The purpose of this course is to introduce relational database concepts and help you learn and apply foundational knowledge of the SQL language. It is also intended to get you started with performing SQL access in a data science environment.
The emphasis in this course is on hands-on and practical learning . As such, you will work with real databases, real data science tools, and real-world datasets. You will create a database instance in the cloud. Through a series of hands-on labs you will practice building and running SQL queries. You will also learn how to access databases from Jupyter notebooks using SQL and Python.
This folder contains data files and a Jupyter Notebook script that demonstrates use of a variety of SQL commands to query the three datasets.
- Census_Data.csv contains six socioeconomic indicators of public health significance and a “hardship index,” by Chicago community area, for the years 2008 – 2012. For more detailed information, see the data description.
- Chicago_Crime_Data.csv reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. For more detailed information, see the data description.
- Chicago_Public_Schools.csv shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. For more detailed information, see the data description.
- Chicago_Datasets_PeerAssign-v5.ipynb contains the script with ipython-sql code, which is used to query the datasets after they were loaded into a Db2 database on IBM Cloud.
