Data Analysis Using Python

Course Outline:

1. Introduction to Statistical Analysis and Python
2. Data Manipulation with Pandas
3. Descriptive Statistics and Data Visualization with Matplotlib and Seaborn
4. Probability Distributions and Random Variables
5. Hypothesis Testing and Statistical Inference
6. Correlation and Regression Analysis
7. Time Series Analysis
8. Multivariate Analysis and Dimensionality Reduction
9. Machine Learning for Statistical Analysis
10. Case Studies and Real-world Applications

Skills You Will Learn:

• Understanding of statistical concepts and methods
• Proficiency in data manipulation and analysis using Pandas
• Ability to visualize data effectively using Matplotlib and Seaborn
• Knowledge of probability theory and its applications
• Expertise in hypothesis testing and statistical inference
• Familiarity with advanced statistical techniques and machine learning algorithms

Requirements:

• Basic knowledge of Python programming
• Familiarity with data structures like lists, tuples, and dictionaries
• Access to a computer with Python and relevant libraries installed (NumPy, Pandas, Matplotlib, Seaborn)
• Eagerness to learn and apply statistical techniques to real-world datasets
• Strong analytical and problem-solving skills