Data Analysis with Jupyter Notebook: Explore and Visualize Data in Python

Raw data is often messy and difficult to interpret, but interactive tools make uncovering insights intuitive and accessible. This course introduces you to Jupyter Notebook, the industry-standard environment for interactive data exploration, cleaning, and visualization. You will transition from writing basic code to confidently exploring complex datasets. Through step-by-step written explanations and practical code walkthroughs, you will learn how to transform raw, unstructured information into polished, interactive data reports. What you'll learn: - Understand the fundamentals of Jupyter Notebook, including environment configuration, markdown notes, and essential shortcuts. - Clean and manipulate messy datasets using modern pandas techniques and data-wrangling best practices. - Create interactive and static data visualizations using matplotlib and plotly to tell compelling visual stories. - Apply basic web scraping techniques to collect and prepare real-world data for analysis. - Manage your data projects efficiently using modern Python virtual environments and clean notebook workflows. - Analyze temporal and spatial data to identify trends, patterns, and anomalies. This course begins with foundational concepts, guiding you through setting up your workspace before progressing to advanced data manipulation and visualization techniques. You will practice by working through realistic data scenarios, such as analyzing public safety and transit datasets. This course is designed for beginners, aspiring data analysts, and researchers who want to build a solid foundation in data science. No prior data analysis experience is required, though a basic understanding of Python is helpful. Start reading today to master interactive data analysis and bring your datasets to life.