Introduction to Data Science and Python

• Overview of data science and its role in decision-making
• Introduction to Python: syntax, functions, and libraries
• Setting up the Python environment: Jupyter, Anaconda, and IDEs

Data Manipulation with Python

• Data wrangling using Python libraries: Pandas, Numpy
• Cleaning, filtering, and transforming data
• Handling missing data, duplicates, and outliers

Data Visualization with Python

• Visualizing data with Matplotlib and Seaborn
• Creating interactive visualizations with Plotly
• Best practices for data storytelling through visualization

Statistics and Probability for Data Science

• Introduction to key statistical concepts: mean, median, variance, correlation
• Probability theory and its application in data science
• Statistical inference: hypothesis testing, confidence intervals

Exploratory Data Analysis (EDA)

• Understanding data through exploration and visualization
• Finding patterns, correlations, and trends in data
• Identifying key insights and drawing conclusions from data sets

Introduction to Machine Learning with Python

• Supervised learning: regression, classification, and decision trees
• Unsupervised learning: clustering and dimensionality reduction
• Introduction to Scikit-learn: building and training models

Working with Big Data in Python

• Managing and processing large datasets using Python
• Introduction to distributed computing tools: PySpark
• Big Data tools and libraries for Python integration

Advanced Machine Learning Techniques

• Deep learning basics with TensorFlow and Keras
• Building neural networks and predictive models
• Model evaluation, tuning, and optimization

Capstone Project: Data Science with Python

• End-to-end data science project using Python
• Data collection, cleaning, analysis, and model building
• Presenting insights and deploying a machine learning model