamentals and gradually introduces the essential concepts, tools, and techniques required to analyze real-world data and build data-driven solutions. Readers will learn how to work with popular Python libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn. The book covers data collection, cleaning, preprocessing, exploratory data analysis, statistical analysis, visualization, and Machine Learning. With practical examples, exercises, and real-world projects, this book helps readers develop the skills required to transform raw data into meaningful insights and build predictive models using Python. What You Will Learn Introduction to Data Science Data Science lifecycle and workflow Python programming fundamentals Variables, data types, operators, and control statements Functions, modules, and packages Lists, tuples, sets, and dictionaries Object-Oriented Programming with Python File handling and JSON processing Python libraries for Data Science NumPy for numerical computing NumPy arrays, indexing, slicing, and broadcasting Mathematical and statistical operations using NumPy Pandas Series and DataFrames Importing and exporting CSV, Excel, JSON, and other datasets Data cleaning and preprocessing Handling missing and duplicate data Data transformation and filtering Grouping, aggregation, merging, and joining datasets Exploratory Data Analysis (EDA) Data visualization with Matplotlib Data visualization with Seaborn Charts, graphs, and statistical visualizations Introduction to Statistics for Data Science Descriptive statistics Mean, median, mode, variance, and standard deviation Probability fundamentals Correlation and covariance Data preprocessing for Machine Learning Feature engineering and feature selection Encoding categorical data Feature scaling and normalization Supervised Learning Linear Regression Logistic Regression K-Nearest Neighbors (KNN) Decision Trees Random Forest Support Vector Machines (SVM) Naive Bayes Unsupervised Learning K-Means Clustering Hierarchical Clustering Principal Component Analysis (PCA) Model evaluation and performance metrics Confusion Matrix Accuracy, Precision, Recall, and F1-Score MAE, MSE, RMSE, and R² Train-test split and cross-validation Overfitting and underfitting Hyperparameter tuning Grid Search and Randomized Search Predictive analytics Introduction to Deep Learning Introduction to Natural Language Processing Working with real-world datasets Building data-driven applications Practical Data Science projects