This module introduces essential data analysis techniques using Python and the pandas library. You will learn how to import and work with data efficiently, leveraging DataFrames and Series to manipulate, filter, and analyze datasets. The module covers fundamental concepts such as vectorization for performance optimization, distinguishing between attributes and methods, and performing descriptive statistics. Additionally, you will explore data visualization techniques and segmentation methods to extract meaningful insights from structured data.

This module focuses on data visualization using Python, covering essential tools and techniques for creating effective visuals. You will learn to generate visualizations directly from pandas DataFrames and Series, as well as use popular libraries like matplotlib and Seaborn to develop custom plots. The module explores various visualization types, from basic line graphs and bar charts to advanced distribution and categorical plots. Additionally, you will learn how to enhance readability through styling, annotations, and design choices to highlight trends, patterns, and anomalies in data.

This module introduces statistical inference and regression modeling using Python. You will learn to construct confidence intervals, perform hypothesis testing with t-tests, and simulate data using NumPy. The module covers both simple and multiple linear regression, guiding you through model development, interpretation of key metrics (such as R-squared, p-values, and coefficients), and prediction of new data points. Additionally, you will explore methods to encode categorical variables, evaluate model performance using error metrics, and refine regression models with the help of Large Language Models (LLMs).

This module explores working with time series data in Python, focusing on DateTime objects, indexing, and visualization. You will learn to manipulate time-based data, apply descriptive statistics, and segment time series by key date features. The module covers resampling and reshaping techniques, as well as using simple and multiple linear regression to model trends and seasonality. Additionally, you will evaluate forecasting models using appropriate error metrics to assess their performance.