Python & Python Library with Data Science by Aravind choudhary

Python & Python Library with Data Science is a practical and comprehensive guide designed to help readers learn Python programming and apply powerful Python libraries to Data Science problems. The book takes readers from Python fundamentals to data analysis, visualization, statistics, and Machine Learning. Readers will learn how to use essential libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn to work with real-world datasets. The book explains how to collect, clean, transform, analyze, visualize, and interpret data before applying Machine Learning techniques to solve practical problems. With practical examples, programming exercises, and real-world projects, this book provides a step-by-step learning path for students, beginners, Python developers, Data Analysts, and aspiring Data Scientists. What You Will Learn Python programming fundamentals Variables, data types, operators, and control statements Functions, modules, packages, and exception handling Lists, tuples, sets, and dictionaries Object-Oriented Programming with Python File handling and JSON processing Python libraries and package management Introduction to Data Science Data Science lifecycle and workflow NumPy for numerical computing NumPy arrays, indexing, slicing, and broadcasting Mathematical and statistical operations with NumPy Pandas Series and DataFrames Importing and exporting CSV, Excel, JSON, and other datasets Data cleaning and preprocessing with Pandas Handling missing and duplicate data Data filtering and transformation Grouping, aggregation, merging, and joining datasets Exploratory Data Analysis (EDA) Matplotlib for data visualization Line charts, bar charts, histograms, and scatter plots Seaborn for statistical visualization Advanced data visualization techniques Statistics for Data Science 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 Introduction to Machine Learning Supervised and Unsupervised Learning Scikit-Learn for Machine Learning Linear Regression Logistic Regression K-Nearest Neighbors (KNN) Decision Trees Random Forest Support Vector Machines (SVM) Naive Bayes K-Means Clustering Principal Component Analysis (PCA) Model training and testing Cross-validation Model evaluation and performance metrics Confusion Matrix Accuracy, Precision, Recall, and F1-Score MAE, MSE, RMSE, and R² 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