Overview of Data Science.

Data Science workflow and stage.

Core elements: data gathering, cleaning, analysis, visualization, machine learning, deployment

Installing Python, Jupyter Notebook, and Anaconda

Configuring essential libraries (NumPy, Pandas, Matplotlib, Seaborn)

Introduction to Python programming

Working with data structures and functions

Basic data manipulation and plotting

Methods for collecting data

Web scraping and using APIs

Handling structured and unstructured data

Managing missing data

Data normalization and feature engineering

Removing duplicates and handling outliers

Descriptive statistics and data patterns

Visualization using Matplotlib and Seaborn

Creating various plots (line, bar, scatter, histogram, boxplot)

Advanced visualizations (heatmaps, pair plots)

Interactive plots using Plotly

Introduction to machine learning concepts

Key models: regression, classification, clustering

Linear and logistic regression

Decision Trees and Random Forests

Clustering algorithms (K-Means, hierarchical)

Dimensionality reduction (PCA)

Applications of unsupervised learning

Introduction to neural networks

Basics of TensorFlow and Keras

Designing and training a neural network

Completing a comprehensive data science project

End-to-end project workflow: problem definition, data collection, analysis, modeling, and deployment