Training on Python for Data Science

Module 1: Introduction to Python for Data Science

• Overview of Data Science and Python
• Setting up the Python environment (Anaconda, Jupyter Notebooks)
• Basic Python syntax and data types

Module 2: Data Structures and Control Flow

• Lists, tuples, dictionaries, and sets
• Control flow (if statements, loops)

Module 3: Data Manipulation with Pandas

• Introduction to pandas
• DataFrames and Series
• Data cleaning and preparation techniques

Module 4: Data Visualization with Matplotlib and Seaborn

• Introduction to data visualization
• Creating visualizations using Matplotlib
• Advanced visualizations with Seaborn

Module 5: Numpy for Numerical Computing

• Introduction to NumPy
• Array manipulation and operations
• Performance improvements with NumPy

Module 6: Exploratory Data Analysis (EDA)

• Techniques for EDA
• Descriptive statistics and data summarization
• Identifying trends and correlations

Module 7: Introduction to Machine Learning

• Overview of machine learning concepts
• Supervised vs. unsupervised learning
• Implementing simple models using scikit-learn

Module 8: Supervised Learning Algorithms

• Linear regression and logistic regression
• Decision trees and support vector machines
• Model evaluation techniques

Module 9: Unsupervised Learning Algorithms

• K-means clustering and hierarchical clustering
• Principal Component Analysis (PCA)
• Anomaly detection

Module 10: Capstone Project and Course Wrap-Up

• Real-world project where participants apply learned skills
• Presenting findings and insights
• Course review and next steps for further learning