Jupyter Hub for Time Series Analysis using Python packaged by Data Science Dojo

Data Science Dojo

Jupyter Hub for Time Series Analysis using Python packaged by Data Science Dojo

Data Science Dojo

Our Jupyter Instance provides an easy-to-use environment for Time Series Analysis applications.

Data Science Dojo delivers data science education, consulting, and technical services to harvest the power of data.

Trademarks: This software listing is packaged by Data Science Dojo. The respective trademarks mentioned in the offering are owned by the respective companies, and use of them does not imply any affiliation or endorsement.

About the offer:

Jupyter Hub for Time Series Analysis using Python gives you an effortless coding environment in the cloud with pre-installed Time Series Analysis python libraries, reducing the burden of installation and maintenance of tasks. Through this offer, a user can work on different applications of time series analysis including sales forecasting, weather forecasting, inventory studies, census analysis, stock market analysis, budgetary analysis and many more. The heavy computations required for these applications are not performed on the user’s local machine. Instead, they are performed in the Azure cloud, which increases responsiveness and processing speed.

Who benefits from this offer:

Following can benefit from our instance:

  • Teams of developers
  • Machine learning engineers
  • Scientific researcher groups
  • And anyone else interested in data science tools

What is included in this offer:
  • Pre-installed Time Series Analysis libraries for python
  • Ready to go notebooks which consist of example codes through which user can get guidance for working on Time Series Analysis
  • Work with multiple notebooks at the same time
  • Kernel-backed documents enable code in any text file (Markdown, Python, etc.) to be run interactively in Jupyter kernel
  • Code consoles to run code interactively, with full support for rich output
Technical Specifications:
  • Recommended memory: 8GB RAM
  • Recommended vCPU:4 vCPUs
  • Operating System:Ubuntu 20.04

Our offer provides repositories from following sources:

  • Github repository of book Practical Time Series Analysis Book , by authors Dr. Avishek Pal , Dr. PKS Prakash
  • Github repository of library Hands on Time Series Analysis with Python, by author B V Vishwas and Ashish Patel
  • Github repository of library Machine Learning for Time Series with Python,by author Ben Auffarth
  • Github repository of library Time Series Analysis with Python Cookbook,by author Tarek A. Atwan

Following Authoring Tools are supported in this offer:

  • JupyterHub
  • Jupyter Lab
  • Terminal

Our instance supports following python libraries:

  • pandas
  • tsfresh
  • darts
  • audioread
  • greykite
  • autots
  • orbit
  • pastas
  • arrow
  • flint
  • prophet
  • pyflux
  • sktime
  • pmdarima
  • cesium
  • featuretools
  • kats
  • sklearn

The default port JupyterHub listens to is 8000. You can access the web interface at http://yourip:8000 using the credentials

  • username:guest
  • password:guest@123
The Jupyter Trademark is registered with the U.S. Patent & Trademark Office.