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Work in the cloud with Google Cloud Platform  Google Cloud Platform allow you to use free credit to run your code in Google Compute Engine.  You can interact with external data sources by using Google's BigQuery, Google Cloud Storage, and Cloud Pub/Sub. Note, the free version is limited to 5 large instances per project.
Work on paper with Microsoft Azure Notebooks  Microsoft Azure allows you to interact with external data sources by using Azure Storage and Data Lake Store.  It is also a cloud IDE that let you use free credit to run your code and get feedback for free.
Software
Integrate any data source to run machine learning  Any data source can be added to your Notebook.
Visualize your data using Tableau  Tableau is a data visualization tool. It is one of the most used free-to-use tools for data visualization.
Have more insights into your data with OpenRefine  OpenRefine is a free online tool for data cleaning and entity extraction. It will help you cleanse the text in your data, and turn it into a clean and useful form for you to use it.
Know how to access data and make predictions with R  R is an open source language for data analysis. It is used by many data scientists in industry. R provides a higher level of abstraction for common tasks.
Build a report in R Markdown  R Markdown is a markup language for writing reports. It uses R and RStudio for the processing of code. It is the most popular format for data scientists to write reports for data analysis.
Modeling
Create an ARIMA model to forecast future trends  ARIMA is an abbreviation for Auto Regressive Integrated Moving Average. It is a combination of Auto Regression and Integrated Moving Average.
Recreate, analyze, and improve your historical data with Sigma.ai  Sigma.ai is a powerful AI platform to generate insights from your data.
Look for differences and variations in data using Cluster, K-means, and Decision Tree clustering  Clustering algorithms are used to look for different types of differences in your data. For example, clustering can be used to find changes in time to identify one time of year that is better than another time. Clustering is also used to find different variations in your data. For example, clustering can be used to find the different varieties of Starbucks coffee.
Spatial analysis
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