If you are ready to challenge yourself, the following is a project that you can work out how to do on your own. There is no education like actually doing the work yourself, especially when you are not sure of the right answers:

The project steps are as follows:

1. Set up the AWS environment: Follow Chapter 4, Creating an AWS Cloud Analytics Environment to prepare a secure area for data storage and IoT analytics.
2. Build a data feed to NOAA hourly weather data: You could use Python code in an AWS Lambda function or a service such as Amazon Kinesis to process the feed.
3. Import the dataset into a Hadoop environment (store in HDFS): Practice querying data using Hive. Amazon EMR can be used for this or a Cloudera/Hortonworks distribution.
4. Combine with another data set: You choose; have fun.
5. Analyze with Tableau to understand the data: Connect to Hive and explore the combined data. Create a dashboard to communicate some metrics and alerts.
6. Use R to create a machine learning prediction model: Use random forest and gradient boosting machines to fit models. Judge which one is best using an ROC curve chart. Predict the weather; be your own weatherman!