Explanation
Jupyter Notebook for Data Science This video course will help you learn Jupyter Notebook and all 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 a standard tool for data scientists. In this course, we will start with basic data analysis tasks in Jupyter Notebook and work our way to learn some common scientific Python tools such as pandas, matplotlib, and plot. I will work with real data, such as crime and traffic accidents in New York City, to explore common issues such as data scraping and cleaning. We will create effective visualizations, showing time-stamped data and spatial data. By the end of the course, you’ll feel confident about approaching new data, cleaning it, exploring it, and analyzing it in Jupyter Notebook to generate useful information in the form of interactive reports and data-rich databases.
Dražen Lucanin is a developer, data analyst, and founder of Punk Rock Dev, an indie web development studio. He has been building web applications and doing data analysis in Python, JavaScript, and other technologies professionally since 2009. In the past, Drazen worked as a research assistant and did his PhD in computer science at the Vienna University of Technology. There he studied the energy efficiency of geographically distributed databases and worked on optimizing VM schedules based on real-time electricity prices and weather conditions.
What will you learn?
- Learn how to effectively use Jupyter Notebook for data manipulation and visualization.
- Perform interactive data analysis and visualization using Jupyter Notebook for real data
- Analyze time series data using Pandas
- Create interactive widgets where non-technical users can also participate in data exploration using the notebooks you create.
- Archive websites to build a database and address common challenges such as unstructured or missing data.
- Combine different datasets into one graph to enable people to visually compare and gain new insights.
- Analyze and visualize geospatial data tables to create information-rich maps
Who is this course for?
- This course is designed for developers with a basic understanding of Python and Jupyter Notebook.
Jupyter Notebook Guidelines for Data Science
- Publisher: Udemy
- Teacher: Package Printing
- Language : English
- Level: Medium
- Number of courses: 20
- Duration: 3 hours and 11 minutes
Jupyter Notebook Contents for Data Science
Requirements
- Basic understanding of Python and Jupyter Notebook is required. A basic understanding of math and statistics will come in handy.
Pictures
Sample Clip
Installation Guide
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Quality: 720p
Download Links
Password file: free download software
file size
825MB