Roxie Zhai - IBM Data Science (SQL & Python)

IBM Data Science (SQL & Python) on Coursera

Summary

I took the Coursera online courses about IBM Data Science.

There are two parts in this course:

The first one is using SQL doing DDL,DML,DCL.
The second one is using SQL in Jupyter Notebook.

Part 1. Basic SQL knowledge

Basic SQL
String Patterns, Ranges, Sorting, and Grouping
Functions, Sub-Queries, Multiple Tables

This is the first table I created.

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For the next question, I created several tables:

Then I loaded the data into the tables:

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Next are some functions I used in SQL:

First, I created a PETSALE table, then I loaded data into it.

Here are some questions about joining tables (using tables I created above):

Part 2. Working with a real-world data-set using SQL and Python in Jupyter Notebook

I used three data sets here.

1. Socioeconomic Indicators in Chicago

This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” for each Chicago community area, for the years 2008 – 2012.

2. Chicago Public Schools

This dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year.

3. Chicago Crime Data

This dataset 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.

First, I downloaded the datasets. Then I store the datasets in database table.

I named the new tables as follows:

CENSUS_DATA
CHICAGO_PUBLIC_SCHOOLS
CHICAGO_CRIME_DATA

Next, I loaded the SQL extension and established a connection with the database.

Then, I entered my db2 connection string.

Here are the samples of my database table:

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