Python & Python Library with Data Science

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