Course Title: Data Science with Python

Module 1: Introduction to Data Science and Python

• Overview of Data Science
• Introduction to Python for Data Science
• Setting Up Python Environment (Anaconda, Jupyter)

Module 2: Data Manipulation with Python

• Introduction to NumPy and Pandas
• Data Cleaning and Preprocessing
• Working with DataFrame Operations
• Handling Missing Data and Outliers

Module 3: Data Visualization with Matplotlib and Seaborn

• Introduction to Data Visualization
• Creating Plots and Charts with Matplotlib
• Enhancing Visualizations with Seaborn

Module 4: Exploratory Data Analysis (EDA) with Python

• Descriptive Statistics in Python
• Data Distribution Analysis
• Correlation and Heatmaps

Module 5: Introduction to Machine Learning with Scikit-Learn

• Basics of Machine Learning with Python
• Supervised Learning vs. Unsupervised Learning
• Model Training, Testing, and Validation

Module 6: Regression and Classification with Scikit-Learn

• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Support Vector Machines

Module 7: Unsupervised Learning with Python

• K-Means Clustering
• Hierarchical Clustering
• Principal Component Analysis (PCA)

Module 8: Introduction to Natural Language Processing (NLP) with NLTK

• Working with Text Data in Python
• Tokenization and Stop Words Removal
• Sentiment Analysis with NLTK

Module 9: Introduction to Deep Learning with TensorFlow and Keras

• Basics of Neural Networks
• Building and Training Neural Networks with Keras
• Convolutional Neural Networks (CNNs) for Image Classification

Module 10: Working with Real-world Datasets

• Data Collection and APIs
• Web Scraping with Beautiful Soup
• Handling JSON and CSV Data

Module 11: Data Analysis with Pandas and SQL

• Advanced Data Analysis with Pandas
• Introduction to SQL for Data Analysis

Module 12: Data Visualization and Storytelling with Python

• Advanced Data Visualization with Plotly
• Creating Interactive Visualizations
• Effective Data Storytelling

Module 13: Capstone Project

• Applying Data Science Techniques to a Real-world Problem
• End-to-end Data Analysis, Cleaning, Modeling, and Visualization
• Presenting Project Results