Jupyter Notebook for Data Science

Collaborate, create interactive visualizations, and manipulate big data in the language of your choice.

Ratings 3.89 / 5.00

What You Will Learn!

Learn how to efficiently use Jupyter Notebook for data manipulation and visualisation
Perform interactive data analysis and visualisation using Jupyter Notebook on real data
Analyse time series data using Pandas
Create interactive widgets where non-technical users can also get involved in the data exploration using the notebooks you create
Scrape websites to build datasets and deal with common challenges like unstructured or missing data
Combine different datasets in a single graph to enable people to compare them visually and gain new insights
Analyse and visualise geographic datasets to create stunning information-rich maps

Description

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.

This course uses Jupyter 5.4.1, while not the latest version available, it provides relevant and informative content for data science enthusiasts.

About the Author

Dražen Lucanin is a developer, data analyst, and the founder of Punk Rock Dev, an indie web development studio. He's been building web applications and doing data analysis in Python, JavaScript, and other technologies professionally since 2009. In the past, Dražen worked as a research assistant and did a PhD in computer science at the Vienna University of Technology. There he studied the energy efficiency of geographically distributed datacenters and worked on optimizing VM scheduling based on real-time electricity prices and weather conditions. He also worked as an external associate at the Ruder Boškovic Institute, researching machine learning methods for forecasting financial crises. During Dražen's scientific work Python, Jupyter Notebook (back then still IPython Notebook), Matplotlib, and Pandas were his best friends over many nights of interactive manipulation of all sorts of time series and spatial data. Dražen also did a Master's degree in computer science at the University of Zagreb.