Data Analysis with Pandas

From raw data to structured insights. If NumPy gives you the power to work with arrays, Pandas gives you the tools to manage and analyze real-world datasets. Built on top of NumPy, Pandas is the most widely used Python library for data analysis, enabling you to clean, transform, and explore data with speed and clarity. This course begins with the essentials: creating and working with Series and DataFrames, the core data structures of Pandas. You’ll learn how to load data from different sources (CSV, Excel, databases), inspect and summarize datasets, and select specific rows and columns with indexing and filtering techniques. Next, we dive into data cleaning and transformation. You’ll practice handling missing values, duplicates, and inconsistent formats—common challenges in any data project. You’ll also learn how to apply groupby operations, aggregations, and merges to organize data for deeper analysis. The course also covers practical techniques for reshaping datasets, creating new calculated columns, and applying functions across your data. By the end, you’ll be comfortable building data pipelines that prepare information for visualization, statistics, or machine learning. By the end of this course, you will be able to: Create and manipulate Pandas Series and DataFrames. Load, inspect, and explore datasets from multiple sources. Clean and transform data to ensure accuracy and usability. Apply grouping, aggregation, and merging operations. Build workflows that prepare data for analysis and visualization. Every step is hands-on: you’ll work with realistic datasets, debug issues, and see how Pandas makes messy data manageable. Pandas is the backbone of data science in Python. Once you master it, you’ll have the confidence to tackle exploratory analysis, visualization, and advanced analytical workflows.