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.

Jupyter Hub for Data Wrangling using Python gives you an effortless coding environment in the cloud with pre-installed Data Wrangling python libraries, which reduces the burden of installation and maintenance of tasks. Data wrangling is important because it saves costs, improves data analysis, increases data usability and enhances business insights. Through our offer, users can work on various application domains of Data Wrangling such as Fraud Detection, Customer Behavior Analysis, and many more. Furthermore, users can combine data from multiple sources, identify and remove data gaps, and delete irrelevant data for better analysis. The heavy computations required for these applications are performed in the Azure cloud, which increases responsiveness and processing speed.

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

• Pre-installed Python libraries and packages for Data Wrangling.
• Ready to go notebooks which consist of example codes through which user can get guidance for working on Data Wrangling applications.
• Code consoles to run code interactively, with full support for rich output.
• Kernel-backed documents enable code in any text file (Markdown, Python, etc.) to be run interactively in Jupyter kernel.
• Work with multiple notebooks at the same time.

• Minimum Recommended memory: 4GB RAM
• Minimum Recommended vCPU: 2 vCPUs
• Operating System: Ubuntu 20.04

Following Authoring Tools are supported in this offer:

• JupyterHub
• Jupyter Lab
• Terminal

Our instance supports following Python Data Wrangling libraries:

• Numpy
• Matplotlib
• Pandas
• Seaborn
• Plotly
• Plotnine
• Bokeh
• Ggplot
• Datacleaner
• Dora
• Scipy
• Statsmodels
• xml-python
• Arrow
• Scrubadub
• Tabulate
• Modin
• Dabl

Our offer provides repositories from following sources:

• GitHub repository of book Data Wrangling with Python, by author Jacqueline Kazil, Katharine Jarmul.
• GitHub repository of book The Data Wrangling Workshop, by author Brian Lipp, Shubhadeep Roychoudhury, and Tirthajyoti Sarkar.
• GitHub repository of book Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, by Wes McKinney.
• GitHub repository of book Practical Data Wrangling, by Allan Visochek.

The default HTTP port JupyterHub listen to is 8000. You can access the web interface at http://yourip:8000

• Username: guest
• Password: guest@123