About this course

The easier way to do data science.

About This Video

• Understand how to effectively utilize Jupyter Notebook for interactive data analysis in Python
• Get hands-on experience of using popular data science libraries, such as Pandas and matplotlib, to work with real datasets
• Special focus is placed on addressing typical challenges, such as web scraping, dealing with data that isn't perfectly structured, and missing data

In Detail

This video course will help you get familiar with Jupyter Notebook and all of its features to perform various data science tasks in Python. Jupyter Notebook is a powerful tool for interactive data exploration and visualization and has become the standard tool among data scientists. In the course, we will start from basic data analysis tasks in Jupyter Notebook and work our way up to learn some common scientific Python tools such as pandas, matplotlib, and plotly. We will work with real datasets, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create insightful visualizations, showing time-stamped and spatial data.

By the end of the course, you will feel confident about approaching a new dataset, cleaning it up, exploring it, and analyzing it in Jupyter Notebook to extract useful information in the form of interactive reports and information-dense data visualizations.

Style and Approach

In this course you won't just work with sterile Hello world examples; instead, we'll analyze real datasets available online. This way, you will learn how to deal with typical problems that pop up in daily data science work.