Professor: Dr. Xuebin Wei // [email protected]

~~ Currently working on uploading the projects here!! ~~

Goal(s): Import the Boston housing dataset from the S3 bucket to visualize the data in Jupyter.

Dataset: https://lib.stat.cmu.edu/datasets/boston

Goal(s): Launch an EC2 instance, using a website from Amazon Machine Image (AMI).

Goal(s): RDS Data in Tableau. i.e: Create an Aurora instance on AWS, create a workbook based in this instance in Tableau.

Goal(s): Analyze Data in Data Warehouse

i.e: Connect the Redshift to Tableau Online and create a dashboard to visualie the total net income for all sellers each month, the sellers’ last names sorted by the net income, and the cities with total net income.

Connect the Redshift to QuickSight, and create a visualization that can compare the total price and the total sold price. Visualize: the total sold price per month with forecasting, the number of shows in each category, the narrative comparison between the total sold price and the total price the anomaly in the total sold price.

Goal(s): Analyze Data in Data Lakes

Data used:

• Elastic Load Balancing Log, more details: https://docs.aws.amazon.com/elasticloadbalancing/latest/application//load- balancer-access-logs.html#access-log-file-format
• NOAA Global Historical Climatology Network Daily (GHCN-D) data, more details: https://docs.opendata.aws/noaa-ghcn-pds/readme.html

Use Athena to query the ELB log data in S3

Explore the NOAA data via Athena. Create two visualizations showing the number of records for snow and precipitation, and the recorded snows and precipitations over the past years.